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Type 'q()' to quit R. > pkgname <- "MASS" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('MASS') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("Insurance") > ### * Insurance > > flush(stderr()); flush(stdout()) > > ### Name: Insurance > ### Title: Numbers of Car Insurance claims > ### Aliases: Insurance > ### Keywords: datasets > > ### ** Examples > > ## main-effects fit as Poisson GLM with offset > glm(Claims ~ District + Group + Age + offset(log(Holders)), + data = Insurance, family = poisson) Call: glm(formula = Claims ~ District + Group + Age + offset(log(Holders)), family = poisson, data = Insurance) Coefficients: (Intercept) District2 District3 District4 Group.L Group.Q -1.810508 0.025868 0.038524 0.234205 0.429708 0.004632 Group.C Age.L Age.Q Age.C -0.029294 -0.394432 -0.000355 -0.016737 Degrees of Freedom: 63 Total (i.e. Null); 54 Residual Null Deviance: 236.3 Residual Deviance: 51.42 AIC: 388.7 > > # same via loglm > loglm(Claims ~ District + Group + Age + offset(log(Holders)), + data = Insurance) Call: loglm(formula = Claims ~ District + Group + Age + offset(log(Holders)), data = Insurance) Statistics: X^2 df P(> X^2) Likelihood Ratio 51.42003 54 0.5745071 Pearson 48.62933 54 0.6809086 > > > > cleanEx() > nameEx("Null") > ### * Null > > flush(stderr()); flush(stdout()) > > ### Name: Null > ### Title: Null Spaces of Matrices > ### Aliases: Null > ### Keywords: algebra > > ### ** Examples > > # The function is currently defined as > function(M) + { + tmp <- qr(M) + set <- if(tmp$rank == 0L) seq_len(ncol(M)) else -seq_len(tmp$rank) + qr.Q(tmp, complete = TRUE)[, set, drop = FALSE] + } function (M) { tmp <- qr(M) set <- if (tmp$rank == 0L) seq_len(ncol(M)) else -seq_len(tmp$rank) qr.Q(tmp, complete = TRUE)[, set, drop = FALSE] } > > > > cleanEx() > nameEx("OME") > ### * OME > > flush(stderr()); flush(stdout()) > > ### Name: OME > ### Title: Tests of Auditory Perception in Children with OME > ### Aliases: OME > ### Keywords: datasets > > ### ** Examples > > # Fit logistic curve from p = 0.5 to p = 1.0 > fp1 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/scal)), + c("L75", "scal"), + function(x,L75,scal)NULL) > nls(Correct/Trials ~ fp1(Loud, L75, scal), data = OME, + start = c(L75=45, scal=3)) Nonlinear regression model model: Correct/Trials ~ fp1(Loud, L75, scal) data: OME L75 scal 44.149 3.775 residual sum-of-squares: 69.88 Number of iterations to convergence: 4 Achieved convergence tolerance: 7.016e-06 > nls(Correct/Trials ~ fp1(Loud, L75, scal), + data = OME[OME$Noise == "coherent",], + start=c(L75=45, scal=3)) Nonlinear regression model model: Correct/Trials ~ fp1(Loud, L75, scal) data: OME[OME$Noise == "coherent", ] L75 scal 47.993 1.259 residual sum-of-squares: 30.35 Number of iterations to convergence: 5 Achieved convergence tolerance: 4.895e-06 > nls(Correct/Trials ~ fp1(Loud, L75, scal), + data = OME[OME$Noise == "incoherent",], + start = c(L75=45, scal=3)) Nonlinear regression model model: Correct/Trials ~ fp1(Loud, L75, scal) data: OME[OME$Noise == "incoherent", ] L75 scal 38.87 2.17 residual sum-of-squares: 23.73 Number of iterations to convergence: 11 Achieved convergence tolerance: 3.846e-06 > > # individual fits for each experiment > > aa <- factor(OME$Age) > ab <- 10*OME$ID + unclass(aa) > ac <- unclass(factor(ab)) > OME$UID <- as.vector(ac) > OME$UIDn <- OME$UID + 0.1*(OME$Noise == "incoherent") > rm(aa, ab, ac) > OMEi <- OME > > library(nlme) > fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/2)), + "L75", function(x,L75) NULL) > dec <- getOption("OutDec") > options(show.error.messages = FALSE, OutDec=".") > OMEi.nls <- nlsList(Correct/Trials ~ fp2(Loud, L75) | UIDn, + data = OMEi, start = list(L75=45), control = list(maxiter=100)) > options(show.error.messages = TRUE, OutDec=dec) > tmp <- sapply(OMEi.nls, function(X) + {if(is.null(X)) NA else as.vector(coef(X))}) > OMEif <- data.frame(UID = round(as.numeric((names(tmp)))), + Noise = rep(c("coherent", "incoherent"), 110), + L75 = as.vector(tmp), stringsAsFactors = TRUE) > OMEif$Age <- OME$Age[match(OMEif$UID, OME$UID)] > OMEif$OME <- OME$OME[match(OMEif$UID, OME$UID)] > OMEif <- OMEif[OMEif$L75 > 30,] > summary(lm(L75 ~ Noise/Age, data = OMEif, na.action = na.omit)) Call: lm(formula = L75 ~ Noise/Age, data = OMEif, na.action = na.omit) Residuals: Min 1Q Median 3Q Max -13.0022 -1.9878 0.3346 2.0229 16.3260 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 47.73580 0.76456 62.435 < 2e-16 *** Noiseincoherent -4.87352 1.11247 -4.381 1.92e-05 *** Noisecoherent:Age -0.02785 0.02349 -1.186 0.237 Noiseincoherent:Age -0.12219 0.02589 -4.719 4.50e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.774 on 196 degrees of freedom (17 observations deleted due to missingness) Multiple R-squared: 0.5246, Adjusted R-squared: 0.5173 F-statistic: 72.09 on 3 and 196 DF, p-value: < 2.2e-16 > summary(lm(L75 ~ Noise/(Age + OME), data = OMEif, + subset = (Age >= 30 & Age <= 60), + na.action = na.omit), correlation = FALSE) Call: lm(formula = L75 ~ Noise/(Age + OME), data = OMEif, subset = (Age >= 30 & Age <= 60), na.action = na.omit) Residuals: Min 1Q Median 3Q Max -10.4514 -2.0588 0.0194 1.6827 15.9738 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 50.21090 1.74482 28.777 < 2e-16 *** Noiseincoherent -5.97491 2.70148 -2.212 0.02890 * Noisecoherent:Age -0.09358 0.03586 -2.609 0.01023 * Noiseincoherent:Age -0.15155 0.04151 -3.651 0.00039 *** Noisecoherent:OMElow 0.45103 1.07594 0.419 0.67583 Noiseincoherent:OMElow -0.14075 1.24537 -0.113 0.91021 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.7 on 119 degrees of freedom (17 observations deleted due to missingness) Multiple R-squared: 0.6073, Adjusted R-squared: 0.5908 F-statistic: 36.81 on 5 and 119 DF, p-value: < 2.2e-16 > > # Or fit by weighted least squares > fpl75 <- deriv(~ sqrt(n)*(r/n - 0.5 - 0.5/(1 + exp(-(x-L75)/scal))), + c("L75", "scal"), + function(r,n,x,L75,scal) NULL) > nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal), + data = OME[OME$Noise == "coherent",], + start = c(L75=45, scal=3)) Nonlinear regression model model: 0 ~ fpl75(Correct, Trials, Loud, L75, scal) data: OME[OME$Noise == "coherent", ] L75 scal 47.798 1.296 residual sum-of-squares: 91.72 Number of iterations to convergence: 5 Achieved convergence tolerance: 9.302e-06 > nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal), + data = OME[OME$Noise == "incoherent",], + start = c(L75=45, scal=3)) Nonlinear regression model model: 0 ~ fpl75(Correct, Trials, Loud, L75, scal) data: OME[OME$Noise == "incoherent", ] L75 scal 38.553 2.078 residual sum-of-squares: 60.19 Number of iterations to convergence: 8 Achieved convergence tolerance: 4.55e-06 > > # Test to see if the curves shift with age > fpl75age <- deriv(~sqrt(n)*(r/n - 0.5 - 0.5/(1 + + exp(-(x-L75-slope*age)/scal))), + c("L75", "slope", "scal"), + function(r,n,x,age,L75,slope,scal) NULL) > OME.nls1 <- + nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal), + data = OME[OME$Noise == "coherent",], + start = c(L75=45, slope=0, scal=2)) > sqrt(diag(vcov(OME.nls1))) L75 slope scal 0.61091761 0.01665916 0.17566450 > > OME.nls2 <- + nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal), + data = OME[OME$Noise == "incoherent",], + start = c(L75=45, slope=0, scal=2)) > sqrt(diag(vcov(OME.nls2))) L75 slope scal 0.49553854 0.01348281 0.24453836 > > # Now allow random effects by using NLME > OMEf <- OME[rep(1:nrow(OME), OME$Trials),] > OMEf$Resp <- with(OME, rep(rep(c(1,0), length(Trials)), + t(cbind(Correct, Trials-Correct)))) > OMEf <- OMEf[, -match(c("Correct", "Trials"), names(OMEf))] > > ## Not run: > ##D ## these fail in R on most platforms > ##D fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/exp(lsc))), > ##D c("L75", "lsc"), > ##D function(x, L75, lsc) NULL) > ##D try(summary(nlme(Resp ~ fp2(Loud, L75, lsc), > ##D fixed = list(L75 ~ Age, lsc ~ 1), > ##D random = L75 + lsc ~ 1 | UID, > ##D data = OMEf[OMEf$Noise == "coherent",], method = "ML", > ##D start = list(fixed=c(L75=c(48.7, -0.03), lsc=0.24)), verbose = TRUE))) > ##D > ##D try(summary(nlme(Resp ~ fp2(Loud, L75, lsc), > ##D fixed = list(L75 ~ Age, lsc ~ 1), > ##D random = L75 + lsc ~ 1 | UID, > ##D data = OMEf[OMEf$Noise == "incoherent",], method = "ML", > ##D start = list(fixed=c(L75=c(41.5, -0.1), lsc=0)), verbose = TRUE))) > ## End(Not run) > > > cleanEx() detaching ‘package:nlme’ > nameEx("Skye") > ### * Skye > > flush(stderr()); flush(stdout()) > > ### Name: Skye > ### Title: AFM Compositions of Aphyric Skye Lavas > ### Aliases: Skye > ### Keywords: datasets > > ### ** Examples > > # ternary() is from the on-line answers. > ternary <- function(X, pch = par("pch"), lcex = 1, + add = FALSE, ord = 1:3, ...) + { + X <- as.matrix(X) + if(any(X < 0)) stop("X must be non-negative") + s <- drop(X %*% rep(1, ncol(X))) + if(any(s<=0)) stop("each row of X must have a positive sum") + if(max(abs(s-1)) > 1e-6) { + warning("row(s) of X will be rescaled") + X <- X / s + } + X <- X[, ord] + s3 <- sqrt(1/3) + if(!add) + { + oldpty <- par("pty") + on.exit(par(pty=oldpty)) + par(pty="s") + plot(c(-s3, s3), c(0.5-s3, 0.5+s3), type="n", axes=FALSE, + xlab="", ylab="") + polygon(c(0, -s3, s3), c(1, 0, 0), density=0) + lab <- NULL + if(!is.null(dn <- dimnames(X))) lab <- dn[[2]] + if(length(lab) < 3) lab <- as.character(1:3) + eps <- 0.05 * lcex + text(c(0, s3+eps*0.7, -s3-eps*0.7), + c(1+eps, -0.1*eps, -0.1*eps), lab, cex=lcex) + } + points((X[,2] - X[,3])*s3, X[,1], ...) + } > > ternary(Skye/100, ord=c(1,3,2)) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("addterm") > ### * addterm > > flush(stderr()); flush(stdout()) > > ### Name: addterm > ### Title: Try All One-Term Additions to a Model > ### Aliases: addterm addterm.default addterm.glm addterm.lm > ### Keywords: models > > ### ** Examples > > quine.hi <- aov(log(Days + 2.5) ~ .^4, quine) > quine.lo <- aov(log(Days+2.5) ~ 1, quine) > addterm(quine.lo, quine.hi, test="F") Single term additions Model: log(Days + 2.5) ~ 1 Df Sum of Sq RSS AIC F Value Pr(F) 106.787 -43.664 Eth 1 10.6820 96.105 -57.052 16.0055 0.0001006 *** Sex 1 0.5969 106.190 -42.483 0.8094 0.3698057 Age 3 4.7469 102.040 -44.303 2.2019 0.0904804 . Lrn 1 0.0043 106.783 -41.670 0.0058 0.9392083 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family=poisson, + data=housing) > addterm(house.glm0, ~. + Sat:(Infl+Type+Cont), test="Chisq") Single term additions Model: Freq ~ Infl * Type * Cont + Sat Df Deviance AIC LRT Pr(Chi) 217.46 610.43 Infl:Sat 4 111.08 512.05 106.371 < 2.2e-16 *** Type:Sat 6 156.79 561.76 60.669 3.292e-11 *** Cont:Sat 2 212.33 609.30 5.126 0.07708 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont)) > addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test = "Chisq") Single term additions Model: Freq ~ Infl + Type + Cont + Sat + Infl:Type + Infl:Cont + Type:Cont + Infl:Sat + Type:Sat + Cont:Sat + Infl:Type:Cont Df Deviance AIC LRT Pr(Chi) 38.662 455.63 Infl:Type:Sat 12 16.107 457.08 22.5550 0.03175 * Infl:Cont:Sat 4 37.472 462.44 1.1901 0.87973 Type:Cont:Sat 6 28.256 457.23 10.4064 0.10855 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > cleanEx() > nameEx("anova.negbin") > ### * anova.negbin > > flush(stderr()); flush(stdout()) > > ### Name: anova.negbin > ### Title: Likelihood Ratio Tests for Negative Binomial GLMs > ### Aliases: anova.negbin > ### Keywords: regression > > ### ** Examples > > m1 <- glm.nb(Days ~ Eth*Age*Lrn*Sex, quine, link = log) > m2 <- update(m1, . ~ . - Eth:Age:Lrn:Sex) > anova(m2, m1) Likelihood ratio tests of Negative Binomial Models Response: Days Model 1 Eth + Age + Lrn + Sex + Eth:Age + Eth:Lrn + Age:Lrn + Eth:Sex + Age:Sex + Lrn:Sex + Eth:Age:Lrn + Eth:Age:Sex + Eth:Lrn:Sex + Age:Lrn:Sex 2 Eth * Age * Lrn * Sex theta Resid. df 2 x log-lik. Test df LR stat. Pr(Chi) 1 1.90799 120 -1040.728 2 1.92836 118 -1039.324 1 vs 2 2 1.403843 0.4956319 > anova(m2) Warning in anova.negbin(m2) : tests made without re-estimating 'theta' Analysis of Deviance Table Model: Negative Binomial(1.908), link: log Response: Days Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev Pr(>Chi) NULL 145 270.03 Eth 1 19.0989 144 250.93 1.241e-05 *** Age 3 16.3483 141 234.58 0.000962 *** Lrn 1 3.5449 140 231.04 0.059730 . Sex 1 0.3989 139 230.64 0.527666 Eth:Age 3 14.6030 136 216.03 0.002189 ** Eth:Lrn 1 0.0447 135 215.99 0.832601 Age:Lrn 2 1.7482 133 214.24 0.417240 Eth:Sex 1 1.1470 132 213.09 0.284183 Age:Sex 3 21.9746 129 191.12 6.603e-05 *** Lrn:Sex 1 0.0277 128 191.09 0.867712 Eth:Age:Lrn 2 9.0099 126 182.08 0.011054 * Eth:Age:Sex 3 4.8218 123 177.26 0.185319 Eth:Lrn:Sex 1 3.3160 122 173.94 0.068608 . Age:Lrn:Sex 2 6.3941 120 167.55 0.040882 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > cleanEx() > nameEx("area") > ### * area > > flush(stderr()); flush(stdout()) > > ### Name: area > ### Title: Adaptive Numerical Integration > ### Aliases: area > ### Keywords: nonlinear > > ### ** Examples > > area(sin, 0, pi) # integrate the sin function from 0 to pi. [1] 2 > > > > cleanEx() > nameEx("bacteria") > ### * bacteria > > flush(stderr()); flush(stdout()) > > ### Name: bacteria > ### Title: Presence of Bacteria after Drug Treatments > ### Aliases: bacteria > ### Keywords: datasets > > ### ** Examples > > contrasts(bacteria$trt) <- structure(contr.sdif(3), + dimnames = list(NULL, c("drug", "encourage"))) > ## fixed effects analyses > ## IGNORE_RDIFF_BEGIN > summary(glm(y ~ trt * week, binomial, data = bacteria)) Call: glm(formula = y ~ trt * week, family = binomial, data = bacteria) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.97548 0.30053 6.573 4.92e-11 *** trtdrug -0.99848 0.69490 -1.437 0.15075 trtencourage 0.83865 0.73482 1.141 0.25374 week -0.11814 0.04460 -2.649 0.00807 ** trtdrug:week -0.01722 0.10570 -0.163 0.87061 trtencourage:week -0.07043 0.10964 -0.642 0.52060 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 217.38 on 219 degrees of freedom Residual deviance: 203.12 on 214 degrees of freedom AIC: 215.12 Number of Fisher Scoring iterations: 4 > summary(glm(y ~ trt + week, binomial, data = bacteria)) Call: glm(formula = y ~ trt + week, family = binomial, data = bacteria) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.96018 0.29705 6.599 4.15e-11 *** trtdrug -1.10667 0.42519 -2.603 0.00925 ** trtencourage 0.45502 0.42766 1.064 0.28735 week -0.11577 0.04414 -2.623 0.00872 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 217.38 on 219 degrees of freedom Residual deviance: 203.81 on 216 degrees of freedom AIC: 211.81 Number of Fisher Scoring iterations: 4 > summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria)) Call: glm(formula = y ~ trt + I(week > 2), family = binomial, data = bacteria) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.2479 0.3560 6.315 2.71e-10 *** trtdrug -1.1187 0.4288 -2.609 0.00909 ** trtencourage 0.4815 0.4330 1.112 0.26614 I(week > 2)TRUE -1.2949 0.4104 -3.155 0.00160 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 217.38 on 219 degrees of freedom Residual deviance: 199.18 on 216 degrees of freedom AIC: 207.18 Number of Fisher Scoring iterations: 5 > ## IGNORE_RDIFF_END > > # conditional random-effects analysis > library(survival) > bacteria$Time <- rep(1, nrow(bacteria)) > coxph(Surv(Time, unclass(y)) ~ week + strata(ID), + data = bacteria, method = "exact") Call: coxph(formula = Surv(Time, unclass(y)) ~ week + strata(ID), data = bacteria, method = "exact") coef exp(coef) se(coef) z p week -0.16256 0.84996 0.05472 -2.971 0.00297 Likelihood ratio test=9.85 on 1 df, p=0.001696 n= 220, number of events= 177 > coxph(Surv(Time, unclass(y)) ~ factor(week) + strata(ID), + data = bacteria, method = "exact") Call: coxph(formula = Surv(Time, unclass(y)) ~ factor(week) + strata(ID), data = bacteria, method = "exact") coef exp(coef) se(coef) z p factor(week)2 0.1983 1.2193 0.7241 0.274 0.7842 factor(week)4 -1.4206 0.2416 0.6665 -2.131 0.0331 factor(week)6 -1.6615 0.1899 0.6825 -2.434 0.0149 factor(week)11 -1.6752 0.1873 0.6780 -2.471 0.0135 Likelihood ratio test=15.45 on 4 df, p=0.003854 n= 220, number of events= 177 > coxph(Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID), + data = bacteria, method = "exact") Call: coxph(formula = Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID), data = bacteria, method = "exact") coef exp(coef) se(coef) z p I(week > 2)TRUE -1.6701 0.1882 