我已将矩阵中的所有变量组合在一起，我希望逐行进行模拟。 但我发现代码只适用于九个样本，而不是全部（45）。 我试过，循环是迭代的，但由于这两行，所以会出现问题。
#minus the population mean to ensure the true of null hypo
gamma1<-gamma1-16/9*all_combine1[ss,4]
gamma2<-gamma2-16/9
任何人都可以帮助....请
#For gamma disribution with equal skewness 1.5
#to evaluate the same R function on many different sets of data
library(parallel)
nSims<-100 #number of simulation
alpha<-0.05 #significance level
#set nrow =nsims because wan storing every p-value simulated
matrix3_equal <-matrix(0,nrow=nSims,ncol=3)
matrix4_unequal<-matrix(0,nrow=nSims,ncol=3)
matrix5_mann <-matrix(0,nrow=nSims,ncol=3)
#set empty vector of length to store p-value
equal3<-c(rep(0,nrow(all_combine1)))
unequal4<-c(rep(0,nrow(all_combine1)))
mann5<-c(rep(0,nrow(all_combine1)))
#for gamma distribution with equal skewness
# to ensure the reproducity of the result
#here we declare the random seed generator
set.seed(1)
## Put the samples sizes into matrix then use a loop for sample sizes
sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100),
nrow=2)
#shape parameter for both gamma distribution for equal skewness
shp<-rep(16/9,each=45)
#scale parameter for sample 1
#scale paramter for sample 2 set as constant 1
scp1<-c(1,1.5,2,2.5,3)
scp1<-rep(scp1,9)
#create a matrix combining the forty five cases of combination of sample sizes,shape and scale parameter
all_combine1 <- cbind(rep(sample_sizes[1,], 5),rep(sample_sizes[2,],5),shp,scp1)
# name the column samples 1 and 2 and standard deviation
colnames(all_combine1) <- c("m", "n","sp(skewness1.5)","scp1")
##for the samples sizes into matrix then use a loop for sample sizes
# this loop steps through the all_combine matrix
for(ss in 1:nrow(all_combine1))
{
#generate samples from the first column and second column
m<-all_combine1[ss,1]
n<-all_combine1[ss,2]
for (sim in 1:nSims)
{
#generate 2 random samples from gamma distribution with equal skewness
gamma1<-rgamma(m,16/9,all_combine1[ss,4])
gamma2<-rgamma(n,16/9,1)
#minus the population mean to ensure the true of null hypo
gamma1<-gamma1-16/9*all_combine1[ss,4]
gamma2<-gamma2-16/9
#extract p-value out and store every p-value into matrix
matrix3_equal[sim,1]<-t.test(gamma1,gamma2,var.equal=TRUE)$p.value
matrix4_unequal[sim,2]<-t.test(gamma1,gamma2,var.equal=FALSE)$p.value
matrix5_mann[sim,3] <-wilcox.test(gamma1,gamma2)$p.value
}
##store the result
equal3[ss]<- sum(matrix3_equal[,1]<=alpha)
unequal4[ss]<-sum(matrix4_unequal[,2]<=alpha)
mann5[ss]<- sum(matrix5_mann[,3]<=alpha)
}
这是我的结果。很明显，每次循环都没有成功运行.TT
m n sp(skewness1.5) scp1 equal3 unequal4 mann5
[1,] 10 10 1.777778 1.0 9 9 6
[2,] 10 25 1.777778 1.5 94 93 95
[3,] 25 25 1.777778 2.0 100 100 100
[4,] 25 50 1.777778 2.5 100 100 100
[5,] 25 100 1.777778 3.0 100 100 100
[6,] 50 25 1.777778 1.0 3 8 6
[7,] 50 100 1.777778 1.5 100 100 100
[8,] 100 25 1.777778 2.0 100 100 100
[9,] 100 100 1.777778 2.5 100 100 100
[10,] 10 10 1.777778 3.0 100 100 100
