#---------------------------------------------------- #永田(2003)第6章 2つの母分散の比の検定 #---------------------------------------------------- ################################ #2つの母分散の比の検定(検出力) #(1) sigma_1 =/ sigma_2 ################################ power.sigma1 <- function(delta,sig.level,n1,n2){ pf(1/(delta^2*qf(sig.level/2,df1=n2-1,df2=n1-1,lower.tail=F)),df1=n1-1,df2=n2-1,lower.tail=T)+ pf(qf(sig.level/2,df2=n2-1,df1=n1-1,lower.tail=F)/delta^2,df1=n1-1,df2=n2-1,lower.tail=F) } power.sigma1(n1=10,n2=8,delta=2,sig.level=.05) ################################ #2つの母分散の比の検定(検出力) #(2) sigma_1 > sigma_2 ################################ power.sigma2 <- function(delta,sig.level,n1,n2){ pf(qf(sig.level,df2=n2-1,df1=n1-1,lower.tail=F)/delta^2,df1=n1-1,df2=n2-1,lower.tail=F) } power.sigma2(n1=10,n2=8,delta=2,sig.level=.05) ################################ #2つの母分散の比の検定(検出力) #(3) sigma_1 < sigma_2 ################################ power.sigma3 <- function(delta,sig.level,n1,n2){ pf(1/(delta^2*qf(sig.level,df1=n2-1,df2=n1-1,lower.tail=F)),df1=n1-1,df2=n2-1,lower.tail=T) } power.sigma3(n1=10,n2=8,delta=.5,sig.level=.05) ################################ #2つの母分散の比の検定(サンプルサイズ) #(1) sigma_1 =/ sigma_2 ################################ sample.sigma <- function(delta,sig.level,power){ 1+((qnorm(sig.level/2,lower.tail=F)-qnorm(power,lower.tail=F))/ log(delta))^2 } sample.sigma(delta=5/2,sig.level=0.05,power=0.9) ################################ #2つの母分散の比の検定(サンプルサイズ) #(2) sigma_1 > sigma_2 ################################ sample.sigma2 <- function(delta,sig.level,power){ 1+((qnorm(sig.level,lower.tail=F)-qnorm(power,lower.tail=F))/ log(delta))^2 } sample.sigma2(delta=2,sig.level=0.05,power=0.8) power.sigma2(delta=2,n1=14,n2=14,sig.level=.05) power.sigma2(delta=2,n1=15,n2=15,sig.level=.05) ################################ #2つの母分散の比の検定(サンプルサイズ) #(3) sigma_1 < sigma_2 ################################ sample.sigma3 <- function(delta,sig.level,power){ 1+((qnorm(sig.level,lower.tail=F)-qnorm(power,lower.tail=F))/ log(delta))^2 } sample.sigma3(delta=1/3,sig.level=0.05,power=0.95) power.sigma3(delta=1/3,n1=10,n2=10,sig.level=.05) power.sigma3(delta=1/3,n1=11,n2=11,sig.level=.05) ##ex 6.2 delta <- seq(0,3.5,by=.01) plot(power.sigma1(n1=10,n2=8,delta=delta,sig.level=.05)~delta,type="l") lines(power.sigma1(n1=21,n2=16,delta=delta,sig.level=.05)~delta,type="l") lines(power.sigma1(n1=31,n2=25,delta=delta,sig.level=.05)~delta,type="l") ##ex 6.4 delta <- seq(0,3.5,by=.01) plot(power.sigma2(n1=10,n2=8,delta=delta,sig.level=.05)~delta,type="l") lines(power.sigma2(n1=21,n2=16,delta=delta,sig.level=.05)~delta,type="l") lines(power.sigma2(n1=31,n2=25,delta=delta,sig.level=.05)~delta,type="l") ##ex 6.6 delta <- seq(0,3.5,by=.01) plot(power.sigma3(n1=10,n2=8,delta=delta,sig.level=.05)~delta,type="l") lines(power.sigma3(n1=21,n2=16,delta=delta,sig.level=.05)~delta,type="l") lines(power.sigma3(n1=31,n2=25,delta=delta,sig.level=.05)~delta,type="l") ##ex 6.7 sample.sigma(delta=1/2,sig.level=0.05,power=0.9)