0.4817 -3.467 0.000527 Likelihood ratio test=15.15 on 1 df, p=9.927e-05 n= 220, number of events= 177 > > # PQL glmm analysis > library(nlme) > ## IGNORE_RDIFF_BEGIN > summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID, + family = binomial, data = bacteria)) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 Linear mixed-effects model fit by maximum likelihood Data: bacteria AIC BIC logLik NA NA NA Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.410637 0.7800511 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ trt + I(week > 2) Value Std.Error DF t-value p-value (Intercept) 2.7447864 0.3784193 169 7.253294 0.0000 trtdrug -1.2473553 0.6440635 47 -1.936696 0.0588 trtencourage 0.4930279 0.6699339 47 0.735935 0.4654 I(week > 2)TRUE -1.6072570 0.3583379 169 -4.485311 0.0000 Correlation: (Intr) trtdrg trtncr trtdrug 0.009 trtencourage 0.036 -0.518 I(week > 2)TRUE -0.710 0.047 -0.046 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -5.1985361 0.1572336 0.3513075 0.4949482 1.7448845 Number of Observations: 220 Number of Groups: 50 > ## IGNORE_RDIFF_END > > > > cleanEx() detaching ‘package:nlme’, ‘package:survival’ > nameEx("bandwidth.nrd") > ### * bandwidth.nrd > > flush(stderr()); flush(stdout()) > > ### Name: bandwidth.nrd > ### Title: Bandwidth for density() via Normal Reference Distribution > ### Aliases: bandwidth.nrd > ### Keywords: dplot > > ### ** Examples > > # The function is currently defined as > function(x) + { + r <- quantile(x, c(0.25, 0.75)) + h <- (r[2] - r[1])/1.34 + 4 * 1.06 * min(sqrt(var(x)), h) * length(x)^(-1/5) + } function (x) { r <- quantile(x, c(0.25, 0.75)) h <- (r[2] - r[1])/1.34 4 * 1.06 * min(sqrt(var(x)), h) * length(x)^(-1/5) } > > > > cleanEx() > nameEx("bcv") > ### * bcv > > flush(stderr()); flush(stdout()) > > ### Name: bcv > ### Title: Biased Cross-Validation for Bandwidth Selection > ### Aliases: bcv > ### Keywords: dplot > > ### ** Examples > > bcv(geyser$duration) [1] 0.8940809 > > > > cleanEx() > nameEx("beav1") > ### * beav1 > > flush(stderr()); flush(stdout()) > > ### Name: beav1 > ### Title: Body Temperature Series of Beaver 1 > ### Aliases: beav1 > ### Keywords: datasets > > ### ** Examples > > beav1 <- within(beav1, + hours <- 24*(day-346) + trunc(time/100) + (time%%100)/60) > plot(beav1$hours, beav1$temp, type="l", xlab="time", + ylab="temperature", main="Beaver 1") > usr <- par("usr"); usr[3:4] <- c(-0.2, 8); par(usr=usr) > lines(beav1$hours, beav1$activ, type="s", lty=2) > temp <- ts(c(beav1$temp[1:82], NA, beav1$temp[83:114]), + start = 9.5, frequency = 6) > activ <- ts(c(beav1$activ[1:82], NA, beav1$activ[83:114]), + start = 9.5, frequency = 6) > > acf(temp[1:53]) > acf(temp[1:53], type = "partial") > ar(temp[1:53]) Call: ar(x = temp[1:53]) Coefficients: 1 0.8222 Order selected 1 sigma^2 estimated as 0.01011 > act <- c(rep(0, 10), activ) > X <- cbind(1, act = act[11:125], act1 = act[10:124], + act2 = act[9:123], act3 = act[8:122]) > alpha <- 0.80 > stemp <- as.vector(temp - alpha*lag(temp, -1)) > sX <- X[-1, ] - alpha * X[-115,] > beav1.ls <- lm(stemp ~ -1 + sX, na.action = na.omit) > summary(beav1.ls, correlation = FALSE) Call: lm(formula = stemp ~ -1 + sX, na.action = na.omit) Residuals: Min 1Q Median 3Q Max -0.21317 -0.04317 0.00683 0.05483 0.37683 Coefficients: Estimate Std. Error t value Pr(>|t|) sX 36.85587 0.03922 939.833 < 2e-16 *** sXact 0.25400 0.03930 6.464 3.37e-09 *** sXact1 0.17096 0.05100 3.352 0.00112 ** sXact2 0.16202 0.05147 3.148 0.00215 ** sXact3 0.10548 0.04310 2.448 0.01605 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.08096 on 104 degrees of freedom (5 observations deleted due to missingness) Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999 F-statistic: 1.81e+05 on 5 and 104 DF, p-value: < 2.2e-16 > rm(temp, activ) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("beav2") > ### * beav2 > > flush(stderr()); flush(stdout()) > > ### Name: beav2 > ### Title: Body Temperature Series of Beaver 2 > ### Aliases: beav2 > ### Keywords: datasets > > ### ** Examples > > attach(beav2) > beav2$hours <- 24*(day-307) + trunc(time/100) + (time%%100)/60 > plot(beav2$hours, beav2$temp, type = "l", xlab = "time", + ylab = "temperature", main = "Beaver 2") > usr <- par("usr"); usr[3:4] <- c(-0.2, 8); par(usr = usr) > lines(beav2$hours, beav2$activ, type = "s", lty = 2) > > temp <- ts(temp, start = 8+2/3, frequency = 6) > activ <- ts(activ, start = 8+2/3, frequency = 6) > acf(temp[activ == 0]); acf(temp[activ == 1]) # also look at PACFs > ar(temp[activ == 0]); ar(temp[activ == 1]) Call: ar(x = temp[activ == 0]) Coefficients: 1 0.7392 Order selected 1 sigma^2 estimated as 0.02011 Call: ar(x = temp[activ == 1]) Coefficients: 1 0.7894 Order selected 1 sigma^2 estimated as 0.01792 > > arima(temp, order = c(1,0,0), xreg = activ) Call: arima(x = temp, order = c(1, 0, 0), xreg = activ) Coefficients: ar1 intercept activ 0.8733 37.1920 0.6139 s.e. 0.0684 0.1187 0.1381 sigma^2 estimated as 0.01518: log likelihood = 66.78, aic = -125.55 > dreg <- cbind(sin = sin(2*pi*beav2$hours/24), cos = cos(2*pi*beav2$hours/24)) > arima(temp, order = c(1,0,0), xreg = cbind(active=activ, dreg)) Call: arima(x = temp, order = c(1, 0, 0), xreg = cbind(active = activ, dreg)) Coefficients: ar1 intercept active dreg.sin dreg.cos 0.7905 37.1674 0.5322 -0.282 0.1201 s.e. 0.0681 0.0939 0.1282 0.105 0.0997 sigma^2 estimated as 0.01434: log likelihood = 69.83, aic = -127.67 > > ## IGNORE_RDIFF_BEGIN > library(nlme) # for gls and corAR1 > beav2.gls <- gls(temp ~ activ, data = beav2, correlation = corAR1(0.8), + method = "ML") > summary(beav2.gls) Generalized least squares fit by maximum likelihood Model: temp ~ activ Data: beav2 AIC BIC logLik -125.5505 -115.1298 66.77523 Correlation Structure: AR(1) Formula: ~1 Parameter estimate(s): Phi 0.8731771 Coefficients: Value Std.Error t-value p-value (Intercept) 37.19195 0.1131328 328.7460 0 activ 0.61418 0.1087286 5.6487 0 Correlation: (Intr) activ -0.582 Standardized residuals: Min Q1 Med Q3 Max -2.42080776 -0.61510519 -0.03573836 0.81641138 2.15153495 Residual standard error: 0.2527856 Degrees of freedom: 100 total; 98 residual > summary(update(beav2.gls, subset = 6:100)) Generalized least squares fit by maximum likelihood Model: temp ~ activ Data: beav2 Subset: 6:100 AIC BIC logLik -124.981 -114.7654 66.49048 Correlation Structure: AR(1) Formula: ~1 Parameter estimate(s): Phi 0.8380448 Coefficients: Value Std.Error t-value p-value (Intercept) 37.25001 0.09634047 386.6496 0 activ 0.60277 0.09931904 6.0690 0 Correlation: (Intr) activ -0.657 Standardized residuals: Min Q1 Med Q3 Max -2.0231494 -0.8910348 -0.1497564 0.7640939 2.2719468 Residual standard error: 0.2188542 Degrees of freedom: 95 total; 93 residual > detach("beav2"); rm(temp, activ) > ## IGNORE_RDIFF_END > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() detaching ‘package:nlme’ > nameEx("birthwt") > ### * birthwt > > flush(stderr()); flush(stdout()) > > ### Name: birthwt > ### Title: Risk Factors Associated with Low Infant Birth Weight > ### Aliases: birthwt > ### Keywords: datasets > > ### ** Examples > > bwt <- with(birthwt, { + race <- factor(race, labels = c("white", "black", "other")) + ptd <- factor(ptl > 0) + ftv <- factor(ftv) + levels(ftv)[-(1:2)] <- "2+" + data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0), + ptd, ht = (ht > 0), ui = (ui > 0), ftv) + }) > options(contrasts = c("contr.treatment", "contr.poly")) > glm(low ~ ., binomial, bwt) Call: glm(formula = low ~ ., family = binomial, data = bwt) Coefficients: (Intercept) age lwt raceblack raceother smokeTRUE 0.82302 -0.03723 -0.01565 1.19241 0.74068 0.75553 ptdTRUE htTRUE uiTRUE ftv1 ftv2+ 1.34376 1.91317 0.68020 -0.43638 0.17901 Degrees of Freedom: 188 Total (i.e. Null); 178 Residual Null Deviance: 234.7 Residual Deviance: 195.5 AIC: 217.5 > > > > base::options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("boxcox") > ### * boxcox > > flush(stderr()); flush(stdout()) > > ### Name: boxcox > ### Title: Box-Cox Transformations for Linear Models > ### Aliases: boxcox boxcox.default boxcox.formula boxcox.lm > ### Keywords: regression models hplot > > ### ** Examples > > boxcox(Volume ~ log(Height) + log(Girth), data = trees, + lambda = seq(-0.25, 0.25, length.out = 10)) > > boxcox(Days+1 ~ Eth*Sex*Age*Lrn, data = quine, + lambda = seq(-0.05, 0.45, length.out = 20)) > > > > cleanEx() > nameEx("caith") > ### * caith > > flush(stderr()); flush(stdout()) > > ### Name: caith > ### Title: Colours of Eyes and Hair of People in Caithness > ### Aliases: caith > ### Keywords: datasets > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > ## The signs can vary by platform > corresp(caith) First canonical correlation(s): 0.4463684 Row scores: blue light medium dark 0.89679252 0.98731818 -0.07530627 -1.57434710 Column scores: fair red medium dark black 1.21871379 0.52257500 0.09414671 -1.31888486 -2.45176017 > ## IGNORE_RDIFF_END > dimnames(caith)[[2]] <- c("F", "R", "M", "D", "B") > par(mfcol=c(1,3)) > plot(corresp(caith, nf=2)); title("symmetric") > plot(corresp(caith, nf=2), type="rows"); title("rows") > plot(corresp(caith, nf=2), type="col"); title("columns") > par(mfrow=c(1,1)) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("cement") > ### * cement > > flush(stderr()); flush(stdout()) > > ### Name: cement > ### Title: Heat Evolved by Setting Cements > ### Aliases: cement > ### Keywords: datasets > > ### ** Examples > > lm(y ~ x1 + x2 + x3 + x4, cement) Call: lm(formula = y ~ x1 + x2 + x3 + x4, data = cement) Coefficients: (Intercept) x1 x2 x3 x4 62.4054 1.5511 0.5102 0.1019 -0.1441 > > > > cleanEx() > nameEx("contr.sdif") > ### * contr.sdif > > flush(stderr()); flush(stdout()) > > ### Name: contr.sdif > ### Title: Successive Differences Contrast Coding > ### Aliases: contr.sdif > ### Keywords: models > > ### ** Examples > > (A <- contr.sdif(6)) 2-1 3-2 4-3 5-4 6-5 1 -0.8333333 -0.6666667 -0.5 -0.3333333 -0.1666667 2 0.1666667 -0.6666667 -0.5 -0.3333333 -0.1666667 3 0.1666667 0.3333333 -0.5 -0.3333333 -0.1666667 4 0.1666667 0.3333333 0.5 -0.3333333 -0.1666667 5 0.1666667 0.3333333 0.5 0.6666667 -0.1666667 6 0.1666667 0.3333333 0.5 0.6666667 0.8333333 > zapsmall(ginv(A)) [,1] [,2] [,3] [,4] [,5] [,6] [1,] -1 1 0 0 0 0 [2,] 0 -1 1 0 0 0 [3,] 0 0 -1 1 0 0 [4,] 0 0 0 -1 1 0 [5,] 0 0 0 0 -1 1 > > > > cleanEx() > nameEx("corresp") > ### * corresp > > flush(stderr()); flush(stdout()) > > ### Name: corresp > ### Title: Simple Correspondence Analysis > ### Aliases: corresp corresp.xtabs corresp.data.frame corresp.default > ### corresp.factor corresp.formula corresp.matrix > ### Keywords: category multivariate > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > ## The signs can vary by platform > (ct <- corresp(~ Age + Eth, data = quine)) First canonical correlation(s): 0.05317534 Age scores: F0 F1 F2 F3 -0.3344445 1.4246090 -1.0320002 -0.4612728 Eth scores: A N -1.0563816 0.9466276 > plot(ct) > > corresp(caith) First canonical correlation(s): 0.4463684 Row scores: blue light medium dark 0.89679252 0.98731818 -0.07530627 -1.57434710 Column scores: fair red medium dark black 1.21871379 0.52257500 0.09414671 -1.31888486 -2.45176017 > biplot(corresp(caith, nf = 2)) > ## IGNORE_RDIFF_END > > > > cleanEx() > nameEx("cov.rob") > ### * cov.rob > > flush(stderr()); flush(stdout()) > > ### Name: cov.rob > ### Title: Resistant Estimation of Multivariate Location and Scatter > ### Aliases: cov.rob cov.mve cov.mcd > ### Keywords: robust multivariate > > ### ** Examples > > set.seed(123) > cov.rob(stackloss) $center Air.Flow Water.Temp Acid.Conc. stack.loss 56.3750 20.0000 85.4375 13.0625 $cov Air.Flow Water.Temp Acid.Conc. stack.loss Air.Flow 23.050000 6.666667 16.625000 19.308333 Water.Temp 6.666667 5.733333 5.333333 7.733333 Acid.Conc. 16.625000 5.333333 34.395833 13.837500 stack.loss 19.308333 7.733333 13.837500 18.462500 $msg [1] "20 singular samples of size 5 out of 2500" $crit [1] 19.89056 $best [1] 5 6 7 8 9 10 11 12 15 16 18 19 20 $n.obs [1] 21 > cov.rob(stack.x, method = "mcd", nsamp = "exact") $center Air.Flow Water.Temp Acid.Conc. 56.70588 20.23529 85.52941 $cov Air.Flow Water.Temp Acid.Conc. Air.Flow 23.470588 7.573529 16.102941 Water.Temp 7.573529 6.316176 5.367647 Acid.Conc. 16.102941 5.367647 32.389706 $msg [1] "266 singular samples of size 4 out of 5985" $crit [1] 5.472581 $best [1] 4 5 6 7 8 9 10 11 12 13 14 20 $n.obs [1] 21 > > > > cleanEx() > nameEx("cov.trob") > ### * cov.trob > > flush(stderr()); flush(stdout()) > > ### Name: cov.trob > ### Title: Covariance Estimation for Multivariate t Distribution > ### Aliases: cov.trob > ### Keywords: multivariate > > ### ** Examples > > cov.trob(stackloss) $cov Air.Flow Water.Temp Acid.Conc. stack.loss Air.Flow 60.47035 17.027203 18.554452 62.28032 Water.Temp 17.02720 8.085857 5.604132 20.50469 Acid.Conc. 18.55445 5.604132 24.404633 16.91085 stack.loss 62.28032 20.504687 16.910855 72.80743 $center Air.Flow Water.Temp Acid.Conc. stack.loss 58.96905 20.79263 86.05588 16.09028 $n.obs [1] 21 $call cov.trob(x = stackloss) $iter [1] 5 > > > > cleanEx() > nameEx("denumerate") > ### * denumerate > > flush(stderr()); flush(stdout()) > > ### Name: denumerate > ### Title: Transform an Allowable Formula for 'loglm' into one for 'terms' > ### Aliases: denumerate denumerate.formula > ### Keywords: models > > ### ** Examples > > denumerate(~(1+2+3)^3 + a/b) ~(.v1 + .v2 + .v3)^3 + a/b > ## which gives ~ (.v1 + .v2 + .v3)^3 + a/b > > > > cleanEx() > nameEx("dose.p") > ### * dose.p > > flush(stderr()); flush(stdout()) > > ### Name: dose.p > ### Title: Predict Doses for Binomial Assay model > ### Aliases: dose.p print.glm.dose > ### Keywords: regression models > > ### ** Examples > > ldose <- rep(0:5, 2) > numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) > sex <- factor(rep(c("M", "F"), c(6, 6))) > SF <- cbind(numdead, numalive = 20 - numdead) > budworm.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial) > > dose.p(budworm.lg0, cf = c(1,3), p = 1:3/4) Dose SE p = 0.25: 2.231265 0.2499089 p = 0.50: 3.263587 0.2297539 p = 0.75: 4.295910 0.2746874 > dose.p(update(budworm.lg0, family = binomial(link=probit)), + cf = c(1,3), p = 1:3/4) Dose SE p = 0.25: 2.191229 0.2384478 p = 0.50: 3.257703 0.2240685 p = 0.75: 4.324177 0.2668745 > > > > cleanEx() > nameEx("dropterm") > ### * dropterm > > flush(stderr()); flush(stdout()) > > ### Name: dropterm > ### Title: Try All One-Term Deletions from a Model > ### Aliases: dropterm dropterm.default dropterm.glm dropterm.lm > ### Keywords: models > > ### ** Examples > > quine.hi <- aov(log(Days + 2.5) ~ .