146 lines
5.0 KiB
Plaintext
146 lines
5.0 KiB
Plaintext
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R Under development (unstable) (2024-04-17 r86441) -- "Unsuffered Consequences"
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Copyright (C) 2024 The R Foundation for Statistical Computing
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Platform: aarch64-unknown-linux-gnu
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> library(survival)
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> aeq <- function(x, y, ...) all.equal(as.vector(x), as.vector(y), ...)
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>
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> fit1 <- lm(skips ~ Opening + Solder + Mask + PadType + Panel,
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+ data=solder)
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> y1 <- yates(fit1, "Opening")
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>
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> temp <- levels(solder$Opening)
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> tpred <- matrix(0., nrow(solder), 3)
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> for (i in 1:3) {
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+ tdata <- solder
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+ tdata$Opening <- temp[i]
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+ tpred[,i] <- predict(fit1, newdata=tdata)
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+ }
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> all.equal(y1$estimate[,"pmm"], colMeans(tpred))
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[1] TRUE
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>
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> # This fit is deficient: there are no Opening=L and Mask=A6 obs
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> # The MPV for Mask=A6 and Opening L will therefore be NA, as well
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> # as for all levels of Solder, but we can compute the others.
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> # Solder will be NA for all levels
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> fit2 <- lm(skips ~ Opening*Mask + Solder,
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+ data=solder)
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> y2a <- yates(fit2, "Mask", population="factorial")
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> y2b <- yates(fit2, "Opening", population="factorial")
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> y2c <- yates(fit2, "Solder", population="factorial")
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>
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> # The predict.lm function gives correct predictions for estimable
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> # functions (all but L,A6) and nonsense for others. It knows that
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> # some are not estimable due to the NA coefficients, but not which ones,
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> # so always prints a warning. Hence the suppressWarnings call.
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> tdata <- do.call(expand.grid, fit2$xlevels[1:3])
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> temp <- levels(solder$Mask)
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> tpreda <- matrix(0., nrow(tdata), length(temp),
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+ dimnames=list(NULL, temp))
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> for (i in seq_along(temp)) {
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+ tdata$Mask <- temp[i]
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+ suppressWarnings(tpreda[,i] <- predict(fit2, newdata=tdata))
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+ }
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> tpreda[,"A6"] <- NA # the A6 estimate is deficient
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> aeq(y2a$estimate[,"pmm"], colMeans(tpreda))
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[1] TRUE
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>
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> tdata <- do.call(expand.grid, fit2$xlevels[1:3])
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> temp <- levels(solder$Opening)
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> tpredb <- matrix(0., nrow(tdata), length(temp),
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+ dimnames=list(NULL, temp))
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> for (i in seq_along(temp)) {
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+ tdata$Opening <- temp[i]
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+ suppressWarnings(tpredb[,i] <- predict(fit2, newdata=tdata))
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+ }
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> tpredb[,"L"] <- NA
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> aeq(y2b$estimate[,"pmm"], colMeans(tpredb))
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[1] TRUE
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>
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> # Solder should be all NA
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> all(is.na(y2c$estimate[,"pmm"]))
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[1] TRUE
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>
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> # Tests for Solder are defined for a non-factorial population, however.
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> # the [] below retains the factor structure of the variable, where the
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> # runs above did not. R gets prediction correct both ways.
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> y2d <- yates(fit2, ~Solder)
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> temp <- levels(solder$Solder)
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> tdata <- solder
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> tpredd <- matrix(0, nrow(tdata), length(temp),
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+ dimnames=list(NULL, temp))
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> for (i in seq_along(temp)) {
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+ tdata$Solder[] <- temp[i]
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+ suppressWarnings(tpredd[,i] <- predict(fit2, newdata=tdata))
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+ }
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> aeq(y2d$estimate$pmm, colMeans(tpredd))
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[1] TRUE
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>
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> #
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> # Verify that the result is unchanged by how dummies are coded
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> # The coefs move all over the map, but predictions are unchanged
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> fit3 <- lm(skips ~ C(Opening, contr.helmert)*Mask + C(Solder, contr.SAS),
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+ data=solder)
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> y3a <- yates(fit3, ~Mask, population='yates')
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> equal <- c("estimate", "test", "mvar")
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> all.equal(y3a[equal], y2a[equal])
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[1] TRUE
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>
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> tdata <- do.call(expand.grid, fit2$xlevels[1:3]) # use orignal variable names
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> temp <- levels(solder$Mask)
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> cpred <- matrix(0., nrow(tdata), length(temp),
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+ dimnames=list(NULL, temp))
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> for (i in seq_along(temp)) {
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+ tdata$Mask <- temp[i]
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+ suppressWarnings(cpred[,i] <- predict(fit3, newdata=tdata))
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+ }
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> aeq(cpred[, temp!="A6"], tpreda[, temp!= "A6"]) # same predictions
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[1] TRUE
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> all.equal(y3a$estimate, y2a$estimate)
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[1] TRUE
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>
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> y3b <- yates(fit3, ~Opening, population='yates')
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> # column names will differ
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> all.equal(y3b$estimate, y2b$estimate, check.attributes=FALSE)
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[1] TRUE
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>
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> y3d <- yates(fit3, ~Solder)
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> for (i in 1:3) {
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+ print(all.equal(y3d[[i]], y2d[[i]], check.attributes=FALSE))
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+ }
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[1] TRUE
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[1] TRUE
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[1] TRUE
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>
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> # Reprise this with a character variable in the model
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> sdata <- solder
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> sdata$Mask <- as.character(sdata$Mask)
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> fit4 <- lm(skips ~ Opening*Mask + Solder, data=sdata)
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> y4a <- yates(fit4, ~ Mask, population= "yates")
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> y4b <- yates(fit4, ~ Opening, population= "yates")
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> y4d <- yates(fit4, ~ Solder)
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> equal <- c("estimate", "tests", "mvar", "cmat")
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> all.equal(y2a[equal], y4a[equal]) # the "call" component differs
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[1] TRUE
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> all.equal(y2b[equal], y4b[equal])
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[1] TRUE
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> all.equal(y2d[equal], y4d[equal])
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[1] TRUE
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>
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> proc.time()
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user system elapsed
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0.490 0.008 0.495
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