吴裕雄--天生自然 R语言开发学习：功效分析

待我称王封你为后i 2021-11-05 10:31 302阅读 0赞

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#----------------------------------------#
    # 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)

转载于:https://www.cnblogs.com/tszr/p/11175713.html

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