^4, quine) > quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn) > dropterm(quine.nxt, test= "F") Single term deletions Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Age + Eth:Sex:Lrn + Eth:Age:Lrn + Sex:Age:Lrn Df Sum of Sq RSS AIC F Value Pr(F) 64.099 -68.184 Eth:Sex:Age 3 0.97387 65.073 -71.982 0.60773 0.61125 Eth:Sex:Lrn 1 1.57879 65.678 -66.631 2.95567 0.08816 . Eth:Age:Lrn 2 2.12841 66.227 -67.415 1.99230 0.14087 Sex:Age:Lrn 2 1.46623 65.565 -68.882 1.37247 0.25743 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > quine.stp <- stepAIC(quine.nxt, + scope = list(upper = ~Eth*Sex*Age*Lrn, lower = ~1), + trace = FALSE) > dropterm(quine.stp, test = "F") Single term deletions Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Lrn + Eth:Age:Lrn Df Sum of Sq RSS AIC F Value Pr(F) 66.600 -72.597 Sex:Age 3 10.7959 77.396 -56.663 6.7542 0.0002933 *** Eth:Sex:Lrn 1 3.0325 69.632 -68.096 5.6916 0.0185476 * Eth:Age:Lrn 2 2.0960 68.696 -72.072 1.9670 0.1441822 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > quine.3 <- update(quine.stp, . ~ . - Eth:Age:Lrn) > dropterm(quine.3, test = "F") Single term deletions Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Lrn Df Sum of Sq RSS AIC F Value Pr(F) 68.696 -72.072 Eth:Age 3 3.0312 71.727 -71.768 1.8679 0.1383323 Sex:Age 3 11.4272 80.123 -55.607 7.0419 0.0002037 *** Age:Lrn 2 2.8149 71.511 -70.209 2.6020 0.0780701 . Eth:Sex:Lrn 1 4.6956 73.391 -64.419 8.6809 0.0038268 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > quine.4 <- update(quine.3, . ~ . - Eth:Age) > dropterm(quine.4, test = "F") Single term deletions Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Lrn Df Sum of Sq RSS AIC F Value Pr(F) 71.727 -71.768 Sex:Age 3 11.5656 83.292 -55.942 6.9873 0.0002147 *** Age:Lrn 2 2.9118 74.639 -69.959 2.6387 0.0752793 . Eth:Sex:Lrn 1 6.8181 78.545 -60.511 12.3574 0.0006052 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > quine.5 <- update(quine.4, . ~ . - Age:Lrn) > dropterm(quine.5, test = "F") Single term deletions Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Lrn + Sex:Age + Sex:Lrn + Eth:Sex:Lrn Df Sum of Sq RSS AIC F Value Pr(F) 74.639 -69.959 Sex:Age 3 9.9002 84.539 -57.774 5.8362 0.0008944 *** Eth:Sex:Lrn 1 6.2988 80.937 -60.130 11.1396 0.0010982 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family=poisson, + data = housing) > house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont)) > dropterm(house.glm1, test = "Chisq") Single term deletions Model: Freq ~ Infl + Type + Cont + Sat + Infl:Type + Infl:Cont + Type:Cont + Infl:Sat + Type:Sat + Cont:Sat + Infl:Type:Cont Df Deviance AIC LRT Pr(Chi) 38.662 455.63 Infl:Sat 4 147.780 556.75 109.117 < 2.2e-16 *** Type:Sat 6 100.889 505.86 62.227 1.586e-11 *** Cont:Sat 2 54.722 467.69 16.060 0.0003256 *** Infl:Type:Cont 6 43.952 448.92 5.290 0.5072454 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > cleanEx() > nameEx("eagles") > ### * eagles > > flush(stderr()); flush(stdout()) > > ### Name: eagles > ### Title: Foraging Ecology of Bald Eagles > ### Aliases: eagles > ### Keywords: datasets > > ### ** Examples > > eagles.glm <- glm(cbind(y, n - y) ~ P*A + V, data = eagles, + family = binomial) > dropterm(eagles.glm) Single term deletions Model: cbind(y, n - y) ~ P * A + V Df Deviance AIC 0.333 23.073 V 1 53.737 74.478 P:A 1 6.956 27.696 > prof <- profile(eagles.glm) > plot(prof) > pairs(prof) > > > > cleanEx() > nameEx("epil") > ### * epil > > flush(stderr()); flush(stdout()) > > ### Name: epil > ### Title: Seizure Counts for Epileptics > ### Aliases: epil > ### Keywords: datasets > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > summary(glm(y ~ lbase*trt + lage + V4, family = poisson, + data = epil), correlation = FALSE) Call: glm(formula = y ~ lbase * trt + lage + V4, family = poisson, data = epil) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.89791 0.04260 44.552 < 2e-16 *** lbase 0.94862 0.04360 21.759 < 2e-16 *** trtprogabide -0.34588 0.06100 -5.670 1.42e-08 *** lage 0.88760 0.11650 7.619 2.56e-14 *** V4 -0.15977 0.05458 -2.927 0.00342 ** lbase:trtprogabide 0.56154 0.06352 8.841 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2517.83 on 235 degrees of freedom Residual deviance: 869.07 on 230 degrees of freedom AIC: 1647 Number of Fisher Scoring iterations: 5 > ## IGNORE_RDIFF_END > epil2 <- epil[epil$period == 1, ] > epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"] > epil["time"] <- 1; epil2["time"] <- 4 > epil2 <- rbind(epil, epil2) > epil2$pred <- unclass(epil2$trt) * (epil2$period > 0) > epil2$subject <- factor(epil2$subject) > epil3 <- aggregate(epil2, list(epil2$subject, epil2$period > 0), + function(x) if(is.numeric(x)) sum(x) else x[1]) > epil3$pred <- factor(epil3$pred, + labels = c("base", "placebo", "drug")) > > contrasts(epil3$pred) <- structure(contr.sdif(3), + dimnames = list(NULL, c("placebo-base", "drug-placebo"))) > ## IGNORE_RDIFF_BEGIN > summary(glm(y ~ pred + factor(subject) + offset(log(time)), + family = poisson, data = epil3), correlation = FALSE) Call: glm(formula = y ~ pred + factor(subject) + offset(log(time)), family = poisson, data = epil3) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.122e+00 2.008e-01 5.590 2.28e-08 *** predplacebo-base 1.087e-01 4.691e-02 2.318 0.020474 * preddrug-placebo -1.016e-01 6.507e-02 -1.561 0.118431 factor(subject)2 -9.105e-16 2.828e-01 0.000 1.000000 factor(subject)3 -3.857e-01 3.144e-01 -1.227 0.219894 factor(subject)4 -1.744e-01 2.960e-01 -0.589 0.555847 factor(subject)5 1.577e+00 2.197e-01 7.178 7.08e-13 *** factor(subject)6 6.729e-01 2.458e-01 2.738 0.006182 ** factor(subject)7 -4.082e-02 2.858e-01 -0.143 0.886411 factor(subject)8 1.758e+00 2.166e-01 8.117 4.77e-16 *** factor(subject)9 5.878e-01 2.494e-01 2.356 0.018454 * factor(subject)10 5.423e-01 2.515e-01 2.156 0.031060 * factor(subject)11 1.552e+00 2.202e-01 7.048 1.81e-12 *** factor(subject)12 9.243e-01 2.364e-01 3.910 9.22e-05 *** factor(subject)13 3.075e-01 2.635e-01 1.167 0.243171 factor(subject)14 1.212e+00 2.278e-01 5.320 1.04e-07 *** factor(subject)15 1.765e+00 2.164e-01 8.153 3.54e-16 *** factor(subject)16 9.708e-01 2.348e-01 4.134 3.57e-05 *** factor(subject)17 -4.082e-02 2.858e-01 -0.143 0.886411 factor(subject)18 2.236e+00 2.104e-01 10.629 < 2e-16 *** factor(subject)19 2.776e-01 2.651e-01 1.047 0.295060 factor(subject)20 3.646e-01 2.603e-01 1.401 0.161324 factor(subject)21 3.922e-02 2.801e-01 0.140 0.888645 factor(subject)22 -8.338e-02 2.889e-01 -0.289 0.772894 factor(subject)23 1.823e-01 2.708e-01 0.673 0.500777 factor(subject)24 8.416e-01 2.393e-01 3.517 0.000436 *** factor(subject)25 2.069e+00 2.123e-01 9.750 < 2e-16 *** factor(subject)26 -5.108e-01 3.266e-01 -1.564 0.117799 factor(subject)27 -2.231e-01 3.000e-01 -0.744 0.456990 factor(subject)28 1.386e+00 2.236e-01 6.200 5.66e-10 *** factor(subject)29 1.604e+00 2.227e-01 7.203 5.90e-13 *** factor(subject)30 1.023e+00 2.372e-01 4.313 1.61e-05 *** factor(subject)31 9.149e-02 2.821e-01 0.324 0.745700 factor(subject)32 -3.111e-02 2.909e-01 -0.107 0.914822 factor(subject)33 4.710e-01 2.597e-01 1.814 0.069736 . factor(subject)34 3.887e-01 2.640e-01 1.473 0.140879 factor(subject)35 1.487e+00 2.250e-01 6.609 3.87e-11 *** factor(subject)36 3.598e-01 2.656e-01 1.355 0.175551 factor(subject)37 -1.221e-01 2.979e-01 -0.410 0.681943 factor(subject)38 1.344e+00 2.283e-01 5.889 3.90e-09 *** factor(subject)39 1.082e+00 2.354e-01 4.596 4.30e-06 *** factor(subject)40 -7.687e-01 3.634e-01 -2.116 0.034384 * factor(subject)41 1.656e-01 2.772e-01 0.597 0.550234 factor(subject)42 5.227e-02 2.848e-01 0.184 0.854388 factor(subject)43 1.543e+00 2.239e-01 6.891 5.54e-12 *** factor(subject)44 9.605e-01 2.393e-01 4.014 5.96e-05 *** factor(subject)45 1.177e+00 2.326e-01 5.061 4.18e-07 *** factor(subject)46 -5.275e-01 3.355e-01 -1.572 0.115840 factor(subject)47 1.053e+00 2.363e-01 4.456 8.35e-06 *** factor(subject)48 -5.275e-01 3.355e-01 -1.572 0.115840 factor(subject)49 2.949e+00 2.082e-01 14.168 < 2e-16 *** factor(subject)50 3.887e-01 2.640e-01 1.473 0.140879 factor(subject)51 1.038e+00 2.367e-01 4.385 1.16e-05 *** factor(subject)52 5.711e-01 2.548e-01 2.241 0.025023 * factor(subject)53 1.670e+00 2.215e-01 7.538 4.76e-14 *** factor(subject)54 4.443e-01 2.611e-01 1.702 0.088759 . factor(subject)55 2.674e-01 2.709e-01 0.987 0.323618 factor(subject)56 1.124e+00 2.341e-01 4.800 1.59e-06 *** factor(subject)57 2.674e-01 2.709e-01 0.987 0.323618 factor(subject)58 -6.017e-01 3.436e-01 -1.751 0.079911 . factor(subject)59 -7.556e-02 2.942e-01 -0.257 0.797331 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 3180.82 on 117 degrees of freedom Residual deviance: 303.16 on 57 degrees of freedom AIC: 1003.5 Number of Fisher Scoring iterations: 5 > ## IGNORE_RDIFF_END > > summary(glmmPQL(y ~ lbase*trt + lage + V4, + random = ~ 1 | subject, + family = poisson, data = epil)) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 Linear mixed-effects model fit by maximum likelihood Data: epil AIC BIC logLik NA NA NA Random effects: Formula: ~1 | subject (Intercept) Residual StdDev: 0.4442704 1.400807 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ lbase * trt + lage + V4 Value Std.Error DF t-value p-value (Intercept) 1.8696677 0.1055620 176 17.711554 0.0000 lbase 0.8818228 0.1292834 54 6.820849 0.0000 trtprogabide -0.3095253 0.1490438 54 -2.076740 0.0426 lage 0.5335460 0.3463119 54 1.540652 0.1292 V4 -0.1597696 0.0774521 176 -2.062819 0.0406 lbase:trtprogabide 0.3415425 0.2033325 54 1.679725 0.0988 Correlation: (Intr) lbase trtprg lage V4 lbase -0.126 trtprogabide -0.691 0.089 lage -0.103 -0.038 0.088 V4 -0.162 0.000 0.000 0.000 lbase:trtprogabide 0.055 -0.645 -0.184 0.267 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.13240534 -0.63871136 -0.08486339 0.41960195 4.97872138 Number of Observations: 236 Number of Groups: 59 > summary(glmmPQL(y ~ pred, random = ~1 | subject, + family = poisson, data = epil3)) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 iteration 7 iteration 8 Linear mixed-effects model fit by maximum likelihood Data: epil3 AIC BIC logLik NA NA NA Random effects: Formula: ~1 | subject (Intercept) Residual StdDev: 0.7257895 2.16629 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ pred Value Std.Error DF t-value p-value (Intercept) 3.213631 0.10569117 58 30.405865 0.0000 predplacebo-base 0.110855 0.09989089 57 1.109763 0.2718 preddrug-placebo -0.105613 0.13480483 57 -0.783450 0.4366 Correlation: (Intr) prdpl- predplacebo-base 0.081 preddrug-placebo -0.010 -0.700 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.0446864 -0.4765135 -0.1975651 0.3145761 2.6532834 Number of Observations: 118 Number of Groups: 59 > > > > cleanEx() > nameEx("farms") > ### * farms > > flush(stderr()); flush(stdout()) > > ### Name: farms > ### Title: Ecological Factors in Farm Management > ### Aliases: farms > ### Keywords: datasets > > ### ** Examples > > farms.mca <- mca(farms, abbrev = TRUE) # Use levels as names > eqscplot(farms.mca$cs, type = "n") > text(farms.mca$rs, cex = 0.7) > text(farms.mca$cs, labels = dimnames(farms.mca$cs)[[1]], cex = 0.7) > > > > cleanEx() > nameEx("fitdistr") > ### * fitdistr > > flush(stderr()); flush(stdout()) > > ### Name: fitdistr > ### Title: Maximum-likelihood Fitting of Univariate Distributions > ### Aliases: fitdistr > ### Keywords: distribution htest > > ### ** Examples > > ## avoid spurious accuracy > op <- options(digits = 3) > set.seed(123) > x <- rgamma(100, shape = 5, rate = 0.1) > fitdistr(x, "gamma") shape rate 6.4870 0.1365 (0.8946) (0.0196) > ## now do this directly with more control. > fitdistr(x, dgamma, list(shape = 1, rate = 0.1), lower = 0.001) shape rate 6.4869 0.1365 (0.8944) (0.0196) > > set.seed(123) > x2 <- rt(250, df = 9) > fitdistr(x2, "t", df = 9) m s -0.0107 1.0441 ( 0.0722) ( 0.0543) > ## allow df to vary: not a very good idea! > fitdistr(x2, "t") Warning in dt((x - m)/s, df, log = TRUE) : NaNs produced m s df -0.00965 1.00617 6.62729 ( 0.07147) ( 0.07707) ( 2.71033) > ## now do fixed-df fit directly with more control. > mydt <- function(x, m, s, df) dt((x-m)/s, df)/s > fitdistr(x2, mydt, list(m = 0, s = 1), df = 9, lower = c(-Inf, 0)) m s -0.0107 1.0441 ( 0.0722) ( 0.0543) > > set.seed(123) > x3 <- rweibull(100, shape = 4, scale = 100) > fitdistr(x3, "weibull") shape scale 4.080 99.984 ( 0.313) ( 2.582) > > set.seed(123) > x4 <- rnegbin(500, mu = 5, theta = 4) > fitdistr(x4, "Negative Binomial") size mu 4.216 4.945 (0.504) (0.147) > options(op) > > > > cleanEx() > nameEx("fractions") > ### * fractions > > flush(stderr()); flush(stdout()) > > ### Name: fractions > ### Title: Rational Approximation > ### Aliases: fractions Math.fractions Ops.fractions Summary.fractions > ### [.fractions [<-.fractions as.character.fractions as.fractions > ### is.fractions print.fractions t.fractions > ### Keywords: math > > ### ** Examples > > X <- matrix(runif(25), 5, 5) > zapsmall(solve(X, X/5)) # print near-zeroes as zero [,1] [,2] [,3] [,4] [,5] [1,] 0.2 0.0 0.0 0.0 0.0 [2,] 0.0 0.2 0.0 0.0 0.0 [3,] 0.0 0.0 0.2 0.0 0.0 [4,] 0.0 0.0 0.0 0.2 0.0 [5,] 0.0 0.0 0.0 0.0 0.2 > fractions(solve(X, X/5)) [,1] [,2] [,3] [,4] [,5] [1,] 1/5 0 0 0 0 [2,] 0 1/5 0 0 0 [3,] 0 0 1/5 0 0 [4,] 0 0 0 1/5 0 [5,] 0 0 0 0 1/5 > fractions(solve(X, X/5)) + 1 [,1] [,2] [,3] [,4] [,5] [1,] 6/5 1 1 1 1 [2,] 1 6/5 1 1 1 [3,] 1 1 6/5 1 1 [4,] 1 1 1 6/5 1 [5,] 1 1 1 1 6/5 > > > > cleanEx() > nameEx("galaxies") > ### * galaxies > > flush(stderr()); flush(stdout()) > > ### Name: galaxies > ### Title: Velocities for 82 Galaxies > ### Aliases: galaxies > ### Keywords: datasets > > ### ** Examples > > gal <- galaxies/1000 > c(width.SJ(gal, method = "dpi"), width.SJ(gal)) [1] 3.256151 2.566423 > plot(x = c(0, 40), y = c(0, 0.3), type = "n", bty = "l", + xlab = "velocity of galaxy (1000km/s)", ylab = "density") > rug(gal) > lines(density(gal, width = 3.25, n = 200), lty = 1) > lines(density(gal, width = 2.56, n = 200), lty = 3) > > > > cleanEx() > nameEx("gamma.shape.glm") > ### * gamma.shape.glm > > flush(stderr()); flush(stdout()) > > ### Name: gamma.shape > ### Title: Estimate the Shape Parameter of the Gamma Distribution in a GLM > ### Fit > ### Aliases: gamma.shape gamma.shape.glm print.gamma.shape > ### Keywords: models > > ### ** Examples > > clotting <- data.frame( + u = c(5,10,15,20,30,40,60,80,100), + lot1 = c(118,58,42,35,27,25,21,19,18), + lot2 = c(69,35,26,21,18,16,13,12,12)) > clot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma) > gamma.shape(clot1) Alpha: 538.1315 SE: 253.5991 > > gm <- glm(Days + 0.1 ~ Age*Eth*Sex*Lrn, + quasi(link=log, variance="mu^2"), quine, + start = c(3, rep(0,31))) > gamma.shape(gm, verbose = TRUE) Initial estimate: 1.060344 Iter. 1 Alpha: 1.238408 Iter. 2 Alpha: 1.276997 Iter. 3 Alpha: 1.278343 Iter. 4 Alpha: 1.278345 Alpha: 1.2783449 SE: 0.1345175 > ## IGNORE_RDIFF_BEGIN > summary(gm, dispersion = gamma.dispersion(gm)) # better summary Call: glm(formula = Days + 0.1 ~ Age * Eth * Sex * Lrn, family = quasi(link = log, variance = "mu^2"), data = quine, start = c(3, rep(0, 31))) Coefficients: (4 not defined because of singularities) Estimate Std. Error z value Pr(>|z|) (Intercept) 3.06105 0.44223 6.922 4.46e-12 *** AgeF1 -0.61870 0.59331 -1.043 0.297041 AgeF2 -2.31911 0.98885 -2.345 0.019014 * AgeF3 -0.37623 0.53149 -0.708 0.479020 EthN -0.13789 0.62540 -0.220 0.825496 SexM -0.48844 0.59331 -0.823 0.410369 LrnSL -1.92965 0.98885 -1.951 0.051009 . AgeF1:EthN 0.10249 0.82338 0.124 0.900942 AgeF2:EthN -0.50874 1.39845 -0.364 0.716017 AgeF3:EthN 0.06314 0.74584 0.085 0.932534 AgeF1:SexM 0.40695 0.94847 0.429 0.667884 AgeF2:SexM 3.06173 1.11626 2.743 0.006091 ** AgeF3:SexM 1.10841 0.74208 1.494 0.135267 EthN:SexM -0.74217 0.82338 -0.901 0.367394 AgeF1:LrnSL 2.60967 1.10114 2.370 0.017789 * AgeF2:LrnSL 4.78434 1.36304 3.510 0.000448 *** AgeF3:LrnSL NA NA NA NA EthN:LrnSL 2.22936 1.39845 1.594 0.110899 SexM:LrnSL 1.56531 1.18112 1.325 0.185077 AgeF1:EthN:SexM -0.30235 1.32176 -0.229 0.819065 AgeF2:EthN:SexM 0.29742 1.57035 0.189 0.849780 AgeF3:EthN:SexM 0.82215 1.03277 0.796 0.425995 AgeF1:EthN:LrnSL -3.50803 1.54655 -2.268 0.023311 * AgeF2:EthN:LrnSL -3.33529 1.92481 -1.733 0.083133 . AgeF3:EthN:LrnSL NA NA NA NA AgeF1:SexM:LrnSL -2.39791 1.51050 -1.587 0.112400 AgeF2:SexM:LrnSL -4.12161 1.60698 -2.565 0.010323 * AgeF3:SexM:LrnSL NA NA NA NA EthN:SexM:LrnSL -0.15305 1.66253 -0.092 0.926653 AgeF1:EthN:SexM:LrnSL 2.13480 2.08685 1.023 0.306317 AgeF2:EthN:SexM:LrnSL 2.11886 2.27882 0.930 0.352473 AgeF3:EthN:SexM:LrnSL NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for quasi family taken to be 0.7822615) Null deviance: 190.40 on 145 degrees of freedom Residual deviance: 128.36 on 118 degrees of freedom AIC: NA Number of Fisher Scoring iterations: 7 > ## IGNORE_RDIFF_END > > > > cleanEx() > nameEx("gehan") > ### * gehan > > flush(stderr()); flush(stdout()) > > ### Name: gehan > ### Title: Remission Times of Leukaemia Patients > ### Aliases: gehan > ### Keywords: datasets > > ### ** Examples > > library(survival) > gehan.surv <- survfit(Surv(time, cens) ~ treat, data = gehan, + conf.type = "log-log") > summary(gehan.surv) Call: survfit(formula = Surv(time, cens) ~ treat, data = gehan, conf.type = "log-log") treat=6-MP time n.risk n.event survival std.err lower 95% CI upper 95% CI 6 21 3 0.857 0.0764 0.620 0.952 7 17 1 0.807 0.0869 0.563 0.923 10 15 1 0.753 0.0963 0.503 0.889 13 12 1 0.690 0.1068 0.432 0.849 16 11 1 0.627 0.1141 0.368 0.805 22 7 1 0.538 0.1282 0.268 0.747 23 6 1 0.448 0.1346 0.188 0.680 treat=control time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 21 2 0.9048 0.0641 0.67005 0.975 2 19 2 0.8095 0.0857 0.56891 0.924 3 17 1 0.7619 0.0929 0.51939 0.893 4 16 2 0.6667 0.1029 0.42535 0.825 5 14 2 0.5714 0.1080 0.33798 0.749 8 12 4 0.3810 0.1060 0.18307 0.578 11 8 2 0.2857 0.0986 0.11656 0.482 12 6 2 0.1905 0.0857 0.05948 0.377 15 4 1 0.1429 0.0764 0.03566 0.321 17 3 1 0.0952 0.0641 0.01626 0.261 22 2 1 0.0476 0.0465 0.00332 0.197 23 1 1 0.0000 NaN NA NA > survreg(Surv(time, cens) ~ factor(pair) + treat, gehan, dist = "exponential") Call: survreg(formula = Surv(time, cens) ~ factor(pair) + treat, data = gehan, dist = "exponential") Coefficients: (Intercept) factor(pair)2 factor(pair)3 