91 lines
2.7 KiB
R
91 lines
2.7 KiB
R
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# faq7_31
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# FAQ 7.31
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a0 <- 2
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b <- sqrt(a0)
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a1 <- b ^ 2
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identical(a0, a1)
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a0 - a1
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a <- c(a0, a1)
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# hexadecimal representation
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sprintf("%a", a)
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# faq7_31digest
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library(digest)
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# different hashes with digest
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sapply(a, digest, algo = "sha1")
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# same hash with sha1 with default digits (14)
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sapply(a, sha1)
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# larger digits can lead to different hashes
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sapply(a, sha1, digits = 15)
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# decreasing the number of digits gives a stronger truncation
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# the hash will change when then truncation gives a different result
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# case where truncating gives same hexadecimal value
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sapply(a, sha1, digits = 13)
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sapply(a, sha1, digits = 10)
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# case where truncating gives different hexadecimal value
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c(sha1(pi), sha1(pi, digits = 13), sha1(pi, digits = 10))
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# sha1_lm_sum
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# taken from the help file of lm.influence
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lm_SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
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lm_sum <- summary(lm_SR)
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class(lm_sum)
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# str() gives the structure of the lm object
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str(lm_sum)
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# extract the coefficients and their standard error
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coef_sum <- coef(lm_sum)[, c("Estimate", "Std. Error")]
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# extract sigma
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sigma <- lm_sum$sigma
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# check the class of each component
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class(coef_sum)
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class(sigma)
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# sha1() has methods for both matrix and numeric
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# because the values originate from floating point arithmetic it is better to use a low number of digits
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sha1(coef_sum, digits = 4)
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sha1(sigma, digits = 4)
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# we want a single hash
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# combining the components in a list is a solution that works
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sha1(list(coef_sum, sigma), digits = 4)
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# now turn everything into an S3 method
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# - a function with name "sha1.classname"
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# - must have the same arguments as sha1()
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sha1.summary.lm <- function(x, digits = 4, zapsmall = 7){
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coef_sum <- coef(x)[, c("Estimate", "Std. Error")]
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sigma <- x$sigma
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combined <- list(coef_sum, sigma)
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sha1(combined, digits = digits, zapsmall = zapsmall)
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}
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sha1(lm_sum)
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# try an altered dataset
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LCS2 <- LifeCycleSavings[rownames(LifeCycleSavings) != "Zambia", ]
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lm_SR2 <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LCS2)
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sha1(summary(lm_SR2))
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# sha1_lm
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class(lm_SR)
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# str() gives the structure of the lm object
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str(lm_SR)
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# extract the model and the terms
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lm_model <- lm_SR$model
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lm_terms <- lm_SR$terms
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# check their class
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class(lm_model) # handled by sha1()
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class(lm_terms) # not handled by sha1()
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# define a method for formula
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sha1.formula <- function(x, digits = 14, zapsmall = 7, ..., algo = "sha1"){
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sha1(as.character(x), digits = digits, zapsmall = zapsmall, algo = algo)
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}
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sha1(lm_terms)
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sha1(lm_model)
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# define a method for lm
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sha1.lm <- function(x, digits = 14, zapsmall = 7, ..., algo = "sha1"){
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lm_model <- x$model
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lm_terms <- x$terms
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combined <- list(lm_model, lm_terms)
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sha1(combined, digits = digits, zapsmall = zapsmall, ..., algo = algo)
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}
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sha1(lm_SR)
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sha1(lm_SR2)
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