吴裕雄--天生自然 R语言开发学习：重抽样与自助法（续一）

#-------------------------------------------------------------------------#
# R in Action (2nd ed): Chapter 12                                        #
# Resampling statistics and bootstrapping                                 #
# requires packages coin, multcomp, vcd, MASS, lmPerm, boot               #
# install.packages(c("coin","multcomp", "vcd", "MASS", "boot"))           #
# Follow chapter instructions for installing lmPerm                       #
#-------------------------------------------------------------------------#

par(ask=TRUE)

# Listing 12.1 - t-test vs. oneway permutation test for the hypothetical data
library(coin)
score <- c(40, 57, 45, 55, 58, 57, 64, 55, 62, 65)
treatment <- factor(c(rep("A",5), rep("B",5)))
mydata <- data.frame(treatment, score)
t.test(score~treatment, data=mydata, var.equal=TRUE)
oneway_test(score~treatment, data=mydata, distribution="exact")

# Wilcoxon Mann-Whitney U test
UScrime <- transform(UScrime, So = factor(So))
wilcox_test(Prob ~ So, data=UScrime, distribution="exact")

# k sample test
library(multcomp)
set.seed(1234) # make results reproducible
oneway_test(response~trt, data=cholesterol,
            distribution=approximate(B=9999))

# independence in contingency tables
library(coin)
library(vcd)
Arthritis <- transform(Arthritis,
                       Improved = as.factor(as.numeric(Improved)))
set.seed(1234)
chisq_test(Treatment~Improved, data=Arthritis,
           distribution=approximate(B=9999))

# independence between numeric variables
states <- as.data.frame(state.x77)
set.seed(1234)
spearman_test(Illiteracy ~ Murder, data=states,
              distribution=approximate(B=9999))

# dependent 2-sample and k-sample tests
library(coin)
library(MASS)
wilcoxsign_test(U1 ~ U2, data=UScrime, distribution="exact")

# Listing 12.2 - Permutation tests for simple linear regression
library(lmPerm)
set.seed(1234)
fit <- lmp(weight ~ height, data=women, perm="Prob")
summary(fit)

# Listing 12.3 - Permutation tests for polynomial regression
library(lmPerm)
set.seed(1234)
fit <- lmp(weight ~ height + I(height^2), data=women, perm="Prob")
summary(fit)

# Listing 12.4 - Permutation tests for multiple regression
library(lmPerm)
set.seed(1234)
states <- as.data.frame(state.x77)
fit <- lmp(Murder ~ Population + Illiteracy+Income+Frost,data=states, perm="Prob")
summary(fit)

# Listing 12.5 - Permutation test for One-Way ANOVA
library(lmPerm)
library(multcomp)
set.seed(1234)
fit <- lmp(response ~ trt, data=cholesterol, perm="Prob")
anova(fit)

#  Listing 12.6 - Permutation test for One-Way ANCOVA
library(lmPerm)
set.seed(1234)
fit <- lmp(weight ~ gesttime + dose, data=litter, perm="Prob")
anova(fit)

# Listing 12.7 - Permutation test for Two-way ANOVA
library(lmPerm)
set.seed(1234)
fit <- lmp(len ~ supp*dose, data=ToothGrowth, perm="Prob")
anova(fit)

# bootstrapping a single statistic (R2)
rsq <- function(formula, data, indices) {
  d <- data[indices,]
  fit <- lm(formula, data=d)
  return(summary(fit)$r.square)
} 

library(boot)
set.seed(1234)
results <- boot(data=mtcars, statistic=rsq,
                R=1000, formula=mpg~wt+disp)
print(results)
plot(results)
boot.ci(results, type=c("perc", "bca"))

# bootstrapping several statistics (regression coefficients)
bs <- function(formula, data, indices) {
  d <- data[indices,]
  fit <- lm(formula, data=d)
  return(coef(fit))
}
library(boot)
set.seed(1234)
results <- boot(data=mtcars, statistic=bs,
                R=1000, formula=mpg~wt+disp) 

print(results)
plot(results, index=2)
boot.ci(results, type="bca", index=2)
boot.ci(results, type="bca", index=3)