[11,] 10 25 1.777778 1.0 3 4 6
[12,] 25 25 1.777778 1.5 99 99 100
[13,] 25 50 1.777778 2.0 100 100 100
[14,] 25 100 1.777778 2.5 100 100 100
[15,] 50 25 1.777778 3.0 100 100 100
[16,] 50 100 1.777778 1.0 3 4 1
[17,] 100 25 1.777778 1.5 100 100 100
[18,] 100 100 1.777778 2.0 100 100 100
[19,] 10 10 1.777778 2.5 100 100 100
[20,] 10 25 1.777778 3.0 100 100 100
[21,] 25 25 1.777778 1.0 4 3 5
[22,] 25 50 1.777778 1.5 100 99 100
[23,] 25 100 1.777778 2.0 100 100 100
[24,] 50 25 1.777778 2.5 100 100 100
[25,] 50 100 1.777778 3.0 100 100 100
[26,] 100 25 1.777778 1.0 8 9 10
[27,] 100 100 1.777778 1.5 100 100 100
[28,] 10 10 1.777778 2.0 100 100 100
[29,] 10 25 1.777778 2.5 100 100 100
[30,] 25 25 1.777778 3.0 100 100 100
[31,] 25 50 1.777778 1.0 2 3 2
[32,] 25 100 1.777778 1.5 100 100 100
[33,] 50 25 1.777778 2.0 100 100 100
[34,] 50 100 1.777778 2.5 100 100 100
[35,] 100 25 1.777778 3.0 100 100 100
[36,] 100 100 1.777778 1.0 7 7 5
[37,] 10 10 1.777778 1.5 88 87 90
[38,] 10 25 1.777778 2.0 100 100 100
[39,] 25 25 1.777778 2.5 100 100 100
[40,] 25 50 1.777778 3.0 100 100 100
[41,] 25 100 1.777778 1.0 7 7 6
[42,] 50 25 1.777778 1.5 100 100 100
[43,] 50 100 1.777778 2.0 100 100 100
[44,] 100 25 1.777778 2.5 100 100 100
[45,] 100 100 1.777778 3.0 100 100 100
答案 0 :(得分：1)
我相信你的错误在于以下几行：
##store the result
equal[ss]<- mean(matrix2_equal[,1]<=alpha)
unequal[ss]<-mean(matrix5_unequal[,2]<=alpha)
mann[ss]<- mean(matrix8_mann[,3]<=alpha)
matrix2_equal [，1]＆lt; = alpha将返回true或false值，因此mean（matrix2_equal [，1]＆lt; = alpha）基本上从模型中返回True的％。这可能是你想要的：
equal[ss]<- mean(matrix2_equal[matrix2_equal[,1]<=alpha, 1])
仅供参考：此问题与您之前发布的帖子有关：R： coding why show 0.00 in result
答案 1 :(得分：1)
@ Dave2e注释掉这两行会得到以下结果。
m n scp equal3 unequal4 mann5
[1,] 10 10 1.0 8 8 9
[2,] 10 25 1.5 16 36 23
[3,] 25 25 2.0 83 82 78
[4,] 25 50 2.5 100 100 100
[5,] 25 100 3.0 100 100 100
[6,] 50 25 1.0 3 5 7
[7,] 50 100 1.5 82 86 79
[8,] 100 25 2.0 98 92 91
[9,] 100 100 2.5 100 100 100
[10,] 10 10 3.0 76 72 77
[11,] 10 25 1.0 1 3 3
[12,] 25 25 1.5 44 42 37
[13,] 25 50 2.0 94 96 92
[14,] 25 100 2.5 100 100 100
[15,] 50 25 3.0 100 100 100
[16,] 50 100 1.0 4 4 3
[17,] 100 25 1.5 72 54 56
[18,] 100 100 2.0 100 100 100
[19,] 10 10 2.5 65 60 57
[20,] 10 25 3.0 90 98 95
[21,] 25 25 1.0 2 2 5
[22,] 25 50 1.5 48 61 50
[23,] 25 100 2.0 95 96 93
[24,] 50 25 2.5 100 99 98
[25,] 50 100 3.0 100 100 100
[26,] 100 25 1.0 5 6 2
[27,] 100 100 1.5 100 100 95
[28,] 10 10 2.0 50 49 49
[29,] 10 25 2.5 79 92 85
[30,] 25 25 3.0 99 99 99
[31,] 25 50 1.0 6 3 6
[32,] 25 100 1.5 58 76 54
[33,] 50 25 2.0 94 91 90
[34,] 50 100 2.5 100 100 100
[35,] 100 25 3.0 100 100 100
[36,] 100 100 1.0 3 3 4
[37,] 10 10 1.5 22 20 13
[38,] 10 25 2.0 45 70 55
[39,] 25 25 2.5 97 97 95
[40,] 25 50 3.0 100 100 100
[41,] 25 100 1.0 5 5 5
[42,] 50 25 1.5 62 48 52
[43,] 50 100 2.0 100 100 100
[44,] 100 25 2.5 100 100 100
[45,] 100 100 3.0 100 100 100