<br />
# 第10章 功效分析<br />
# 10.1 假设检验速览<br />
# 10.2 用pwr包做功效分析<br />
# 10.3 绘制功效分析图形<br />
# 10.4 其他软件包<br />
# 10.5 小结</p>
<p># 10.1 假设检验速览<br />
# -----------------------------------------------------------------------<br />
# 预先约定的阈值（0.05）称为检验的显著性水平（significance level）<br />
# I型错误<br />
# 样本大小、显著性水平、功效和效应值<br />
# 功效通过1减去II型错误的概率来定义</p>
<p># 10.2 用pwr包做功效分析<br />
# 10.2.1 t检验<br />
# 10.2.2 方差分析<br />
# 10.2.3 相关性<br />
# 10.2.4 线性模型<br />
# 10.2.5 比例检验<br />
# 10.2.6 卡方检验<br />
# 10.2.7 在新情况中选择合适的效应值<br />
# ----------------------------------------------------------------------<br />
# 表10-1 pwr包中的函数<br />
# pwr.2p.test() pwr.2p2n.test() pwr.anova.test() pwr.chisq.test()<br />
# pwr.f2.test() pwr.p.test() pwr.r.test() pwr.t.test()<br />
# pwr.t.test() pwr.t2n.test()</p>
<p>install.packages("pwr")<br />
## Warning in install.packages :<br />
## package ‘pwr’ is not available (for R version 3.0.1)</p>
<p>library(pwr)<br />
pwr.t.test(d=.8, sig.level=.05, power=.9, type="two.sample",<br />
alternative="two.sided")<br />
pwr.t.test(n=20, d=.5, sig.level=.01, type="two.sample",<br />
alternative="two.sided")</p>
<p>pwr.anova.test(k=5, f=.25, sig.level=.05, power=.8)</p>
<p>pwr.r.test(r=.25, sig.level=.05, power=.90, alternative="greater")</p>
<p>pwr.f2.test(u=3, f2=.0769, sig.level=.05, power=.90)</p>
<p>pwr.2p.test(h=ES.h(.65, .6), sig.level=.05, power=.9,<br />
alternative="greater")</p>
<p>prob <- matrix(c(.42, .28, .03, .07, .10, .10), byrow=TRUE, nrow=3)<br />
pwr.chisq.test(w=ES.w2(prob), df=2, sig.level=.05, power=.9)</p>
<p># 表10-3 Cohen效应值基准<br />
# 代码清单10-1 单因素ANOVA中检测显著效应所需的样本大小<br />
library(pwr)<br />
es <- seq(.1, .5, .01)<br />
nes <- length(es)<br />
samsize <- NULL<br />
for (i in 1:nes){<br />
result <- pwr.anova.test(k=5, f=es[i], sig.level=.05, power=.9)<br />
samsize[i] <- ceiling(result$n)<br />
}<br />
plot(samsize, es, type="l", lwd=2, col="red",<br />
ylab="Effect Size",<br />
xlab="Sample Size (per cell)",<br />
main="One Way ANOVA with Power=.9 and Alpha=.05")</p>
<p># 10.3 绘制功效分析图形<br />
# ---------------------------------------------------------------------<br />
# 代码清单10-2 检验各种效应值下的相关性所需的样本量曲线<br />
library(pwr)<br />
r <- seq(.1, .5, .01)<br />
nr <- length(r)</p>
<p>p <- seq(.4, .9, .1)<br />
np <- length(p)</p>
<p>samsize <- array(numeric(nr*np), dim=c(nr,np))<br />
for (i in 1:np){<br />
for (j in 1:nr){<br />
result <- pwr.r.test(n=NULL, r=r[j],<br />
sig.level=.05, power=p[i],<br />
alternative="two.sided")<br />
samsize[j, i] <- ceiling(result$n)<br />
}<br />
}<br />
xrange <- range(r)<br />
yrange <- round(range(samsize))<br />
colors <- rainbow(length(p))<br />
plot(xrange, yrange, type="n",<br />
xlab="Correlation Coefficient (r)",<br />
ylab="Sample Size (n)")</p>
<p>for (i in 1:np){<br />
lines(r, samsize[,i], type="l", lwd=2, col=colors[i])<br />
}</p>
<p>abline(v=0, h=seq(0, yrange[2], 50), lty=2, col="grey89")<br />
abline(h=0, v=seq(xrange[1], xrange[2], .02), lty=2, col="gray89")<br />
title("Sample Size Estimation for Correlation Studies\n<br />
Sig=0.05 (Two-tailed)")<br />
legend("topright", title="Power", as.character(p),<br />
fill=colors)</p>
<p># 10.4 其他软件包<br />
# ----------------------------------------------------------------------<br />
# piface包<br />
install.packages("piface", repos="http://R-Forge.R-project.org")<br />
library(piface)<br />
piface()<br />
# 表10-4 专业化的功效分析软件包<br />
# asypow PerGSD pamm powerSurvEpi powerpkg<br />
# powerGWASinteraction pedantics gap ssize.fdr</p>
<p># 10.5 小结<br />
# ----------------------------------------------------------------------<br />
</p>