factor(pair)4 factor(pair)5 2.0702861 2.1476909 1.8329493 1.7718527 1.4682566 factor(pair)6 factor(pair)7 factor(pair)8 factor(pair)9 factor(pair)10 1.8954775 0.5583010 2.5187140 2.2970513 2.4862208 factor(pair)11 factor(pair)12 factor(pair)13 factor(pair)14 factor(pair)15 1.0524472 1.8270477 1.6772567 1.7778672 2.0859913 factor(pair)16 factor(pair)17 factor(pair)18 factor(pair)19 factor(pair)20 3.0634288 0.7996252 1.5855018 1.4083884 0.4023946 factor(pair)21 treatcontrol 1.9698390 -1.7671562 Scale fixed at 1 Loglik(model)= -101.6 Loglik(intercept only)= -116.8 Chisq= 30.27 on 21 degrees of freedom, p= 0.0866 n= 42 > summary(survreg(Surv(time, cens) ~ treat, gehan, dist = "exponential")) Call: survreg(formula = Surv(time, cens) ~ treat, data = gehan, dist = "exponential") Value Std. Error z p (Intercept) 3.686 0.333 11.06 < 2e-16 treatcontrol -1.527 0.398 -3.83 0.00013 Scale fixed at 1 Exponential distribution Loglik(model)= -108.5 Loglik(intercept only)= -116.8 Chisq= 16.49 on 1 degrees of freedom, p= 4.9e-05 Number of Newton-Raphson Iterations: 4 n= 42 > summary(survreg(Surv(time, cens) ~ treat, gehan)) Call: survreg(formula = Surv(time, cens) ~ treat, data = gehan) Value Std. Error z p (Intercept) 3.516 0.252 13.96 < 2e-16 treatcontrol -1.267 0.311 -4.08 4.5e-05 Log(scale) -0.312 0.147 -2.12 0.034 Scale= 0.732 Weibull distribution Loglik(model)= -106.6 Loglik(intercept only)= -116.4 Chisq= 19.65 on 1 degrees of freedom, p= 9.3e-06 Number of Newton-Raphson Iterations: 5 n= 42 > gehan.cox <- coxph(Surv(time, cens) ~ treat, gehan) > summary(gehan.cox) Call: coxph(formula = Surv(time, cens) ~ treat, data = gehan) n= 42, number of events= 30 coef exp(coef) se(coef) z Pr(>|z|) treatcontrol 1.5721 4.8169 0.4124 3.812 0.000138 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 treatcontrol 4.817 0.2076 2.147 10.81 Concordance= 0.69 (se = 0.041 ) Likelihood ratio test= 16.35 on 1 df, p=5e-05 Wald test = 14.53 on 1 df, p=1e-04 Score (logrank) test = 17.25 on 1 df, p=3e-05 > > > > cleanEx() detaching ‘package:survival’ > nameEx("glm.convert") > ### * glm.convert > > flush(stderr()); flush(stdout()) > > ### Name: glm.convert > ### Title: Change a Negative Binomial fit to a GLM fit > ### Aliases: glm.convert > ### Keywords: regression models > > ### ** Examples > > quine.nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) > quine.nbA <- glm.convert(quine.nb1) > quine.nbB <- update(quine.nb1, . ~ . + Sex:Age:Lrn) > anova(quine.nbA, quine.nbB) Analysis of Deviance Table Model 1: Days ~ Sex/(Age + Eth * Lrn) Model 2: Days ~ Sex + Sex:Age + Sex:Eth + Sex:Lrn + Sex:Eth:Lrn + Sex:Age:Lrn Resid. Df Resid. Dev Df Deviance 1 132 167.56 2 128 166.83 4 0.723 > > > > cleanEx() > nameEx("glm.nb") > ### * glm.nb > > flush(stderr()); flush(stdout()) > > ### Name: glm.nb > ### Title: Fit a Negative Binomial Generalized Linear Model > ### Aliases: glm.nb family.negbin logLik.negbin > ### Keywords: regression models > > ### ** Examples > > quine.nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) > quine.nb2 <- update(quine.nb1, . ~ . + Sex:Age:Lrn) > quine.nb3 <- update(quine.nb2, Days ~ .^4) > anova(quine.nb1, quine.nb2, quine.nb3) Likelihood ratio tests of Negative Binomial Models Response: Days Model 1 Sex/(Age + Eth * Lrn) 2 Sex + Sex:Age + Sex:Eth + Sex:Lrn + Sex:Eth:Lrn + Sex:Age:Lrn 3 Sex + Sex:Age + Sex:Eth + Sex:Lrn + Sex:Eth:Lrn + Sex:Age:Lrn + Sex:Age:Eth + Sex:Age:Eth:Lrn theta Resid. df 2 x log-lik. Test df LR stat. Pr(Chi) 1 1.597991 132 -1063.025 2 1.686899 128 -1055.398 1 vs 2 4 7.627279 0.10622602 3 1.928360 118 -1039.324 2 vs 3 10 16.073723 0.09754136 > ## Don't show: > ## PR#1695 > y <- c(7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, 7, 10, 6, 12, + 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5, 3, 3, 4) > > lag1 <- c(0, 7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, 7, 10, + 6, 12, 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5, 3, 3) > > lag2 <- c(0, 0, 7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, 7, + 10, 6, 12, 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5, 3) > > lag3 <- c(0, 0, 0, 7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, + 7, 10, 6, 12, 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5) > > (fit <- glm(y ~ lag1+lag2+lag3, family=poisson(link=identity), + start=c(2, 0.1, 0.1, 0.1))) Call: glm(formula = y ~ lag1 + lag2 + lag3, family = poisson(link = identity), start = c(2, 0.1, 0.1, 0.1)) Coefficients: (Intercept) lag1 lag2 lag3 2.6609 0.1573 0.1424 0.1458 Degrees of Freedom: 41 Total (i.e. Null); 38 Residual Null Deviance: 100.2 Residual Deviance: 90.34 AIC: 225.6 > try(glm.nb(y ~ lag1+lag2+lag3, link=identity)) Warning in log(y/mu) : NaNs produced Error : no valid set of coefficients has been found: please supply starting values > glm.nb(y ~ lag1+lag2+lag3, link=identity, start=c(2, 0.1, 0.1, 0.1)) Call: glm.nb(formula = y ~ lag1 + lag2 + lag3, start = c(2, 0.1, 0.1, 0.1), link = identity, init.theta = 4.406504429) Coefficients: (Intercept) lag1 lag2 lag3 2.6298 0.1774 0.1407 0.1346 Degrees of Freedom: 41 Total (i.e. Null); 38 Residual Null Deviance: 55.07 Residual Deviance: 50.09 AIC: 215.9 > glm.nb(y ~ lag1+lag2+lag3, link=identity, start=coef(fit)) Call: glm.nb(formula = y ~ lag1 + lag2 + lag3, start = coef(fit), link = identity, init.theta = 4.406504429) Coefficients: (Intercept) lag1 lag2 lag3 2.6298 0.1774 0.1407 0.1346 Degrees of Freedom: 41 Total (i.e. Null); 38 Residual Null Deviance: 55.07 Residual Deviance: 50.09 AIC: 215.9 > glm.nb(y ~ lag1+lag2+lag3, link=identity, etastart=rep(5, 42)) Call: glm.nb(formula = y ~ lag1 + lag2 + lag3, etastart = rep(5, 42), link = identity, init.theta = 4.406504429) Coefficients: (Intercept) lag1 lag2 lag3 2.6298 0.1774 0.1407 0.1346 Degrees of Freedom: 41 Total (i.e. Null); 38 Residual Null Deviance: 55.07 Residual Deviance: 50.09 AIC: 215.9 > ## End(Don't show) > > > cleanEx() > nameEx("glmmPQL") > ### * glmmPQL > > flush(stderr()); flush(stdout()) > > ### Name: glmmPQL > ### Title: Fit Generalized Linear Mixed Models via PQL > ### Aliases: glmmPQL > ### Keywords: models > > ### ** Examples > > summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID, + family = binomial, data = bacteria)) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 Linear mixed-effects model fit by maximum likelihood Data: bacteria AIC BIC logLik NA NA NA Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.410637 0.7800511 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ trt + I(week > 2) Value Std.Error DF t-value p-value (Intercept) 3.412014 0.5185033 169 6.580506 0.0000 trtdrug -1.247355 0.6440635 47 -1.936696 0.0588 trtdrug+ -0.754327 0.6453978 47 -1.168779 0.2484 I(week > 2)TRUE -1.607257 0.3583379 169 -4.485311 0.0000 Correlation: (Intr) trtdrg trtdr+ trtdrug -0.598 trtdrug+ -0.571 0.460 I(week > 2)TRUE -0.537 0.047 -0.001 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -5.1985361 0.1572336 0.3513075 0.4949482 1.7448845 Number of Observations: 220 Number of Groups: 50 > > ## an example of an offset: the coefficient of 'week' changes by one. > summary(glmmPQL(y ~ trt + week, random = ~ 1 | ID, + family = binomial, data = bacteria)) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 Linear mixed-effects model fit by maximum likelihood Data: bacteria AIC BIC logLik NA NA NA Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.325243 0.7903088 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ trt + week Value Std.Error DF t-value p-value (Intercept) 3.0302276 0.4791396 169 6.324310 0.0000 trtdrug -1.2176812 0.6160113 47 -1.976719 0.0540 trtdrug+ -0.7886376 0.6193895 47 -1.273250 0.2092 week -0.1446463 0.0392343 169 -3.686730 0.0003 Correlation: (Intr) trtdrg trtdr+ trtdrug -0.622 trtdrug+ -0.609 0.464 week -0.481 0.050 0.030 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.2868074 0.2039043 0.3140333 0.5440835 1.9754065 Number of Observations: 220 Number of Groups: 50 > summary(glmmPQL(y ~ trt + week + offset(week), random = ~ 1 | ID, + family = binomial, data = bacteria)) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 Linear mixed-effects model fit by maximum likelihood Data: bacteria AIC BIC logLik NA NA NA Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.325243 0.7903088 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ trt + week + offset(week) Value Std.Error DF t-value p-value (Intercept) 3.0302276 0.4791396 169 6.324310 0.0000 trtdrug -1.2176812 0.6160113 47 -1.976719 0.0540 trtdrug+ -0.7886376 0.6193895 47 -1.273250 0.2092 week -1.1446463 0.0392343 169 -29.174622 0.0000 Correlation: (Intr) trtdrg trtdr+ trtdrug -0.622 trtdrug+ -0.609 0.464 week -0.481 0.050 0.030 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.2868074 0.2039043 0.3140333 0.5440835 1.9754065 Number of Observations: 220 Number of Groups: 50 > > > > cleanEx() > nameEx("housing") > ### * housing > > flush(stderr()); flush(stdout()) > > ### Name: housing > ### Title: Frequency Table from a Copenhagen Housing Conditions Survey > ### Aliases: housing > ### Keywords: datasets > > ### ** Examples > > options(contrasts = c("contr.treatment", "contr.poly")) > > # Surrogate Poisson models > house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family = poisson, + data = housing) > ## IGNORE_RDIFF_BEGIN > summary(house.glm0, correlation = FALSE) Call: glm(formula = Freq ~ Infl * Type * Cont + Sat, family = poisson, data = housing) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.136e+00 1.196e-01 26.225 < 2e-16 *** InflMedium 2.733e-01 1.586e-01 1.723 0.084868 . InflHigh -2.054e-01 1.784e-01 -1.152 0.249511 TypeApartment 3.666e-01 1.555e-01 2.357 0.018403 * TypeAtrium -7.828e-01 2.134e-01 -3.668 0.000244 *** TypeTerrace -8.145e-01 2.157e-01 -3.775 0.000160 *** ContHigh 1.402e-15 1.690e-01 0.000 1.000000 Sat.L 1.159e-01 4.038e-02 2.871 0.004094 ** Sat.Q 2.629e-01 4.515e-02 5.824 5.76e-09 *** InflMedium:TypeApartment -1.177e-01 2.086e-01 -0.564 0.572571 InflHigh:TypeApartment 1.753e-01 2.279e-01 0.769 0.441783 InflMedium:TypeAtrium -4.068e-01 3.035e-01 -1.340 0.180118 InflHigh:TypeAtrium -1.692e-01 3.294e-01 -0.514 0.607433 InflMedium:TypeTerrace 6.292e-03 2.860e-01 0.022 0.982450 InflHigh:TypeTerrace -9.305e-02 3.280e-01 -0.284 0.776633 InflMedium:ContHigh -1.398e-01 2.279e-01 -0.613 0.539715 InflHigh:ContHigh -6.091e-01 2.800e-01 -2.176 0.029585 * TypeApartment:ContHigh 5.029e-01 2.109e-01 2.385 0.017083 * TypeAtrium:ContHigh 6.774e-01 2.751e-01 2.462 0.013811 * TypeTerrace:ContHigh 1.099e+00 2.675e-01 4.106 4.02e-05 *** InflMedium:TypeApartment:ContHigh 5.359e-02 2.862e-01 0.187 0.851450 InflHigh:TypeApartment:ContHigh 1.462e-01 3.380e-01 0.432 0.665390 InflMedium:TypeAtrium:ContHigh 1.555e-01 3.907e-01 0.398 0.690597 InflHigh:TypeAtrium:ContHigh 4.782e-01 4.441e-01 1.077 0.281619 InflMedium:TypeTerrace:ContHigh -4.980e-01 3.671e-01 -1.357 0.174827 InflHigh:TypeTerrace:ContHigh -4.470e-01 4.545e-01 -0.984 0.325326 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 833.66 on 71 degrees of freedom Residual deviance: 217.46 on 46 degrees of freedom AIC: 610.43 Number of Fisher Scoring iterations: 5 > ## IGNORE_RDIFF_END > > addterm(house.glm0, ~. + Sat:(Infl+Type+Cont), test = "Chisq") Single term additions Model: Freq ~ Infl * Type * Cont + Sat Df Deviance AIC LRT Pr(Chi) 217.46 610.43 Infl:Sat 4 111.08 512.05 106.371 < 2.2e-16 *** Type:Sat 6 156.79 561.76 60.669 3.292e-11 *** Cont:Sat 2 212.33 609.30 5.126 0.07708 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont)) > ## IGNORE_RDIFF_BEGIN > summary(house.glm1, correlation = FALSE) Call: glm(formula = Freq ~ Infl + Type + Cont + Sat + Infl:Type + Infl:Cont + Type:Cont + Infl:Sat + Type:Sat + Cont:Sat + Infl:Type:Cont, family = poisson, data = housing) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.135074 0.120112 26.101 < 2e-16 *** InflMedium 0.248327 0.159979 1.552 0.120602 InflHigh -0.412645 0.184947 -2.231 0.025671 * TypeApartment 0.292524 0.157477 1.858 0.063231 . TypeAtrium -0.792847 0.214413 -3.698 0.000218 *** TypeTerrace -1.018074 0.221263 -4.601 4.20e-06 *** ContHigh -0.001407 0.169711 -0.008 0.993385 Sat.L -0.098106 0.112592 -0.871 0.383570 Sat.Q 0.285657 0.122283 2.336 0.019489 * InflMedium:TypeApartment -0.017882 0.210496 -0.085 0.932302 InflHigh:TypeApartment 0.386869 0.233297 1.658 0.097263 . InflMedium:TypeAtrium -0.360311 0.304979 -1.181 0.237432 InflHigh:TypeAtrium -0.036788 0.334793 -0.110 0.912503 InflMedium:TypeTerrace 0.185154 0.288892 0.641 0.521580 InflHigh:TypeTerrace 0.310749 0.334815 0.928 0.353345 InflMedium:ContHigh -0.200060 0.228748 -0.875 0.381799 InflHigh:ContHigh -0.725790 0.282352 -2.571 0.010155 * TypeApartment:ContHigh 0.569691 0.212152 2.685 0.007247 ** TypeAtrium:ContHigh 0.702115 0.276056 2.543 0.010979 * TypeTerrace:ContHigh 1.215930 0.269968 4.504 6.67e-06 *** InflMedium:Sat.L 0.519627 0.096830 5.366 8.03e-08 *** InflHigh:Sat.L 1.140302 0.118180 9.649 < 2e-16 *** InflMedium:Sat.Q -0.064474 0.102666 -0.628 0.530004 InflHigh:Sat.Q 0.115436 0.127798 0.903 0.366380 TypeApartment:Sat.L -0.520170 0.109793 -4.738 2.16e-06 *** TypeAtrium:Sat.L -0.288484 0.149551 -1.929 0.053730 . TypeTerrace:Sat.L -0.998666 0.141527 -7.056 1.71e-12 *** TypeApartment:Sat.Q 0.055418 0.118515 0.468 0.640068 TypeAtrium:Sat.Q -0.273820 0.149713 -1.829 0.067405 . TypeTerrace:Sat.Q -0.032328 0.149251 -0.217 0.828520 ContHigh:Sat.L 0.340703 0.087778 3.881 0.000104 *** ContHigh:Sat.Q -0.097929 0.094068 -1.041 0.297851 InflMedium:TypeApartment:ContHigh 0.046900 0.286212 0.164 0.869837 InflHigh:TypeApartment:ContHigh 0.126229 0.338208 0.373 0.708979 InflMedium:TypeAtrium:ContHigh 0.157239 0.390719 0.402 0.687364 InflHigh:TypeAtrium:ContHigh 0.478611 0.444244 1.077 0.281320 InflMedium:TypeTerrace:ContHigh -0.500162 0.367135 -1.362 0.173091 InflHigh:TypeTerrace:ContHigh -0.463099 0.454713 -1.018 0.308467 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 833.657 on 71 degrees of freedom Residual deviance: 38.662 on 34 degrees of freedom AIC: 455.63 Number of Fisher Scoring iterations: 4 > ## IGNORE_RDIFF_END > > 1 - pchisq(deviance(house.glm1), house.glm1$df.residual) [1] 0.2671363 > > dropterm(house.glm1, test = "Chisq") Single term deletions Model: Freq ~ Infl + Type + Cont + Sat + Infl:Type + Infl:Cont + Type:Cont + Infl:Sat + Type:Sat + Cont:Sat + Infl:Type:Cont Df Deviance AIC LRT Pr(Chi) 38.662 455.63 Infl:Sat 4 147.780 556.75 109.117 < 2.2e-16 *** Type:Sat 6 100.889 505.86 62.227 1.586e-11 *** Cont:Sat 2 54.722 467.69 16.060 0.0003256 *** Infl:Type:Cont 6 43.952 448.92 5.290 0.5072454 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test = "Chisq") Single term additions Model: Freq ~ Infl + Type + Cont + Sat + Infl:Type + Infl:Cont + Type:Cont + Infl:Sat + Type:Sat + Cont:Sat + Infl:Type:Cont Df Deviance AIC LRT Pr(Chi) 38.662 455.63 Infl:Type:Sat 12 16.107 457.08 22.5550 0.03175 * Infl:Cont:Sat 4 37.472 462.44 1.1901 0.87973 Type:Cont:Sat 6 28.256 457.23 10.4064 0.10855 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > hnames <- lapply(housing[, -5], levels) # omit Freq > newData <- expand.grid(hnames) > newData$Sat <- ordered(newData$Sat) > house.pm <- predict(house.glm1, newData, + type = "response") # poisson means > house.pm <- matrix(house.pm, ncol = 3, byrow = TRUE, + dimnames = list(NULL, hnames[[1]])) > house.pr <- house.pm/drop(house.pm %*% rep(1, 3)) > cbind(expand.grid(hnames[-1]), round(house.pr, 2)) Infl Type Cont Low Medium High 1 Low Tower Low 0.40 0.26 0.34 2 Medium Tower Low 0.26 0.27 0.47 3 High Tower Low 0.15 0.19 0.66 4 Low Apartment Low 0.54 0.23 0.23 5 Medium Apartment Low 0.39 0.26 0.34 6 High Apartment Low 0.26 0.21 0.53 7 Low Atrium Low 0.43 0.32 0.25 8 Medium Atrium Low 0.30 0.35 0.36 9 High Atrium Low 0.19 0.27 0.54 10 Low Terrace Low 0.65 0.22 0.14 11 Medium Terrace Low 0.51 0.27 0.22 12 High Terrace Low 0.37 0.24 0.39 13 Low Tower High 0.30 0.28 0.42 14 Medium Tower High 0.18 0.27 0.54 15 High Tower High 0.10 0.19 0.71 16 Low Apartment High 0.44 0.27 0.30 17 Medium Apartment High 0.30 0.28 0.42 18 High Apartment High 0.18 0.21 0.61 19 Low Atrium High 0.33 0.36 0.31 20 Medium Atrium High 0.22 0.36 0.42 21 High Atrium High 0.13 0.27 0.60 22 Low Terrace High 0.55 0.27 0.19 23 Medium Terrace High 0.40 0.31 0.29 24 High Terrace High 0.27 0.26 0.47 > > # Iterative proportional scaling > loglm(Freq ~ Infl*Type*Cont + Sat*(Infl+Type+Cont), data = housing) Call: loglm(formula = Freq ~ Infl * Type * Cont + Sat * (Infl + Type + Cont), data = housing) Statistics: X^2 df P(> X^2) Likelihood Ratio 38.66222 34 0.2671359 Pearson 38.90831 34 0.2582333 > > > # multinomial model > library(nnet) > (house.mult<- multinom(Sat ~ Infl + Type + Cont, weights = Freq, + data = housing)) # weights: 24 (14 variable) initial value 1846.767257 iter 10 value 1747.045232 final value 1735.041933 converged Call: multinom(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq) Coefficients: (Intercept) InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace Medium -0.4192316 0.4464003 0.6649367 -0.4356851 0.1313663 -0.6665728 High -0.1387453 0.7348626 1.6126294 -0.7356261 -0.4079808 -1.4123333 ContHigh Medium 0.3608513 High 0.4818236 Residual Deviance: 3470.084 AIC: 3498.084 > house.mult2 <- multinom(Sat ~ Infl*Type*Cont, weights = Freq, + data = housing) # weights: 75 (48 variable) initial value 1846.767257 iter 10 value 1734.465581 iter 20 value 1717.220153 iter 30 value 1715.760679 iter 40 value 1715.713306 final value 1715.710836 converged > anova(house.mult, house.mult2) Likelihood ratio tests of Multinomial Models Response: Sat Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi) 1 Infl + Type + Cont 130 3470.084 2 Infl * Type * Cont 96 3431.422 1 vs 2 34 38.66219 0.2671367 > > house.pm <- predict(house.mult, expand.grid(hnames[-1]), type = "probs") > cbind(expand.grid(hnames[-1]), round(house.pm, 2)) Infl Type Cont Low Medium High 1 Low Tower Low 0.40 0.26 0.34 2 Medium Tower Low 0.26 0.27 0.47 3 High Tower Low 0.15 0.19 0.66 4 Low Apartment Low 0.54 0.23 0.23 5 Medium Apartment Low 0.39 0.26 0.34 6 High Apartment Low 0.26 0.21 0.53 7 Low Atrium Low 0.43 0.32 0.25 8 Medium Atrium Low 0.30 0.35 0.36 9 High Atrium Low 0.19 0.27 0.54 10 Low Terrace Low 0.65 0.22 0.14 11 Medium Terrace Low 0.51 0.27 0.22 12 High Terrace Low 0.37 0.24 0.39 13 Low Tower High 0.30 0.28 0.42 14 Medium Tower High 0.18 0.27 0.54 15 High Tower High 0.10 0.19 0.71 16 Low Apartment High 0.44 0.27 0.30 17 Medium Apartment High 0.30 0.28 0.42 18 High Apartment High 0.18 0.21 0.61 19 Low Atrium High 0.33 0.36 0.31 20 Medium Atrium High 0.22 0.36 0.42 21 High Atrium High 0.13 0.27 0.60 22 Low Terrace High 0.55 0.27 0.19 23 Medium Terrace High 0.40 0.31 0.29 24 High Terrace High 0.27 0.26 0.47 > > # proportional odds model > house.cpr <- apply(house.pr, 1, cumsum) > logit <- function(x) log(x/(1-x)) > house.ld <- logit(house.cpr[2, ]) - logit(house.cpr[1, ]) > (ratio <- sort(drop(house.ld))) [1] 0.9357341 0.9854433 1.0573182 1.0680491 1.0772649 1.0803574 1.0824895 [8] 1.0998759 1.1199975 1.1554228 1.1768138 1.1866427 1.2091541 1.2435026 [15] 1.2724096 1.2750171 1.2849903 1.3062598 1.3123988 1.3904715 1.4540087 [22] 1.4947753 1.4967585 1.6068789 > mean(ratio) [1] 1.223835 > > (house.plr <- polr(Sat ~ Infl + Type + Cont, + data = housing, weights = Freq)) Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq) Coefficients: InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace 0.5663937 1.2888191 -0.5723501 -0.3661866 -1.0910149 ContHigh 0.3602841 Intercepts: Low|Medium Medium|High -0.4961353 0.6907083 Residual Deviance: 3479.149 AIC: 3495.149 > > house.pr1 <- predict(house.plr, expand.grid(hnames[-1]), type = "probs") > cbind(expand.grid(hnames[-1]), round(house.pr1, 2)) Infl Type Cont Low Medium High 1 Low Tower Low 0.38 0.29 0.33 2 Medium Tower Low 0.26 0.27 0.47 3 High Tower Low 0.14 0.21 0.65 4 Low Apartment Low 0.52 0.26 0.22 5 Medium Apartment Low 0.38 0.29 0.33 6 High Apartment Low 0.23 0.26 0.51 7 Low Atrium Low 0.47 0.27 0.26 8 Medium Atrium Low 0.33 0.29 0.38 9 High Atrium Low 0.19 0.25 0.56 10 Low Terrace Low 0.64 0.21 0.14 11 Medium Terrace Low 0.51 0.26 0.23 12 High Terrace Low 0.33 0.29 0.38 13 Low Tower High 0.30 0.28 0.42 14 Medium Tower High 0.19 0.25 0.56 15 High Tower High 0.10 0.17 0.72 16 Low Apartment High 0.43 0.28 0.29 17 Medium Apartment High 0.30 0.28 0.42 18 High Apartment High 0.17 0.23 0.60 19 Low Atrium High 0.38 0.29 0.33 20 Medium Atrium High 0.26 0.27 0.47 21 High Atrium High 0.14 0.21 0.64 22 Low Terrace High 0.56 0.25 0.19 23 Medium Terrace High 0.42 0.28 0.30 24 High Terrace High 0.26 0.27 0.47 > > Fr <- matrix(housing$Freq, ncol = 3, byrow = TRUE) > 2*sum(Fr*log(house.pr/house.pr1)) [1] 9.065433 > > house.plr2 <- stepAIC(house.plr, ~.^2) Start: AIC=3495.15 Sat ~ Infl + Type + Cont Df AIC + Infl:Type 6 3484.6 + Type:Cont 3 3492.5 3495.1 + Infl:Cont 2 3498.9 - Cont 1 3507.5 - Type 3 3545.1 - Infl 2 3599.4 Step: AIC=3484.64 Sat ~ Infl + Type + Cont + Infl:Type Df AIC + Type:Cont 3 3482.7 3484.6 + Infl:Cont 2 3488.5 - Infl:Type 6 3495.1 - Cont 1 3497.8 Step: AIC=3482.69 Sat ~ Infl + Type + Cont + Infl:Type + Type:Cont Df AIC 3482.7 - Type:Cont 3 3484.6 + Infl:Cont 2 3486.6 - Infl:Type 6 3492.5 > house.plr2$anova Stepwise Model Path Analysis of Deviance Table Initial Model: Sat ~ Infl + Type + Cont Final Model: Sat ~ Infl + Type + Cont + Infl:Type + Type:Cont Step Df Deviance Resid. Df Resid. Dev AIC 1 1673 3479.149 3495.149 2 + Infl:Type 6 22.509347 1667 3456.640 3484.640 3 + Type:Cont 3 7.945029 1664 3448.695 3482.695 > > > > base::options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() detaching ‘package:nnet’ > nameEx("huber") > ### * huber > > flush(stderr()); flush(stdout()) > > ### Name: huber > ### Title: Huber M-estimator of Location with MAD Scale > ### Aliases: huber > ### Keywords: robust > > ### ** Examples > > huber(chem) $mu [1] 3.206724 $s [1] 0.526323 > > > > cleanEx() > nameEx("hubers") > ### * hubers > > flush(stderr()); flush(stdout()) > > ### Name: hubers > ### Title: Huber Proposal 2 Robust Estimator of Location and/or Scale > ### Aliases: hubers > ### Keywords: robust > > ### ** Examples > > hubers(chem) $mu [1] 3.205498 $s [1] 0.673652 > hubers(chem, mu=3.68) $mu [1] 3.68 $s [1] 0.9409628 > > > > cleanEx() > nameEx("immer") > ### * immer > > flush(stderr()); flush(stdout()) > > ### Name: immer > ### Title: Yields from a Barley Field Trial > ### Aliases: immer > ### Keywords: datasets > > ### ** Examples > > immer.aov <- aov(cbind(Y1,Y2) ~ Loc + Var, data = immer) > summary(immer.aov) Response Y1 : Df Sum Sq Mean Sq F value Pr(>F) Loc 5 17829.8 3566.0 21.8923 1.751e-07 *** Var 4 2756.6 689.2 4.2309 0.01214 * Residuals 20 3257.7 162.9 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Response Y2 : Df Sum Sq Mean Sq F value Pr(>F) Loc 5 10285.0 2056.99 10.3901 5.049e-05 *** Var 4 2845.2 711.29 3.5928 0.02306 * Residuals 20 3959.5 197.98 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > immer.aov <- aov((Y1+Y2)/2 ~ Var + Loc, data = immer) > summary(immer.aov) Df Sum Sq Mean Sq F value Pr(>F) Var 4 2655 663.7 5.989 0.00245 ** Loc 5 10610 2122.1 19.148 5.21e-07 *** Residuals 20 2217 110.8 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > model.tables(immer.aov, type = "means", se = TRUE, cterms = "Var") Tables of means Grand mean 101.09 Var Var M P S T V 94.39 102.54 91.13 118.20 99.18 Standard errors for differences of means Var 6.078 replic. 6 > > > > cleanEx() > nameEx("isoMDS") > ### * isoMDS > > flush(stderr()); flush(stdout()) > > ### Name: isoMDS > ### Title: Kruskal's Non-metric Multidimensional Scaling > ### Aliases: isoMDS Shepard > ### Keywords: multivariate > > ### ** Examples > > swiss.x <- as.matrix(swiss[, -1]) > swiss.dist <- dist(swiss.x) > swiss.mds <- isoMDS(swiss.dist) initial value 2.979731 iter 5 value 2.431486 iter 10 value 2.343353 final value 2.338839 converged > plot(swiss.mds$points, type = "n") > text(swiss.mds$points, labels = as.character(1:nrow(swiss.x))) > swiss.sh <- Shepard(swiss.dist, swiss.mds$points) > plot(swiss.sh, pch = ".") > lines(swiss.sh$x, swiss.sh$yf, type = "S") > > > > cleanEx() > nameEx("kde2d") > ### * kde2d > > flush(stderr()); flush(stdout()) > > ### Name: kde2d > ### Title: Two-Dimensional Kernel Density Estimation > ### Aliases: kde2d > ### Keywords: dplot > > ### ** Examples > > attach(geyser) > plot(duration, waiting, xlim = c(0.5,6), ylim = c(40,100)) > f1 <- kde2d(duration, waiting, n = 50, lims = c(0.5, 6, 40, 100)) > image(f1, zlim = c(0, 0.05)) > f2 <- kde2d(duration, waiting, n = 50, lims = c(0.5, 6, 40, 100), + h = c(width.SJ(duration), width.SJ(waiting)) ) > image(f2, zlim = c(0, 0.05)) > persp(f2, phi = 30, theta = 20, d = 5) > > plot(duration[-272], duration[-1], xlim = c(0.5, 6), + ylim = c(1, 6),xlab = "previous duration", ylab = "duration") > f1 <- kde2d(duration[-272], duration[-1], + h = rep(1.5, 2), n = 50, lims = c(0.5, 6, 0.5, 6)) > contour(f1, xlab = "previous duration", + ylab = "duration", levels = c(0.05, 0.1, 0.2, 0.4) ) > f1 <- kde2d(duration[-272], duration[-1], + h = rep(0.6, 2), n = 50, lims = c(0.5, 6, 0.5, 6)) > contour(f1, xlab = "previous duration", + ylab = "duration", levels = c(0.05, 0.1, 0.2, 0.4) ) > f1 <- kde2d(duration[-272], duration[-1], + h = rep(0.4, 2), n = 50, lims = c(0.5, 6, 0.5, 6)) > contour(f1, xlab = "previous duration", + ylab = "duration", levels = c(0.05, 0.1, 0.2, 0.4) ) > detach("geyser") > > > > cleanEx() > nameEx("lda") > ### * lda > > flush(stderr()); flush(stdout()) > > ### Name: lda > ### Title: Linear Discriminant Analysis > ### Aliases: lda lda.default lda.data.frame lda.formula lda.matrix > ### model.frame.lda print.lda coef.lda > ### Keywords: multivariate > > ### ** Examples > > Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + Sp = rep(c("s","c","v"), rep(50,3))) > train <- sample(1:150, 75) > table(Iris$Sp[train]) c s v 20 28 27 > ## your answer may differ > ## c s v > ## 22 23 30 > z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) > predict(z, Iris[-train, ])$class [1] s s s s s s s s s s s s s s s s s s s s s s c c c c c c c c c c c c c c c v [39] c c c c c c c c c c c c c c v v v v v v v v v v v v v v c v v v v v v v v Levels: c s v > ## [1] s s s s s s s s s s s s s s s s s s s s s s s s s s s c c c > ## [31] c c c c c c c v c c c c v c c c c c c c c c c c c v v v v v > ## [61] v v v v v v v v v v v v v v v > (z1 <- update(z, . ~ . - Petal.W.)) Call: lda(Sp ~ Sepal.L. + Sepal.W. + Petal.L., data = Iris, prior = c(1, 1, 1)/3, subset = train) Prior probabilities of groups: c s v 0.3333333 0.3333333 0.3333333 Group means: Sepal.L. Sepal.W. Petal.L. c 5.975000 2.810000 4.395000 s 4.978571 3.432143 1.460714 v 6.748148 2.988889 5.637037 Coefficients of linear discriminants: LD1 LD2 Sepal.L. 1.1643015 0.68235619 Sepal.W. 0.7945307 2.23093702 Petal.L. -3.0421425 0.01236265 Proportion of trace: LD1 LD2 0.9929 0.0071 > > > > cleanEx() > nameEx("leuk") > ### * leuk > > flush(stderr()); flush(stdout()) > > ### Name: leuk > ### Title: Survival Times and White Blood Counts for Leukaemia Patients > ### Aliases: leuk > ### Keywords: datasets > > ### ** Examples > > library(survival) > plot(survfit(Surv(time) ~ ag, data = leuk), lty = 2:3, col = 2:3) > > # now Cox models > leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), leuk) > summary(leuk.cox) Call: coxph(formula = Surv(time) ~ ag + log(wbc), data = leuk) n= 33, number of events= 33 coef exp(coef) se(coef) z Pr(>|z|) agpresent -1.0691 0.3433 0.4293 -2.490 0.01276 * log(wbc) 0.3677 1.4444 0.1360 2.703 0.00687 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 agpresent 0.3433 2.9126 0.148 0.7964 log(wbc) 1.4444 0.6923 1.106 1.8857 Concordance= 0.726 (se = 0.047 ) Likelihood ratio test= 15.64 on 2 df, p=4e-04 Wald test = 15.06 on 2 df, p=5e-04 Score (logrank) test = 16.49 on 2 df, p=3e-04 > > > > cleanEx() detaching ‘package:survival’ > nameEx("lm.ridge") > ### * lm.ridge > > flush(stderr()); flush(stdout()) > > ### Name: lm.ridge > ### Title: Ridge Regression > ### Aliases: lm.ridge plot.ridgelm print.ridgelm select select.ridgelm > ### Keywords: models > > ### ** Examples > > longley # not the same as the S-PLUS dataset GNP.deflator GNP Unemployed Armed.Forces Population Year Employed 1947 83.0 234.289 235.6 159.0 107.608 1947 60.323 1948 88.5 259.426 232.5 145.6 108.632 1948 61.122 1949 88.2 258.054 368.2 161.6 109.773 1949 60.171 1950 89.5 284.599 335.1 165.0 110.929 1950 61.187 1951 96.2 328.975 209.9 309.9 112.075 1951 63.221 1952 98.1 346.999 193.2 359.4 113.270 1952 63.639 1953 99.0 365.385 187.0 354.7 115.094 1953 64.989 1954 100.0 363.112 357.8 335.0 116.219 1954 63.761 1955 101.2 397.469 290.4 304.8 117.388 1955 66.019 1956 104.6 419.180 282.2 285.7 118.734 1956 67.857 1957 108.4 442.769 293.6 279.8 120.445 1957 68.169 1958 110.8 444.546 468.1 263.7 121.950 1958 66.513 1959 112.6 482.704 381.3 255.2 123.366 1959 68.655 1960 114.2 502.601 393.1 251.4 125.368 1960 69.564 1961 115.7 518.173 480.6 257.2 127.852 1961 69.331 1962 116.9 554.894 400.7 282.7 130.081 1962 70.551 > names(longley)[1] <- "y" > lm.ridge(y ~ ., longley) GNP Unemployed Armed.Forces Population 2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984 Year Employed -1.41879853 0.23128785 > plot(lm.ridge(y ~ ., longley, + lambda = seq(0,0.1,0.001))) > select(lm.ridge(y ~ ., longley, + lambda = seq(0,0.1,0.0001))) modified HKB estimator is 0.006836982 modified L-W estimator is 0.05267247 smallest value of GCV at 0.0057 > > > > cleanEx() > nameEx("loglm") > ### * loglm > > flush(stderr()); flush(stdout()) > > ### Name: loglm > ### Title: Fit Log-Linear Models by Iterative Proportional Scaling > ### Aliases: loglm > ### Keywords: category models > > ### ** Examples > > # The data frames Cars93, minn38 and quine are available > # in the MASS package. > > # Case 1: frequencies specified as an array. > sapply(minn38, function(x) length(levels(x))) hs phs fol sex f 3 4 7 2 0 > ## hs phs fol sex f > ## 3 4 7 2 0 > ##minn38a <- array(0, c(3,4,7,2), lapply(minn38[, -5], levels)) > ##minn38a[data.matrix(minn38[,-5])] <- minn38$f > > ## or more simply > minn38a <- xtabs(f ~ ., minn38) > > fm <- loglm(~ 1 + 2 + 3 + 4, minn38a) # numerals as names. > deviance(fm) [1] 3711.914 > ## [1] 3711.9 > fm1 <- update(fm, .~.^2) > fm2 <- update(fm, .~.^3, print = TRUE) 5 iterations: deviation 0.07512432 > ## 5 iterations: deviation 0.075 > anova(fm, fm1, fm2) LR tests for hierarchical log-linear models Model 1: ~1 + 2 + 3 + 4 Model 2: . ~ `1` + `2` + `3` + `4` + `1`:`2` + `1`:`3` + `1`:`4` + `2`:`3` + `2`:`4` + `3`:`4` Model 3: . ~ `1` + `2` + `3` + `4` + `1`:`2` + `1`:`3` + `1`:`4` + `2`:`3` + `2`:`4` + `3`:`4` + `1`:`2`:`3` + `1`:`2`:`4` + `1`:`3`:`4` + `2`:`3`:`4` Deviance df Delta(Dev) Delta(df) P(> Delta(Dev) Model 1 3711.91367 155 Model 2 220.04285 108 3491.87082 47 0.00000 Model 3 47.74492 36 172.29794 72 0.00000 Saturated 0.00000 0 47.74492 36 0.09114 > > # Case 1. An array generated with xtabs. > > loglm(~ Type + Origin, xtabs(~ Type + Origin, Cars93)) Call: loglm(formula = ~Type + Origin, data = xtabs(~Type + Origin, Cars93)) Statistics: X^2 df P(> X^2) Likelihood Ratio 18.36179 5 0.00252554 Pearson 14.07985 5 0.01511005 > > # Case 2. Frequencies given as a vector in a data frame > names(quine) [1] "Eth" "Sex" "Age" "Lrn" "Days" > ## [1] "Eth" "Sex" "Age" "Lrn" "Days" > fm <- loglm(Days ~ .^2, quine) > gm <- glm(Days ~ .