吴裕雄--天生自然 R语言开发学习：功效分析（续一）

#----------------------------------------#

# R in Action (2nd ed): Chapter 10       #

# Power analysis                         #

# requires packages pwr to be installed  #

# install.packages("pwr")                #

#----------------------------------------#

par(ask=TRUE)

library(pwr)

# t tests

pwr.t.test(d=.8, sig.level=.05,power=.9, type="two.sample",

           alternative="two.sided")

pwr.t.test(n=20, d=.5, sig.level=.01, type="two.sample",

           alternative="two.sided")

# ANOVA

pwr.anova.test(k=5,f=.25,sig.level=.05,power=.8)

# Correlations

pwr.r.test(r=.25, sig.level=.05, power=.90, alternative="greater")

# Linear Models

pwr.f2.test(u=3, f2=0.0769, sig.level=0.05, power=0.90)

# Tests of proportions

pwr.2p.test(h=ES.h(.65, .6), sig.level=.05, power=.9,

            alternative="greater")

# Chi-square tests

prob <- matrix(c(.42, .28, .03, .07, .10, .10), byrow=TRUE, nrow=3)

ES.w2(prob)

pwr.chisq.test(w=.1853, df=3 , sig.level=.05, power=.9)

# Listing 10.1 - Sample sizes for detecting significant effects in a One-Way ANOVA

es <- seq(.1, .5, .01)

nes <- length(es)

samsize <- NULL

for (i in 1:nes){

  result <- pwr.anova.test(k=5, f=es[i], sig.level=.05, power=.9)

  samsize[i] <- ceiling(result$n)

}

plot(samsize,es, type="l", lwd=2, col="red",

     ylab="Effect Size",

     xlab="Sample Size (per cell)",

     main="One Way ANOVA with Power=.90 and Alpha=.05")

# Listing 10.2 - Sample size curves for dtecting corelations of various sizes

library(pwr)

r <- seq(.1,.5,.01)

nr <- length(r)

p <- seq(.4,.9,.1)

np <- length(p)

samsize <- array(numeric(nr*np), dim=c(nr,np))

for (i in 1:np){

  for (j in 1:nr){

    result <- pwr.r.test(n = NULL, r = r[j],

                         sig.level = .05, power = p[i],

                         alternative = "two.sided")

    samsize[j,i] <- ceiling(result$n)

  }

}

xrange <- range(r)

yrange <- round(range(samsize))

colors <- rainbow(length(p))

plot(xrange, yrange, type="n",

     xlab="Correlation Coefficient (r)",

     ylab="Sample Size (n)" )

for (i in 1:np){

  lines(r, samsize[,i], type="l", lwd=2, col=colors[i])

}

abline(v=0, h=seq(0,yrange[2],50), lty=2, col="grey89")

abline(h=0, v=seq(xrange[1],xrange[2],.02), lty=2, col="gray89")

title("Sample Size Estimation for Correlation Studies\n

Sig=0.05 (Two-tailed)")

legend("topright", title="Power", as.character(p),

       fill=colors)

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