RinA CH10 Code.txt

#--------------------------------------------------#
# R in Action: Chapter 10                          #
# requires that the pwr package has been installed #
# install.packages('pwr')                          #
#--------------------------------------------------#

# pause on each graph
par(ask = TRUE)

# load required package
library(pwr)

# t tests

pwr.t.test(d = 0.8, sig.level = 0.05, power = 0.9, 
    type = "two.sample", alternative = "two.sided")

pwr.t.test(n = 20, d = 0.5, sig.level = 0.01, 
    type = "two.sample", 
    alternative = "two.sided")

# ANOVA

pwr.anova.test(k = 5, f = 0.25, sig.level = 0.05, 
    power = 0.8)

# Correlations

pwr.r.test(r = 0.25, sig.level = 0.05, power = 0.9, 
    alternative = "greater")

# Linear Models

pwr.f2.test(u = 3, f2 = 0.0769, sig.level = 0.05, 
    power = 0.9)

# Tests of proportions
pwr.2p.test(h = ES.h(0.65, 0.6), sig.level = 0.05, 
    power = 0.9, alternative = "greater")

# Chi-square tests

prob <- matrix(c(0.42, 0.28, 0.03, 0.07, 0.1, 0.1), 
    byrow = TRUE, nrow = 3)
ES.w2(prob)
pwr.chisq.test(w = 0.1853, df = 3, sig.level = 0.05, 
    power = 0.9)

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

es <- seq(0.1, 0.5, 0.01)
nes <- length(es)
samsize <- NULL
for (i in 1:nes) {
    result <- pwr.anova.test(k = 5, f = es[i], 
        sig.level = 0.05, 
        power = 0.9)
    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(0.1, 0.5, 0.01)
nr <- length(r)
p <- seq(0.4, 0.9, 0.1)
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 = 0.05, 
            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], 0.02), 
    lty = 2, col = "grey89")
title("Sample Size Estimation for Correlation Studies\nSig=0.05 (Two-tailed)")
legend("topright", title = "Power", as.character(p), 
    fill = colors)