^2, poisson, quine) # check glm. > c(deviance(fm), deviance(gm)) # deviances agree [1] 1368.669 1368.669 > ## [1] 1368.7 1368.7 > c(fm$df, gm$df) # resid df do not! [1] 127 > c(fm$df, gm$df.residual) # resid df do not! [1] 127 128 > ## [1] 127 128 > # The loglm residual degrees of freedom is wrong because of > # a non-detectable redundancy in the model matrix. > > > > cleanEx() > nameEx("logtrans") > ### * logtrans > > flush(stderr()); flush(stdout()) > > ### Name: logtrans > ### Title: Estimate log Transformation Parameter > ### Aliases: logtrans logtrans.formula logtrans.lm logtrans.default > ### Keywords: regression models hplot > > ### ** Examples > > logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine, + alpha = seq(0.75, 6.5, length.out = 20)) > > > > cleanEx() > nameEx("lqs") > ### * lqs > > flush(stderr()); flush(stdout()) > > ### Name: lqs > ### Title: Resistant Regression > ### Aliases: lqs lqs.formula lqs.default lmsreg ltsreg > ### Keywords: models robust > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > set.seed(123) # make reproducible > lqs(stack.loss ~ ., data = stackloss) Call: lqs.formula(formula = stack.loss ~ ., data = stackloss) Coefficients: (Intercept) Air.Flow Water.Temp Acid.Conc. -3.631e+01 7.292e-01 4.167e-01 -1.659e-16 Scale estimates 0.9149 1.0148 > lqs(stack.loss ~ ., data = stackloss, method = "S", nsamp = "exact") Call: lqs.formula(formula = stack.loss ~ ., data = stackloss, nsamp = "exact", method = "S") Coefficients: (Intercept) Air.Flow Water.Temp Acid.Conc. -35.37611 0.82522 0.44248 -0.07965 Scale estimates 1.912 > ## IGNORE_RDIFF_END > > > > cleanEx() > nameEx("mca") > ### * mca > > flush(stderr()); flush(stdout()) > > ### Name: mca > ### Title: Multiple Correspondence Analysis > ### Aliases: mca print.mca > ### Keywords: category multivariate > > ### ** Examples > > farms.mca <- mca(farms, abbrev=TRUE) > farms.mca Call: mca(df = farms, abbrev = TRUE) Multiple correspondence analysis of 20 cases of 4 factors Correlations 0.806 0.745 cumulative % explained 26.87 51.71 > plot(farms.mca) > > > > cleanEx() > nameEx("menarche") > ### * menarche > > flush(stderr()); flush(stdout()) > > ### Name: menarche > ### Title: Age of Menarche in Warsaw > ### Aliases: menarche > ### Keywords: datasets > > ### ** Examples > > mprob <- glm(cbind(Menarche, Total - Menarche) ~ Age, + binomial(link = probit), data = menarche) > > > > cleanEx() > nameEx("motors") > ### * motors > > flush(stderr()); flush(stdout()) > > ### Name: motors > ### Title: Accelerated Life Testing of Motorettes > ### Aliases: motors > ### Keywords: datasets > > ### ** Examples > > library(survival) > plot(survfit(Surv(time, cens) ~ factor(temp), motors), conf.int = FALSE) > # fit Weibull model > motor.wei <- survreg(Surv(time, cens) ~ temp, motors) > summary(motor.wei) Call: survreg(formula = Surv(time, cens) ~ temp, data = motors) Value Std. Error z p (Intercept) 16.31852 0.62296 26.2 < 2e-16 temp -0.04531 0.00319 -14.2 < 2e-16 Log(scale) -1.09564 0.21480 -5.1 3.4e-07 Scale= 0.334 Weibull distribution Loglik(model)= -147.4 Loglik(intercept only)= -169.5 Chisq= 44.32 on 1 degrees of freedom, p= 2.8e-11 Number of Newton-Raphson Iterations: 7 n= 40 > # and predict at 130C > unlist(predict(motor.wei, data.frame(temp=130), se.fit = TRUE)) fit.1 se.fit.1 33813.06 7506.36 > > motor.cox <- coxph(Surv(time, cens) ~ temp, motors) > summary(motor.cox) Call: coxph(formula = Surv(time, cens) ~ temp, data = motors) n= 40, number of events= 17 coef exp(coef) se(coef) z Pr(>|z|) temp 0.09185 1.09620 0.02736 3.358 0.000786 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 temp 1.096 0.9122 1.039 1.157 Concordance= 0.84 (se = 0.035 ) Likelihood ratio test= 25.56 on 1 df, p=4e-07 Wald test = 11.27 on 1 df, p=8e-04 Score (logrank) test = 22.73 on 1 df, p=2e-06 > # predict at temperature 200 > plot(survfit(motor.cox, newdata = data.frame(temp=200), + conf.type = "log-log")) > summary( survfit(motor.cox, newdata = data.frame(temp=130)) ) Call: survfit(formula = motor.cox, newdata = data.frame(temp = 130)) time n.risk n.event survival std.err lower 95% CI upper 95% CI 408 40 4 1.000 0.000254 0.999 1 504 36 3 1.000 0.000498 0.999 1 1344 28 2 0.999 0.001910 0.995 1 1440 26 1 0.998 0.002697 0.993 1 1764 20 1 0.996 0.005325 0.986 1 2772 19 1 0.994 0.007920 0.978 1 3444 18 1 0.991 0.010673 0.971 1 3542 17 1 0.988 0.013667 0.962 1 3780 16 1 0.985 0.016976 0.952 1 4860 15 1 0.981 0.020692 0.941 1 5196 14 1 0.977 0.024941 0.929 1 > > > > cleanEx() detaching ‘package:survival’ > nameEx("muscle") > ### * muscle > > flush(stderr()); flush(stdout()) > > ### Name: muscle > ### Title: Effect of Calcium Chloride on Muscle Contraction in Rat Hearts > ### Aliases: muscle > ### Keywords: datasets > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > A <- model.matrix(~ Strip - 1, data=muscle) > rats.nls1 <- nls(log(Length) ~ cbind(A, rho^Conc), + data = muscle, start = c(rho=0.1), algorithm="plinear") > (B <- coef(rats.nls1)) rho .lin.StripS01 .lin.StripS02 .lin.StripS03 .lin.StripS04 0.07776401 3.08304824 3.30137838 3.44562531 2.80464434 .lin.StripS05 .lin.StripS06 .lin.StripS07 .lin.StripS08 .lin.StripS09 2.60835015 3.03357725 3.52301734 3.38711844 3.46709396 .lin.StripS10 .lin.StripS11 .lin.StripS12 .lin.StripS13 .lin.StripS14 3.81438456 3.73878664 3.51332581 3.39741115 3.47088608 .lin.StripS15 .lin.StripS16 .lin.StripS17 .lin.StripS18 .lin.StripS19 3.72895847 3.31863862 3.37938673 2.96452195 3.58468686 .lin.StripS20 .lin.StripS21 .lin22 3.39628029 3.36998872 -2.96015460 > > st <- list(alpha = B[2:22], beta = B[23], rho = B[1]) > (rats.nls2 <- nls(log(Length) ~ alpha[Strip] + beta*rho^Conc, + data = muscle, start = st)) Nonlinear regression model model: log(Length) ~ alpha[Strip] + beta * rho^Conc data: muscle alpha..lin.StripS01 alpha..lin.StripS02 alpha..lin.StripS03 alpha..lin.StripS04 3.08305 3.30138 3.44563 2.80464 alpha..lin.StripS05 alpha..lin.StripS06 alpha..lin.StripS07 alpha..lin.StripS08 2.60835 3.03358 3.52302 3.38712 alpha..lin.StripS09 alpha..lin.StripS10 alpha..lin.StripS11 alpha..lin.StripS12 3.46709 3.81438 3.73879 3.51333 alpha..lin.StripS13 alpha..lin.StripS14 alpha..lin.StripS15 alpha..lin.StripS16 3.39741 3.47089 3.72896 3.31864 alpha..lin.StripS17 alpha..lin.StripS18 alpha..lin.StripS19 alpha..lin.StripS20 3.37939 2.96452 3.58469 3.39628 alpha..lin.StripS21 beta..lin22 rho.rho 3.36999 -2.96015 0.07776 residual sum-of-squares: 1.045 Number of iterations to convergence: 0 Achieved convergence tolerance: 4.918e-06 > ## IGNORE_RDIFF_END > > Muscle <- with(muscle, { + Muscle <- expand.grid(Conc = sort(unique(Conc)), Strip = levels(Strip)) + Muscle$Yhat <- predict(rats.nls2, Muscle) + Muscle <- cbind(Muscle, logLength = rep(as.numeric(NA), 126)) + ind <- match(paste(Strip, Conc), + paste(Muscle$Strip, Muscle$Conc)) + Muscle$logLength[ind] <- log(Length) + Muscle}) > > lattice::xyplot(Yhat ~ Conc | Strip, Muscle, as.table = TRUE, + ylim = range(c(Muscle$Yhat, Muscle$logLength), na.rm = TRUE), + subscripts = TRUE, xlab = "Calcium Chloride concentration (mM)", + ylab = "log(Length in mm)", panel = + function(x, y, subscripts, ...) { + panel.xyplot(x, Muscle$logLength[subscripts], ...) + llines(spline(x, y)) + }) > > > > cleanEx() > nameEx("mvrnorm") > ### * mvrnorm > > flush(stderr()); flush(stdout()) > > ### Name: mvrnorm > ### Title: Simulate from a Multivariate Normal Distribution > ### Aliases: mvrnorm > ### Keywords: distribution multivariate > > ### ** Examples > > Sigma <- matrix(c(10,3,3,2),2,2) > Sigma [,1] [,2] [1,] 10 3 [2,] 3 2 > var(mvrnorm(n = 1000, rep(0, 2), Sigma)) [,1] [,2] [1,] 10.697849 3.228279 [2,] 3.228279 2.165271 > var(mvrnorm(n = 1000, rep(0, 2), Sigma, empirical = TRUE)) [,1] [,2] [1,] 10 3 [2,] 3 2 > > > > cleanEx() > nameEx("negative.binomial") > ### * negative.binomial > > flush(stderr()); flush(stdout()) > > ### Name: negative.binomial > ### Title: Family function for Negative Binomial GLMs > ### Aliases: negative.binomial > ### Keywords: regression models > > ### ** Examples > > # Fitting a Negative Binomial model to the quine data > # with theta = 2 assumed known. > # > glm(Days ~ .^4, family = negative.binomial(2), data = quine) Call: glm(formula = Days ~ .^4, family = negative.binomial(2), data = quine) Coefficients: (Intercept) EthN SexM 3.0564 -0.1386 -0.4914 AgeF1 AgeF2 AgeF3 -0.6227 -2.3632 -0.3784 LrnSL EthN:SexM EthN:AgeF1 -1.9577 -0.7524 0.1029 EthN:AgeF2 EthN:AgeF3 EthN:LrnSL -0.5546 0.0633 2.2588 SexM:AgeF1 SexM:AgeF2 SexM:AgeF3 0.4092 3.1098 1.1145 SexM:LrnSL AgeF1:LrnSL AgeF2:LrnSL 1.5900 2.6421 4.8585 AgeF3:LrnSL EthN:SexM:AgeF1 EthN:SexM:AgeF2 NA -0.3105 0.3469 EthN:SexM:AgeF3 EthN:SexM:LrnSL EthN:AgeF1:LrnSL 0.8329 -0.1639 -3.5493 EthN:AgeF2:LrnSL EthN:AgeF3:LrnSL SexM:AgeF1:LrnSL -3.3315 NA -2.4285 SexM:AgeF2:LrnSL SexM:AgeF3:LrnSL EthN:SexM:AgeF1:LrnSL -4.1914 NA 2.1711 EthN:SexM:AgeF2:LrnSL EthN:SexM:AgeF3:LrnSL 2.1029 NA Degrees of Freedom: 145 Total (i.e. Null); 118 Residual Null Deviance: 280.2 Residual Deviance: 172 AIC: 1095 > > > > cleanEx() > nameEx("nlschools") > ### * nlschools > > flush(stderr()); flush(stdout()) > > ### Name: nlschools > ### Title: Eighth-Grade Pupils in the Netherlands > ### Aliases: nlschools > ### Keywords: datasets > > ### ** Examples > > ## Don't show: > op <- options(digits=5) > ## End(Don't show) > nl1 <- within(nlschools, { + IQave <- tapply(IQ, class, mean)[as.character(class)] + IQ <- IQ - IQave + }) > cen <- c("IQ", "IQave", "SES") > nl1[cen] <- scale(nl1[cen], center = TRUE, scale = FALSE) > > nl.lme <- nlme::lme(lang ~ IQ*COMB + IQave + SES, + random = ~ IQ | class, data = nl1) > ## IGNORE_RDIFF_BEGIN > summary(nl.lme) Linear mixed-effects model fit by REML Data: nl1 AIC BIC logLik 15120 15178 -7550.2 Random effects: Formula: ~IQ | class Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 2.78707 (Intr) IQ 0.48424 -0.516 Residual 6.24839 Fixed effects: lang ~ IQ * COMB + IQave + SES Value Std.Error DF t-value p-value (Intercept) 41.370 0.35364 2151 116.985 0.0000 IQ 2.124 0.10070 2151 21.088 0.0000 COMB1 -1.672 0.58719 130 -2.847 0.0051 IQave 3.248 0.30021 130 10.818 0.0000 SES 0.157 0.01465 2151 10.697 0.0000 IQ:COMB1 0.431 0.18594 2151 2.317 0.0206 Correlation: (Intr) IQ COMB1 IQave SES IQ -0.257 COMB1 -0.609 0.155 IQave -0.049 0.041 0.171 SES 0.010 -0.190 -0.001 -0.168 IQ:COMB1 0.139 -0.522 -0.206 -0.016 -0.003 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.059387 -0.631084 0.065519 0.717864 2.794540 Number of Observations: 2287 Number of Groups: 133 > ## IGNORE_RDIFF_END > ## Don't show: > options(op) > ## End(Don't show) > > > > cleanEx() > nameEx("npk") > ### * npk > > flush(stderr()); flush(stdout()) > > ### Name: npk > ### Title: Classical N, P, K Factorial Experiment > ### Aliases: npk > ### Keywords: datasets > > ### ** Examples > > options(contrasts = c("contr.sum", "contr.poly")) > npk.aov <- aov(yield ~ block + N*P*K, npk) > ## IGNORE_RDIFF_BEGIN > npk.aov Call: aov(formula = yield ~ block + N * P * K, data = npk) Terms: block N P K N:P N:K P:K Sum of Squares 343.2950 189.2817 8.4017 95.2017 21.2817 33.1350 0.4817 Deg. of Freedom 5 1 1 1 1 1 1 Residuals Sum of Squares 185.2867 Deg. of Freedom 12 Residual standard error: 3.929447 1 out of 13 effects not estimable Estimated effects may be unbalanced > summary(npk.aov) Df Sum Sq Mean Sq F value Pr(>F) block 5 343.3 68.66 4.447 0.01594 * N 1 189.3 189.28 12.259 0.00437 ** P 1 8.4 8.40 0.544 0.47490 K 1 95.2 95.20 6.166 0.02880 * N:P 1 21.3 21.28 1.378 0.26317 N:K 1 33.1 33.14 2.146 0.16865 P:K 1 0.5 0.48 0.031 0.86275 Residuals 12 185.3 15.44 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > alias(npk.aov) Model : yield ~ block + N * P * K Complete : (Intercept) block1 block2 block3 block4 block5 N1 P1 K1 N1:P1 N1:K1 N1:P1:K1 0 1 -1 -1 -1 1 0 0 0 0 0 P1:K1 N1:P1:K1 0 > coef(npk.aov) (Intercept) block1 block2 block3 block4 block5 54.8750000 -0.8500000 2.5750000 5.9000000 -4.7500000 -4.3500000 N1 P1 K1 N1:P1 N1:K1 P1:K1 -2.8083333 0.5916667 1.9916667 -0.9416667 -1.1750000 0.1416667 > options(contrasts = c("contr.treatment", "contr.poly")) > npk.aov1 <- aov(yield ~ block + N + K, data = npk) > summary.lm(npk.aov1) Call: aov(formula = yield ~ block + N + K, data = npk) Residuals: Min 1Q Median 3Q Max -6.4083 -2.1438 0.2042 2.3292 7.0750 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 53.208 2.276 23.381 8.5e-14 *** block2 3.425 2.787 1.229 0.23690 block3 6.750 2.787 2.422 0.02769 * block4 -3.900 2.787 -1.399 0.18082 block5 -3.500 2.787 -1.256 0.22723 block6 2.325 2.787 0.834 0.41646 N1 5.617 1.609 3.490 0.00302 ** K1 -3.983 1.609 -2.475 0.02487 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.942 on 16 degrees of freedom Multiple R-squared: 0.7163, Adjusted R-squared: 0.5922 F-statistic: 5.772 on 7 and 16 DF, p-value: 0.001805 > se.contrast(npk.aov1, list(N=="0", N=="1"), data = npk) [1] 1.609175 > model.tables(npk.aov1, type = "means", se = TRUE) Tables of means Grand mean 54.875 block block 1 2 3 4 5 6 54.03 57.45 60.78 50.12 50.52 56.35 N N 0 1 52.07 57.68 K K 0 1 56.87 52.88 Standard errors for differences of means block N K 2.787 1.609 1.609 replic. 4 12 12 > ## IGNORE_RDIFF_END > > > base::options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("oats") > ### * oats > > flush(stderr()); flush(stdout()) > > ### Name: oats > ### Title: Data from an Oats Field Trial > ### Aliases: oats > ### Keywords: datasets > > ### ** Examples > > oats$Nf <- ordered(oats$N, levels = sort(levels(oats$N))) > oats.aov <- aov(Y ~ Nf*V + Error(B/V), data = oats, qr = TRUE) > ## IGNORE_RDIFF_BEGIN > summary(oats.aov) Error: B Df Sum Sq Mean Sq F value Pr(>F) Residuals 5 15875 3175 Error: B:V Df Sum Sq Mean Sq F value Pr(>F) V 2 1786 893.2 1.485 0.272 Residuals 10 6013 601.3 Error: Within Df Sum Sq Mean Sq F value Pr(>F) Nf 3 20021 6674 37.686 2.46e-12 *** Nf:V 6 322 54 0.303 0.932 Residuals 45 7969 177 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(oats.aov, split = list(Nf=list(L=1, Dev=2:3))) Error: B Df Sum Sq Mean Sq F value Pr(>F) Residuals 5 15875 3175 Error: B:V Df Sum Sq Mean Sq F value Pr(>F) V 2 1786 893.2 1.485 0.272 Residuals 10 6013 601.3 Error: Within Df Sum Sq Mean Sq F value Pr(>F) Nf 3 20021 6674 37.686 2.46e-12 *** Nf: L 1 19536 19536 110.323 1.09e-13 *** Nf: Dev 2 484 242 1.367 0.265 Nf:V 6 322 54 0.303 0.932 Nf:V: L 2 168 84 0.475 0.625 Nf:V: Dev 4 153 38 0.217 0.928 Residuals 45 7969 177 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## IGNORE_RDIFF_END > par(mfrow = c(1,2), pty = "s") > plot(fitted(oats.aov[[4]]), studres(oats.aov[[4]])) > abline(h = 0, lty = 2) > oats.pr <- proj(oats.aov) > qqnorm(oats.pr[[4]][,"Residuals"], ylab = "Stratum 4 residuals") > qqline(oats.pr[[4]][,"Residuals"]) > > par(mfrow = c(1,1), pty = "m") > oats.aov2 <- aov(Y ~ N + V + Error(B/V), data = oats, qr = TRUE) > model.tables(oats.aov2, type = "means", se = TRUE) Warning in model.tables.aovlist(oats.aov2, type = "means", se = TRUE) : SEs for type 'means' are not yet implemented Tables of means Grand mean 103.9722 N N 0.0cwt 0.2cwt 0.4cwt 0.6cwt 79.39 98.89 114.22 123.39 V V Golden.rain Marvellous Victory 104.50 109.79 97.63 > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("parcoord") > ### * parcoord > > flush(stderr()); flush(stdout()) > > ### Name: parcoord > ### Title: Parallel Coordinates Plot > ### Aliases: parcoord > ### Keywords: hplot > > ### ** Examples > > parcoord(state.x77[, c(7, 4, 6, 2, 5, 3)]) > > ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) > parcoord(log(ir)[, c(3, 4, 2, 1)], col = 1 + (0:149)%/%50) > > > > cleanEx() > nameEx("petrol") > ### * petrol > > flush(stderr()); flush(stdout()) > > ### Name: petrol > ### Title: N. L. Prater's Petrol Refinery Data > ### Aliases: petrol > ### Keywords: datasets > > ### ** Examples > > library(nlme) > Petrol <- petrol > Petrol[, 2:5] <- scale(as.matrix(Petrol[, 2:5]), scale = FALSE) > pet3.lme <- lme(Y ~ SG + VP + V10 + EP, + random = ~ 1 | No, data = Petrol) > pet3.lme <- update(pet3.lme, method = "ML") > pet4.lme <- update(pet3.lme, fixed. = Y ~ V10 + EP) > anova(pet4.lme, pet3.lme) Model df AIC BIC logLik Test L.Ratio p-value pet4.lme 1 5 149.6119 156.9406 -69.80594 pet3.lme 2 7 149.3833 159.6435 -67.69166 1 vs 2 4.22855 0.1207 > > > > cleanEx() detaching ‘package:nlme’ > nameEx("plot.mca") > ### * plot.mca > > flush(stderr()); flush(stdout()) > > ### Name: plot.mca > ### Title: Plot Method for Objects of Class 'mca' > ### Aliases: plot.mca > ### Keywords: hplot multivariate > > ### ** Examples > > plot(mca(farms, abbrev = TRUE)) > > > > cleanEx() > nameEx("polr") > ### * polr > > flush(stderr()); flush(stdout()) > > ### Name: polr > ### Title: Ordered Logistic or Probit Regression > ### Aliases: polr > ### Keywords: models > > ### ** Examples > > options(contrasts = c("contr.treatment", "contr.poly")) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.plr Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq) Coefficients: InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace 0.5663937 1.2888191 -0.5723501 -0.3661866 -1.0910149 ContHigh 0.3602841 Intercepts: Low|Medium Medium|High -0.4961353 0.6907083 Residual Deviance: 3479.149 AIC: 3495.149 > summary(house.plr, digits = 3) Re-fitting to get Hessian Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq) Coefficients: Value Std. Error t value InflMedium 0.566 0.1047 5.41 InflHigh 1.289 0.1272 10.14 TypeApartment -0.572 0.1192 -4.80 TypeAtrium -0.366 0.1552 -2.36 TypeTerrace -1.091 0.1515 -7.20 ContHigh 0.360 0.0955 3.77 Intercepts: Value Std. Error t value Low|Medium -0.496 0.125 -3.974 Medium|High 0.691 0.125 5.505 Residual Deviance: 3479.149 AIC: 3495.149 > ## slightly worse fit from > summary(update(house.plr, method = "probit", Hess = TRUE), digits = 3) Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq, Hess = TRUE, method = "probit") Coefficients: Value Std. Error t value InflMedium 0.346 0.0641 5.40 InflHigh 0.783 0.0764 10.24 TypeApartment -0.348 0.0723 -4.81 TypeAtrium -0.218 0.0948 -2.30 TypeTerrace -0.664 0.0918 -7.24 ContHigh 0.222 0.0581 3.83 Intercepts: Value Std. Error t value Low|Medium -0.300 0.076 -3.937 Medium|High 0.427 0.076 5.585 Residual Deviance: 3479.689 AIC: 3495.689 > ## although it is not really appropriate, can fit > summary(update(house.plr, method = "loglog", Hess = TRUE), digits = 3) Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq, Hess = TRUE, method = "loglog") Coefficients: Value Std. Error t value InflMedium 0.367 0.0727 5.05 InflHigh 0.790 0.0806 9.81 TypeApartment -0.349 0.0757 -4.61 TypeAtrium -0.196 0.0988 -1.98 TypeTerrace -0.698 0.1043 -6.69 ContHigh 0.268 0.0636 4.21 Intercepts: Value Std. Error t value Low|Medium 0.086 0.083 1.038 Medium|High 0.892 0.087 10.223 Residual Deviance: 3491.41 AIC: 3507.41 > summary(update(house.plr, method = "cloglog", Hess = TRUE), digits = 3) Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq, Hess = TRUE, method = "cloglog") Coefficients: Value Std. Error t value InflMedium 0.382 0.0703 5.44 InflHigh 0.915 0.0926 9.89 TypeApartment -0.407 0.0861 -4.73 TypeAtrium -0.281 0.1111 -2.52 TypeTerrace -0.742 0.1013 -7.33 ContHigh 0.209 0.0651 3.21 Intercepts: Value Std. Error t value Low|Medium -0.796 0.090 -8.881 Medium|High 0.055 0.086 0.647 Residual Deviance: 3484.053 AIC: 3500.053 > > predict(house.plr, housing, type = "p") Low Medium High 1 0.3784493 0.2876752 0.3338755 2 0.3784493 0.2876752 0.3338755 3 0.3784493 0.2876752 0.3338755 4 0.2568264 0.2742122 0.4689613 5 0.2568264 0.2742122 0.4689613 6 0.2568264 0.2742122 0.4689613 7 0.1436924 0.2110836 0.6452240 8 0.1436924 0.2110836 0.6452240 9 0.1436924 0.2110836 0.6452240 10 0.5190445 0.2605077 0.2204478 11 0.5190445 0.2605077 0.2204478 12 0.5190445 0.2605077 0.2204478 13 0.3798514 0.2875965 0.3325521 14 0.3798514 0.2875965 0.3325521 15 0.3798514 0.2875965 0.3325521 16 0.2292406 0.2643196 0.5064398 17 0.2292406 0.2643196 0.5064398 18 0.2292406 0.2643196 0.5064398 19 0.4675584 0.2745383 0.2579033 20 0.4675584 0.2745383 0.2579033 21 0.4675584 0.2745383 0.2579033 22 0.3326236 0.2876008 0.3797755 23 0.3326236 0.2876008 0.3797755 24 0.3326236 0.2876008 0.3797755 25 0.1948548 0.2474226 0.5577225 26 0.1948548 0.2474226 0.5577225 27 0.1948548 0.2474226 0.5577225 28 0.6444840 0.2114256 0.1440905 29 0.6444840 0.2114256 0.1440905 30 0.6444840 0.2114256 0.1440905 31 0.5071210 0.2641196 0.2287594 32 0.5071210 0.2641196 0.2287594 33 0.5071210 0.2641196 0.2287594 34 0.3331573 0.2876330 0.3792097 35 0.3331573 0.2876330 0.3792097 36 0.3331573 0.2876330 0.3792097 37 0.2980880 0.2837746 0.4181374 38 0.2980880 0.2837746 0.4181374 39 0.2980880 0.2837746 0.4181374 40 0.1942209 0.2470589 0.5587202 41 0.1942209 0.2470589 0.5587202 42 0.1942209 0.2470589 0.5587202 43 0.1047770 0.1724227 0.7228003 44 0.1047770 0.1724227 0.7228003 45 0.1047770 0.1724227 0.7228003 46 0.4294564 0.2820629 0.2884807 47 0.4294564 0.2820629 0.2884807 48 0.4294564 0.2820629 0.2884807 49 0.2993357 0.2839753 0.4166890 50 0.2993357 0.2839753 0.4166890 51 0.2993357 0.2839753 0.4166890 52 0.1718050 0.2328648 0.5953302 53 0.1718050 0.2328648 0.5953302 54 0.1718050 0.2328648 0.5953302 55 0.3798387 0.2875972 0.3325641 56 0.3798387 0.2875972 0.3325641 57 0.3798387 0.2875972 0.3325641 58 0.2579546 0.2745537 0.4674917 59 0.2579546 0.2745537 0.4674917 60 0.2579546 0.2745537 0.4674917 61 0.1444202 0.2117081 0.6438717 62 0.1444202 0.2117081 0.6438717 63 0.1444202 0.2117081 0.6438717 64 0.5583813 0.2471826 0.1944361 65 0.5583813 0.2471826 0.1944361 66 0.5583813 0.2471826 0.1944361 67 0.4178031 0.2838213 0.2983756 68 0.4178031 0.2838213 0.2983756 69 0.4178031 0.2838213 0.2983756 70 0.2584149 0.2746916 0.4668935 71 0.2584149 0.2746916 0.4668935 72 0.2584149 0.2746916 0.4668935 > addterm(house.plr, ~.^2, test = "Chisq") Single term additions Model: Sat ~ Infl + Type + Cont Df AIC LRT Pr(Chi) 3495.1 Infl:Type 6 3484.6 22.5093 0.0009786 *** Infl:Cont 2 3498.9 0.2090 0.9007957 Type:Cont 3 3492.5 8.6662 0.0340752 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > house.plr2 <- stepAIC(house.plr, ~.^2) Start: AIC=3495.15 Sat ~ Infl + Type + Cont Df AIC + Infl:Type 6 3484.6 + Type:Cont 3 3492.5 3495.1 + Infl:Cont 2 3498.9 - Cont 1 3507.5 - Type 3 3545.1 - Infl 2 3599.4 Step: AIC=3484.64 Sat ~ Infl + Type + Cont + Infl:Type Df AIC + Type:Cont 3 3482.7 3484.6 + Infl:Cont 2 3488.5 - Infl:Type 6 3495.1 - Cont 1 3497.8 Step: AIC=3482.69 Sat ~ Infl + Type + Cont + Infl:Type + Type:Cont Df AIC 3482.7 - Type:Cont 3 3484.6 + Infl:Cont 2 3486.6 - Infl:Type 6 3492.5 > house.plr2$anova Stepwise Model Path Analysis of Deviance Table Initial Model: Sat ~ Infl + Type + Cont Final Model: Sat ~ Infl + Type + Cont + Infl:Type + Type:Cont Step Df Deviance Resid. Df Resid. Dev AIC 1 1673 3479.149 3495.149 2 + Infl:Type 6 22.509347 1667 3456.640 3484.640 3 + Type:Cont 3 7.945029 1664 3448.695 3482.695 > anova(house.plr, house.plr2) Likelihood ratio tests of ordinal regression models Response: Sat Model Resid. df Resid. Dev Test Df 1 Infl + Type + Cont 1673 3479.149 2 Infl + Type + Cont + Infl:Type + Type:Cont 1664 3448.695 1 vs 2 9 LR stat. Pr(Chi) 1 2 30.45438 0.0003670555 > > house.plr <- update(house.plr, Hess=TRUE) > pr <- profile(house.plr) > confint(pr) 2.5 % 97.5 % InflMedium 0.3616415 0.77195375 InflHigh 1.0409701 1.53958138 TypeApartment -0.8069590 -0.33940432 TypeAtrium -0.6705862 -0.06204495 TypeTerrace -1.3893863 -0.79533958 ContHigh 0.1733589 0.54792854 > plot(pr) > pairs(pr) > > > > base::options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("predict.glmmPQL") > ### * predict.glmmPQL > > flush(stderr()); flush(stdout()) > > ### Name: predict.glmmPQL > ### Title: Predict Method for glmmPQL Fits > ### Aliases: predict.glmmPQL > ### Keywords: models > > ### ** Examples > > fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, + family = binomial, data = bacteria) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 > predict(fit, bacteria, level = 0, type="response") [1] 0.9680779 0.9680779 0.8587270 0.8587270 0.9344832 0.9344832 0.7408574 [8] 0.7408574 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 0.9680779 [15] 0.9680779 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 [22] 0.8587270 0.8587270 0.8970307 0.8970307 0.6358511 0.6358511 0.9344832 [29] 0.9344832 0.7408574 0.7408574 0.7408574 0.9680779 0.9680779 0.8587270 [36] 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 [43] 0.9344832 0.7408574 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 [50] 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 0.9680779 0.9680779 [57] 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 0.8970307 [64] 0.8970307 0.6358511 0.6358511 0.6358511 0.9344832 0.9344832 0.7408574 [71] 0.7408574 0.7408574 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 [78] 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 0.9680779 0.9680779 [85] 0.8587270 0.8587270 0.8587270 0.9344832 0.9344832 0.7408574 0.7408574 [92] 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 [99] 0.8587270 0.8587270 0.8587270 0.9680779 0.9680779 0.8587270 0.8587270 [106] 0.8587270 0.9344832 0.9344832 0.7408574 0.7408574 0.7408574 0.8970307 [113] 0.8970307 0.6358511 0.6358511 0.9680779 0.9680779 0.8587270 0.9680779 [120] 0.9680779 0.8587270 0.8587270 0.8970307 0.8970307 0.6358511 0.6358511 [127] 0.6358511 0.9344832 0.7408574 0.7408574 0.7408574 0.9680779 0.8587270 [134] 0.8587270 0.8587270 0.8970307 0.8970307 0.6358511 0.6358511 0.6358511 [141] 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 0.9344832 0.7408574 [148] 0.8970307 0.8970307 0.6358511 0.6358511 0.9680779 0.9680779 0.8587270 [155] 0.8970307 0.8970307 0.6358511 0.9680779 0.9680779 0.8587270 0.8587270 [162] 0.8587270 0.9344832 0.9344832 0.7408574 0.7408574 0.7408574 0.9680779 [169] 0.9680779 0.8587270 0.8587270 0.8587270 0.9344832 0.7408574 0.8970307 [176] 0.8970307 0.6358511 0.6358511 0.6358511 0.9344832 0.9344832 0.7408574 [183] 0.7408574 0.9680779 0.9680779 0.8587270 0.8587270 0.8587270 0.8970307 [190] 0.8970307 0.6358511 0.6358511 0.6358511 0.9344832 0.9344832 0.7408574 [197] 0.7408574 0.7408574 0.8970307 0.6358511 0.6358511 0.9344832 0.9344832 [204] 0.7408574 0.7408574 0.7408574 0.8970307 0.8970307 0.6358511 0.6358511 [211] 0.9344832 0.9344832 0.7408574 0.7408574 0.7408574 0.9344832 0.9344832 [218] 0.7408574 0.7408574 0.7408574 attr(,"label") [1] "Predicted values" > predict(fit, bacteria, level = 1, type="response") X01 X01 X01 X01 X02 X02 X02 X02 0.9828449 0.9828449 0.9198935 0.9198935 0.9050782 0.9050782 0.6564944 0.6564944 X03 X03 X03 X03 X03 X04 X04 X04 0.9724022 0.9724022 0.8759665 0.8759665 0.8759665 0.9851548 0.9851548 0.9300763 X04 X04 X05 X05 X05 X05 X05 X06 0.9300763 0.9300763 0.9851548 0.9851548 0.9300763 0.9300763 0.9300763 0.9662755 X06 X06 X06 X07 X07 X07 X07 X07 0.9662755 0.8516962 0.8516962 0.7291679 0.7291679 0.3504978 0.3504978 0.3504978 X08 X08 X08 X08 X08 X09 X09 X09 0.9426815 0.9426815 0.7672499 0.7672499 0.7672499 0.9851548 0.9851548 0.9300763 X09 X09 X10 X10 X11 X11 X11 X11 0.9300763 0.9300763 0.9640326 0.8430706 0.9851548 0.9851548 0.9300763 0.9300763 X11 X12 X12 X12 X12 X12 X13 X13 0.9300763 0.8334870 0.8334870 0.5008219 0.5008219 0.5008219 0.9851548 0.9851548 X13 X13 X13 X14 X14 X14 X15 X15 0.9300763 0.9300763 0.9300763 0.8907227 0.8907227 0.6203155 0.9724022 0.9724022 X15 X15 X15 X16 X16 X16 X16 X16 0.8759665 0.8759665 0.8759665 0.9287777 0.9287777 0.7232833 0.7232833 0.7232833 X17 X17 X17 X17 X17 X18 X18 X18 0.9426815 0.9426815 0.7672499 0.7672499 0.7672499 0.7070916 0.7070916 0.3260827 X18 X18 X19 X19 X19 X19 X19 X20 0.3260827 0.3260827 0.8702991 0.8702991 0.5735499 0.5735499 0.5735499 0.9736293 X20 X20 X20 X21 X21 X21 X21 X21 0.9736293 0.8809564 0.8809564 0.9851548 0.9851548 0.9300763 0.9300763 0.9300763 Y01 Y01 Y01 Y01 Y01 Y02 Y02 Y02 0.9851548 0.9851548 0.9300763 0.9300763 0.9300763 0.7607971 0.7607971 0.3893126 Y02 Y02 Y03 Y03 Y03 Y03 Y03 Y04 0.3893126 0.3893126 0.8487181 0.8487181 0.5292976 0.5292976 0.5292976 0.5734482 Y04 Y04 Y04 Y05 Y05 Y05 Y06 Y06 0.5734482 0.2122655 0.2122655 0.7144523 0.7144523 0.3339997 0.9828449 0.9828449 Y06 Y06 Y07 Y07 Y07 Y07 Y07 Y08 0.9198935 0.9198935 0.8334870 0.8334870 0.5008219 0.5008219 0.5008219 0.9238389 Y08 Y08 Y08 Y09 Y09 Y09 Y09 Y10 0.7085660 0.7085660 0.7085660 0.9847299 0.9281899 0.9281899 0.9281899 0.9188296 Y10 Y10 Y10 Y10 Y11 Y11 Y11 Y11 0.9188296 0.6940862 0.6940862 0.6940862 0.9851548 0.9851548 0.9300763 0.9300763 Y11 Y12 Y12 Y13 Y13 Y13 Y13 Y14 0.9300763 0.9640326 0.8430706 0.5734482 0.5734482 0.2122655 0.2122655 0.9793383 Y14 Y14 Z01 Z01 Z01 Z02 Z02 Z02 0.9793383 0.9047659 0.9556329 0.9556329 0.8119328 0.9851548 0.9851548 0.9300763 Z02 Z02 Z03 Z03 Z03 Z03 Z03 Z05 0.9300763 0.9300763 0.9779690 0.9779690 0.8989642 0.8989642 0.8989642 0.8702991 Z05 Z05 Z05 Z05 Z06 Z06 Z07 Z07 0.8702991 0.5735499 0.5735499 0.5735499 0.8306525 0.4957505 0.8334870 0.8334870 Z07 Z07 Z07 Z09 Z09 Z09 Z09 Z10 0.5008219 0.5008219 0.5008219 0.9736293 0.9736293 0.8809564 0.8809564 0.9851548 Z10 Z10 Z10 Z10 Z11 Z11 Z11 Z11 0.9851548 0.9300763 0.9300763 0.9300763 0.9724022 0.9724022 0.8759665 0.8759665 Z11 Z14 Z14 Z14 Z14 Z14 Z15 Z15 0.8759665 0.9287777 0.9287777 0.7232833 0.7232833 0.7232833 0.9643851 0.8444172 Z15 Z19 Z19 Z19 Z19 Z19 Z20 Z20 0.8444172 0.9779690 0.9779690 0.8989642 0.8989642 0.8989642 0.7620490 0.7620490 Z20 Z20 Z24 Z24 Z24 Z24 Z24 Z26 0.3909523 0.3909523 0.8487181 0.8487181 0.5292976 0.5292976 0.5292976 0.9287777 Z26 Z26 Z26 Z26 0.9287777 0.7232833 0.7232833 0.7232833 attr(,"label") [1] "Predicted values" > > > > cleanEx() > nameEx("predict.lda") > ### * predict.lda > > flush(stderr()); flush(stdout()) > > ### Name: predict.lda > ### Title: Classify Multivariate Observations by Linear Discrimination > ### Aliases: predict.lda > ### Keywords: multivariate > > ### ** Examples > > tr <- sample(1:50, 25) > train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) > test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) > cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) > z <- lda(train, cl) > predict(z, test)$class [1] s s s s s s s s s s s s s s s s s s s s s s s s s c c c c c c c c c c c c c [39] c c c c c c c c c c c c v v v v v v v v v v v v v v v v v c v v v v v v v Levels: c s v > > > > cleanEx() > nameEx("predict.lqs") > ### * predict.lqs > > flush(stderr()); flush(stdout()) > > ### Name: predict.lqs > ### Title: Predict from an lqs Fit > ### Aliases: predict.lqs > ### Keywords: models > > ### ** Examples > > set.seed(123) > fm <- lqs(stack.loss ~ ., data = stackloss, method = "S", nsamp = "exact") > predict(fm, stackloss) 1 2 3 4 5 6 7 8 35.500000 35.579646 30.409292 19.477876 18.592920 19.035398 19.000000 19.000000 9 10 11 12 13 14 15 16 15.734513 14.079646 13.362832 13.000000 13.920354 13.486726 6.761062 7.000000 17 18 19 20 21 8.557522 8.000000 8.362832 13.154867 23.991150 > > > > cleanEx() > nameEx("predict.qda") > ### * predict.qda > > flush(stderr()); flush(stdout()) > > ### Name: predict.qda > ### Title: Classify from Quadratic Discriminant Analysis > ### Aliases: predict.qda > ### Keywords: multivariate > > ### ** Examples > > tr <- sample(1:50, 25) > train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) > test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) > cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) > zq <- qda(train, cl) > predict(zq, test)$class [1] s s s s s s s s s s s s s s s s s s s s s s s s s c c c c c c c v c c c c c [39] c c c c c c c c c c c c v v v v v v v v v v v v v v v v v v v v v v v v v Levels: c s v > > > > cleanEx() > nameEx("qda") > ### * qda > > flush(stderr()); flush(stdout()) > > ### Name: qda > ### Title: Quadratic Discriminant Analysis > ### Aliases: qda qda.data.frame qda.default qda.formula qda.matrix > ### model.frame.qda print.qda > ### Keywords: multivariate > > ### ** Examples > > tr <- sample(1:50, 25) > train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) > test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) > cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) > z <- qda(train, cl) > predict(z,test)$class [1] s s s s s s s s s s s s s s s s s s s s s s s s s c c c c c c c v c c c c c [39] c c c c c c c c c c c c v v v v v v v v v v v v v v v v v v v v v v v v v Levels: c s v > > > > cleanEx() > nameEx("rational") > ### * rational > > flush(stderr()); flush(stdout()) > > ### Name: rational > ### Title: Rational Approximation > ### Aliases: rational .rat > ### Keywords: math > > ### ** Examples > > X <- matrix(runif(25), 5, 5) > zapsmall(solve(X, X/5)) # print near-zeroes as zero [,1] [,2] [,3] [,4] [,5] [1,] 0.2 0.0 0.0 0.0 0.0 [2,] 0.0 0.2 0.0 0.0 0.0 [3,] 0.0 0.0 0.2 0.0 0.0 [4,] 0.0 0.0 0.0 0.2 0.0 [5,] 0.0 0.0 0.0 0.0 0.2 > rational(solve(X, X/5)) [,1] [,2] [,3] [,4] [,5] [1,] 0.2 0.0 0.0 0.0 0.0 [2,] 0.0 0.2 0.0 0.0 0.0 [3,] 0.0 0.0 0.2 0.0 0.0 [4,] 0.0 0.0 0.0 0.2 0.0 [5,] 0.0 0.0 0.0 0.0 0.2 > > > > cleanEx() > nameEx("renumerate") > ### * renumerate > > flush(stderr()); flush(stdout()) > > ### Name: renumerate > ### Title: Convert a Formula Transformed by 'denumerate' > ### Aliases: renumerate renumerate.formula > ### Keywords: models > > ### ** Examples > > denumerate(~(1+2+3)^3 + a/b) ~(.v1 + .v2 + .v3)^3 + a/b > ## ~ (.v1 + .v2 + .v3)^3 + a/b > renumerate(.Last.value) ~(`1` + `2` + `3`)^3 + a/b > ## ~ (1 + 2 + 3)^3 + a/b > > > > cleanEx() > nameEx("rlm") > ### * rlm > > flush(stderr()); flush(stdout()) > > ### Name: rlm > ### Title: Robust Fitting of Linear Models > ### Aliases: rlm rlm.default rlm.formula print.rlm predict.rlm psi.bisquare > ### psi.hampel psi.huber > ### Keywords: models robust > > ### ** Examples > > summary(rlm(stack.loss ~ ., stackloss)) Call: rlm(formula = stack.loss ~ ., data = stackloss) Residuals: Min 1Q Median 3Q Max -8.91753 -1.73127 0.06187 1.54306 6.50163 Coefficients: Value Std. Error t value (Intercept) -41.0265 9.8073 -4.1832 Air.Flow 0.8294 0.1112 7.4597 Water.Temp 0.9261 0.3034 3.0524 Acid.Conc. -0.1278 0.1289 -0.9922 Residual standard error: 2.441 on 17 degrees of freedom > rlm(stack.loss ~ ., stackloss, psi = psi.hampel, init = "lts") Call: rlm(formula = stack.loss ~ ., data = stackloss, psi = psi.hampel, init = "lts") Converged in 9 iterations Coefficients: (Intercept) Air.Flow Water.Temp Acid.Conc. -40.4747826 0.7410853 1.2250730 -0.1455245 Degrees of freedom: 21 total; 17 residual Scale estimate: 3.09 > rlm(stack.loss ~ ., stackloss, psi = psi.bisquare) Call: rlm(formula = stack.loss ~ ., data = stackloss, psi = psi.bisquare) Converged in 11 iterations Coefficients: (Intercept) Air.Flow Water.Temp Acid.Conc. -42.2852537 0.9275471 0.6507322 -0.1123310 Degrees of freedom: 21 total; 17 residual Scale estimate: 2.28 > > > > cleanEx() > nameEx("rms.curv") > ### * rms.curv > > flush(stderr()); flush(stdout()) > > ### Name: rms.curv > ### Title: Relative Curvature Measures for Non-Linear Regression > ### Aliases: rms.curv print.rms.curv > ### Keywords: nonlinear > > ### ** Examples > > # The treated sample from the Puromycin data > mmcurve <- deriv3(~ Vm * conc/(K + conc), c("Vm", "K"), + function(Vm, K, conc) NULL) > Treated <- Puromycin[Puromycin$state == "treated", ] > (Purfit1 <- nls(rate ~ mmcurve(Vm, K, conc), data = Treated, + start = list(Vm=200, K=0.1))) Nonlinear regression model model: rate ~ mmcurve(Vm, K, conc) data: Treated Vm K 212.68363 0.06412 residual sum-of-squares: 1195 Number of iterations to convergence: 6 Achieved convergence tolerance: 6.096e-06 > rms.curv(Purfit1) Parameter effects: c^theta x sqrt(F) = 0.2121 Intrinsic: c^iota x sqrt(F) = 0.092 > ##Parameter effects: c^theta x sqrt(F) = 0.2121 > ## Intrinsic: c^iota x sqrt(F) = 0.092 > > > > cleanEx() > nameEx("rnegbin") > ### * rnegbin > > flush(stderr()); flush(stdout()) > > ### Name: rnegbin > ### Title: Simulate Negative Binomial Variates > ### Aliases: rnegbin > ### Keywords: distribution > > ### ** Examples > > # Negative Binomials with means fitted(fm) and theta = 4.5 > fm <- glm.nb(Days ~ ., data = quine) > dummy <- rnegbin(fitted(fm), theta = 4.5) > > > > cleanEx() > nameEx("sammon") > ### * sammon > > flush(stderr()); flush(stdout()) > > ### Name: sammon > ### Title: Sammon's Non-Linear Mapping > ### Aliases: sammon > ### Keywords: multivariate > > ### ** Examples > > swiss.x <- as.matrix(swiss[, -1]) > swiss.sam <- sammon(dist(swiss.x)) Initial stress : 0.00824 stress after 10 iters: 0.00439, magic = 0.338 stress after 20 iters: 0.00383, magic = 0.500 stress after 30 iters: 0.00383, magic = 0.500 > plot(swiss.sam$points, type = "n") > text(swiss.sam$points, labels = as.character(1:nrow(swiss.x))) > > > > cleanEx() > nameEx("stepAIC") > ### * stepAIC > > flush(stderr()); flush(stdout()) > > ### Name: stepAIC > ### Title: Choose a model by AIC in a Stepwise Algorithm > ### Aliases: stepAIC extractAIC.gls terms.gls extractAIC.lme terms.lme > ### Keywords: models > > ### ** Examples > > quine.hi <- aov(log(Days + 2.5) ~ .^4, quine) > quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn) > quine.stp <- stepAIC(quine.nxt, + scope = list(upper = ~Eth*Sex*Age*Lrn, lower = ~1), + trace = FALSE) > quine.stp$anova Stepwise Model Path Analysis of Deviance Table Initial Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Age + Eth:Sex:Lrn + Eth:Age:Lrn + Sex:Age:Lrn Final Model: log(Days + 2.5) ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Lrn + Eth:Age:Lrn Step Df Deviance Resid. Df Resid. Dev AIC 1 120 64.09900 -68.18396 2 - Eth:Sex:Age 3 0.973869 123 65.07287 -71.98244 3 - Sex:Age:Lrn 2 1.526754 125 66.59962 -72.59652 > > cpus1 <- cpus > for(v in names(cpus)[2:7]) + cpus1[[v]] <- cut(cpus[[v]], unique(quantile(cpus[[v]])), + include.lowest = TRUE) > cpus0 <- cpus1[, 2:8] # excludes names, authors' predictions > cpus.samp <- sample(1:209, 100) > cpus.lm <- lm(log10(perf) ~ ., data = cpus1[cpus.samp,2:8]) > cpus.lm2 <- stepAIC(cpus.lm, trace = FALSE) > cpus.lm2$anova Stepwise Model Path Analysis of Deviance Table Initial Model: log10(perf) ~ syct + mmin + mmax + cach + chmin + chmax Final Model: log10(perf) ~ syct + mmax + cach + chmax Step Df Deviance Resid. Df Resid. Dev AIC 1 82 3.458189 -300.4425 2 - chmin 3 0.02548983 85 3.483679 -305.7081 3 - mmin 3 0.12039102 88 3.604070 -308.3106 > > example(birthwt) brthwt> bwt <- with(birthwt, { brthwt+ race <- factor(race, labels = c("white", "black", "other")) brthwt+ ptd <- factor(ptl > 0) brthwt+ ftv <- factor(ftv) brthwt+ levels(ftv)[-(1:2)] <- "2+" brthwt+ data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0), brthwt+ ptd, ht = (ht > 0), ui = (ui > 0), ftv) brthwt+ }) brthwt> options(contrasts = c("contr.treatment", "contr.poly")) brthwt> glm(low ~ ., binomial, bwt) Call: glm(formula = low ~ ., family = binomial, data = bwt) Coefficients: (Intercept) age lwt raceblack raceother smokeTRUE 0.82302 -0.03723 -0.01565 1.19241 0.74068 0.75553 ptdTRUE htTRUE uiTRUE ftv1 ftv2+ 1.34376 1.91317 0.68020 -0.43638 0.17901 Degrees of Freedom: 188 Total (i.e. Null); 178 Residual Null Deviance: 234.7 Residual Deviance: 195.5 AIC: 217.5 > birthwt.glm <- glm(low ~ ., family = binomial, data = bwt) > birthwt.step <- stepAIC(birthwt.glm, trace = FALSE) > birthwt.step$anova Stepwise Model Path Analysis of Deviance Table Initial Model: low ~ age + lwt + race + smoke + ptd + ht + ui + ftv Final Model: low ~ lwt + race + smoke + ptd + ht + ui Step Df Deviance Resid. Df Resid. Dev AIC 1 178 195.4755 217.4755 2 - ftv 2 1.358185 180 196.8337 214.8337 3 - age 1 1.017866 181 197.8516 213.8516 > birthwt.step2 <- stepAIC(birthwt.glm, ~ .^2 + I(scale(age)^2) + + I(scale(lwt)^2), trace = FALSE) > birthwt.step2$anova Stepwise Model Path Analysis of Deviance Table Initial Model: low ~ age + lwt + race + smoke + ptd + ht + ui + ftv Final Model: low ~ age + lwt + smoke + ptd + ht + ui + ftv + age:ftv + smoke:ui Step Df Deviance Resid. Df Resid. Dev AIC 1 178 195.4755 217.4755 2 + age:ftv 2 12.474896 176 183.0006 209.0006 3 + smoke:ui 1 3.056805 175 179.9438 207.9438 4 - race 2 3.129586 177 183.0734 207.0734 > > quine.nb <- glm.nb(Days ~ .^4, data = quine) > quine.nb2 <- stepAIC(quine.nb) Start: AIC=1095.32 Days ~ (Eth + Sex + Age + Lrn)^4 Df AIC - Eth:Sex:Age:Lrn 2 1092.7 1095.3 Step: AIC=1092.73 Days ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Age + Eth:Sex:Lrn + Eth:Age:Lrn + Sex:Age:Lrn Df AIC - Eth:Sex:Age 3 1089.4 1092.7 - Eth:Sex:Lrn 1 1093.3 - Eth:Age:Lrn 2 1094.7 - Sex:Age:Lrn 2 1095.0 Step: AIC=1089.41 Days ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Lrn + Eth:Age:Lrn + Sex:Age:Lrn Df AIC 1089.4 - Sex:Age:Lrn 2 1091.1 - Eth:Age:Lrn 2 1091.2 - Eth:Sex:Lrn 1 1092.5 > quine.nb2$anova Stepwise Model Path Analysis of Deviance Table Initial Model: Days ~ (Eth + Sex + Age + Lrn)^4 Final Model: Days ~ Eth + Sex + Age + Lrn + Eth:Sex + Eth:Age + Eth:Lrn + Sex:Age + Sex:Lrn + Age:Lrn + Eth:Sex:Lrn + Eth:Age:Lrn + Sex:Age:Lrn Step Df Deviance Resid. Df Resid. Dev AIC 1 118 167.4535 1095.324 2 - Eth:Sex:Age:Lrn 2 0.09746244 120 167.5509 1092.728 3 - Eth:Sex:Age 3 0.11060087 123 167.4403 1089.409 > > > > cleanEx() > nameEx("summary.negbin") > ### * summary.negbin > > flush(stderr()); flush(stdout()) > > ### Name: summary.negbin > ### Title: Summary Method Function for Objects of Class 'negbin' > ### Aliases: summary.negbin print.summary.negbin > ### Keywords: models > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > summary(glm.nb(Days ~ Eth*Age*Lrn*Sex, quine, link = log)) Call: glm.nb(formula = Days ~ Eth * Age * Lrn * Sex, data = quine, link = log, init.theta = 1.928360145) Coefficients: (4 not defined because of singularities) Estimate Std. Error z value Pr(>|z|) (Intercept) 3.0564 0.3760 8.128 4.38e-16 *** EthN -0.1386 0.5334 -0.260 0.795023 AgeF1 -0.6227 0.5125 -1.215 0.224334 AgeF2 -2.3632 1.0770 -2.194 0.028221 * AgeF3 -0.3784 0.4546 -0.832 0.405215 LrnSL -1.9577 0.9967 -1.964 0.049493 * SexM -0.4914 0.5104 -0.963 0.335653 EthN:AgeF1 0.1029 0.7123 0.144 0.885175 EthN:AgeF2 -0.5546 1.6798 -0.330 0.741297 EthN:AgeF3 0.0633 0.6396 0.099 0.921159 EthN:LrnSL 2.2588 1.3019 1.735 0.082743 . AgeF1:LrnSL 2.6421 1.0821 2.442 0.014618 * AgeF2:LrnSL 4.8585 1.4423 3.369 0.000755 *** AgeF3:LrnSL NA NA NA NA EthN:SexM -0.7524 0.7220 -1.042 0.297400 AgeF1:SexM 0.4092 0.8299 0.493 0.621973 AgeF2:SexM 3.1098 1.1655 2.668 0.007624 ** AgeF3:SexM 1.1145 0.6365 1.751 0.079926 . LrnSL:SexM 1.5900 1.1499 1.383 0.166750 EthN:AgeF1:LrnSL -3.5493 1.4270 -2.487 0.012876 * EthN:AgeF2:LrnSL -3.3315 2.0919 -1.593 0.111256 EthN:AgeF3:LrnSL NA NA NA NA EthN:AgeF1:SexM -0.3105 1.2055 -0.258 0.796735 EthN:AgeF2:SexM 0.3469 1.7965 0.193 0.846875 EthN:AgeF3:SexM 0.8329 0.8970 0.929 0.353092 EthN:LrnSL:SexM -0.1639 1.5250 -0.107 0.914411 AgeF1:LrnSL:SexM -2.4285 1.4201 -1.710 0.087246 . AgeF2:LrnSL:SexM -4.1914 1.6201 -2.587 0.009679 ** AgeF3:LrnSL:SexM NA NA NA NA EthN:AgeF1:LrnSL:SexM 2.1711 1.9192 1.131 0.257963 EthN:AgeF2:LrnSL:SexM 2.1029 2.3444 0.897 0.369718 EthN:AgeF3:LrnSL:SexM NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Negative Binomial(1.9284) family taken to be 1) Null deviance: 272.29 on 145 degrees of freedom Residual deviance: 167.45 on 118 degrees of freedom AIC: 1097.3 Number of Fisher Scoring iterations: 1 Theta: 1.928 Std. Err.: 0.269 2 x log-likelihood: -1039.324 > ## IGNORE_RDIFF_END > > > > cleanEx() > nameEx("summary.rlm") > ### * summary.rlm > > flush(stderr()); flush(stdout()) > > ### Name: summary.rlm > ### Title: Summary Method for Robust Linear Models > ### Aliases: summary.rlm print.summary.rlm > ### Keywords: robust > > ### ** Examples > > summary(rlm(calls ~ year, data = phones, maxit = 50)) Call: rlm(formula = calls ~ year, data = phones, maxit = 50) Residuals: Min 1Q Median 3Q Max -18.314 -5.953 -1.681 26.460 173.769 Coefficients: Value Std. Error t value (Intercept) -102.6222 26.6082 -3.8568 year 2.0414 0.4299 4.7480 Residual standard error: 9.032 on 22 degrees of freedom > > > > cleanEx() > nameEx("theta.md") > ### * theta.md > > flush(stderr()); flush(stdout()) > > ### Name: theta.md > ### Title: Estimate theta of the Negative Binomial > ### Aliases: theta.md theta.ml theta.mm > ### Keywords: models > > ### ** Examples > > quine.nb <- glm.nb(Days ~ .^2, data = quine) > theta.md(quine$Days, fitted(quine.nb), dfr = df.residual(quine.nb)) [1] 1.135441 > theta.ml(quine$Days, fitted(quine.nb)) [1] 1.603641 attr(,"SE") [1] 0.2138379 > theta.mm(quine$Days, fitted(quine.nb), dfr = df.residual(quine.nb)) [1] 1.562879 > > ## weighted example > yeast <- data.frame(cbind(numbers = 0:5, fr = c(213, 128, 37, 18, 3, 1))) > fit <- glm.nb(numbers ~ 1, weights = fr, data = yeast) > ## IGNORE_RDIFF_BEGIN > summary(fit) Call: glm.nb(formula = numbers ~ 1, data = yeast, weights = fr, init.theta = 3.586087428, link = log) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.38199 0.06603 -5.785 7.25e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Negative Binomial(3.5861) family taken to be 1) Null deviance: 408.9 on 5 degrees of freedom Residual deviance: 408.9 on 5 degrees of freedom AIC: 897.06 Number of Fisher Scoring iterations: 1 Theta: 3.59 Std. Err.: 1.75 2 x log-likelihood: -893.063 > ## IGNORE_RDIFF_END > mu <- fitted(fit) > theta.md(yeast$numbers, mu, dfr = 399, weights = yeast$fr) [1] 3.027079 > theta.ml(yeast$numbers, mu, limit = 15, weights = yeast$fr) [1] 3.586087 attr(,"SE") [1] 1.749609 > theta.mm(yeast$numbers, mu, dfr = 399, weights = yeast$fr) [1] 3.549593 > > > > cleanEx() > nameEx("ucv") > ### * ucv > > flush(stderr()); flush(stdout()) > > ### Name: ucv > ### Title: Unbiased Cross-Validation for Bandwidth Selection > ### Aliases: ucv > ### Keywords: dplot > > ### ** Examples > > ucv(geyser$duration) Warning in ucv(geyser$duration) : minimum occurred at one end of the range [1] 0.1746726 > > > > cleanEx() > nameEx("waders") > ### * waders > > flush(stderr()); flush(stdout()) > > ### Name: waders > ### Title: Counts of Waders at 15 Sites in South Africa > ### Aliases: waders > ### Keywords: datasets > > ### ** Examples > > plot(corresp(waders, nf=2)) > > > > cleanEx() > nameEx("whiteside") > ### * whiteside > > flush(stderr()); flush(stdout()) > > ### Name: whiteside > ### Title: House Insulation: Whiteside's Data > ### Aliases: whiteside > ### Keywords: datasets > > ### ** Examples > > require(lattice) Loading required package: lattice > xyplot(Gas ~ Temp | Insul, whiteside, panel = + function(x, y, ...) { + panel.xyplot(x, y, ...) + panel.lmline(x, y, ...) + }, xlab = "Average external temperature (deg. C)", + ylab = "Gas consumption (1000 cubic feet)", aspect = "xy", + strip = function(...) strip.default(..., style = 1)) > > gasB <- lm(Gas ~ Temp, whiteside, subset = Insul=="Before") > gasA <- update(gasB, subset = Insul=="After") > summary(gasB) Call: lm(formula = Gas ~ Temp, data = whiteside, subset = Insul == "Before") Residuals: Min 1Q Median 3Q Max -0.62020 -0.19947 0.06068 0.16770 0.59778 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.85383 0.11842 57.88 <2e-16 *** Temp -0.39324 0.01959 -20.08 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2813 on 24 degrees of freedom Multiple R-squared: 0.9438, Adjusted R-squared: 0.9415 F-statistic: 403.1 on 1 and 24 DF, p-value: < 2.2e-16 > summary(gasA) Call: lm(formula = Gas ~ Temp, data = whiteside, subset = Insul == "After") Residuals: Min 1Q Median 3Q Max -0.97802 -0.11082 0.02672 0.25294 0.63803 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.72385 0.12974 36.41 < 2e-16 *** Temp -0.27793 0.02518 -11.04 1.05e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3548 on 28 degrees of freedom Multiple R-squared: 0.8131, Adjusted R-squared: 0.8064 F-statistic: 121.8 on 1 and 28 DF, p-value: 1.046e-11 > gasBA <- lm(Gas ~ Insul/Temp - 1, whiteside) > summary(gasBA) Call: lm(formula = Gas ~ Insul/Temp - 1, data = whiteside) Residuals: Min 1Q Median 3Q Max -0.97802 -0.18011 0.03757 0.20930 0.63803 Coefficients: Estimate Std. Error t value Pr(>|t|) InsulBefore 6.85383 0.13596 50.41 <2e-16 *** InsulAfter 4.72385 0.11810 40.00 <2e-16 *** InsulBefore:Temp -0.39324 0.02249 -17.49 <2e-16 *** InsulAfter:Temp -0.27793 0.02292 -12.12 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.323 on 52 degrees of freedom Multiple R-squared: 0.9946, Adjusted R-squared: 0.9942 F-statistic: 2391 on 4 and 52 DF, p-value: < 2.2e-16 > > gasQ <- lm(Gas ~ Insul/(Temp + I(Temp^2)) - 1, whiteside) > coef(summary(gasQ)) Estimate Std. Error t value Pr(>|t|) InsulBefore 6.759215179 0.150786777 44.826312 4.854615e-42 InsulAfter 4.496373920 0.160667904 27.985514 3.302572e-32 InsulBefore:Temp -0.317658735 0.062965170 -5.044991 6.362323e-06 InsulAfter:Temp -0.137901603 0.073058019 -1.887563 6.489554e-02 InsulBefore:I(Temp^2) -0.008472572 0.006624737 -1.278930 2.068259e-01 InsulAfter:I(Temp^2) -0.014979455 0.007447107 -2.011446 4.968398e-02 > > gasPR <- lm(Gas ~ Insul + Temp, whiteside) > anova(gasPR, gasBA) Analysis of Variance Table Model 1: Gas ~ Insul + Temp Model 2: Gas ~ Insul/Temp - 1 Res.Df RSS Df Sum of Sq F Pr(>F) 1 53 6.7704 2 52 5.4252 1 1.3451 12.893 0.0007307 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > options(contrasts = c("contr.treatment", "contr.poly")) > gasBA1 <- lm(Gas ~ Insul*Temp, whiteside) > coef(summary(gasBA1)) Estimate Std. Error t value Pr(>|t|) (Intercept) 6.8538277 0.13596397 50.409146 7.997414e-46 InsulAfter -2.1299780 0.18009172 -11.827185 2.315921e-16 Temp -0.3932388 0.02248703 -17.487358 1.976009e-23 InsulAfter:Temp 0.1153039 0.03211212 3.590665 7.306852e-04 > > > > base::options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() detaching ‘package:lattice’ > nameEx("width.SJ") > ### * width.SJ > > flush(stderr()); flush(stdout()) > > ### Name: width.SJ > ### Title: Bandwidth Selection by Pilot Estimation of Derivatives > ### Aliases: width.SJ > ### Keywords: dplot > > ### ** Examples > > width.SJ(geyser$duration, method = "dpi") [1] 0.5747852 > width.SJ(geyser$duration) [1] 0.360518 > > width.SJ(galaxies, method = "dpi") [1] 3256.151 > width.SJ(galaxies) [1] 2566.423 > > > > cleanEx() > nameEx("wtloss") > ### * wtloss > > flush(stderr()); flush(stdout()) > > ### Name: wtloss > ### Title: Weight Loss Data from an Obese Patient > ### Aliases: wtloss > ### Keywords: datasets > > ### ** Examples > > ## IGNORE_RDIFF_BEGIN > wtloss.fm <- nls(Weight ~ b0 + b1*2^(-Days/th), + data = wtloss, start = list(b0=90, b1=95, th=120)) > wtloss.fm Nonlinear regression model model: Weight ~ b0 + b1 * 2^(-Days/th) data: wtloss b0 b1 th 81.37 102.68 141.91 residual sum-of-squares: 39.24 Number of iterations to convergence: 3 Achieved convergence tolerance: 4.389e-06 > ## IGNORE_RDIFF_END > plot(wtloss) > with(wtloss, lines(Days, fitted(wtloss.fm))) > > > > ### *