[R] R [coding : do not run for every row ]

[tan sj]

i have combined all the variables in a matrix, and i wish to conduct a simulation row by row.

But i found out the code only works for the every first row after a cycle of nine samples.

But after check out the code, i don know where is my mistake...

can anyone pls help ....


#For gamma disribution with equal skewness 1.5

#to evaluate the same R function on many different sets of data
library(parallel)

nSims<-100
alpha<-0.05

#set nrow =nsims because wan storing every p-value simulated
#for gamma distribution with equal skewness
matrix2_equal  <-matrix(0,nrow=nSims,ncol=3)
matrix5_unequal<-matrix(0,nrow=nSims,ncol=3)
matrix8_mann   <-matrix(0,nrow=nSims,ncol=3)

# 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
#forty five cases for each skewness!!
shp<-rep(16/9,each=5)

#scale parameter for sample 1
#scale paramter for sample 2 set as constant 1
scp1<-c(1,1.5,2,2.5,3)

#get all combinations with one row of the sample_sizes matrix
##(use expand.grid)to create a data frame from combination of data

ss_sd1<- expand.grid(sample_sizes[2,],shp)
scp1<-rep(scp1,9)

std2<-rep(sd2,9)

#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),ss_sd1,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,all_combine1[ss,3],all_combine1[ss,4])
        gamma2<-rgamma(n,all_combine1[ss,4],1)

        # minus the population mean to ensure that there is no lose of equality of mean
        gamma1<-gamma1-all_combine1[ss,3]*all_combine1[ss,4]
        gamma2<-gamma2-all_combine1[ss,3]

        #extract p-value out and store every p-value into matrix
        matrix2_equal[sim,1]<-t.test(gamma1,gamma2,var.equal=TRUE)$p.value
        matrix5_unequal[sim,2]<-t.test(gamma1,gamma2,var.equal=FALSE)$p.value
        matrix8_mann[sim,3] <-wilcox.test(gamma1,gamma2)$p.value
    }
       ##store the result
      equal[ss]<- mean(matrix2_equal[,1]<=alpha)
      unequal[ss]<-mean(matrix5_unequal[,2]<=alpha)
      mann[ss]<- mean(matrix8_mann[,3]<=alpha)
  }

g_equal<-cbind(all_combine1, equal, unequal, mann)

It is my result but it show a very big mistake ....TT
     m   n sp(skewness1.5) scp1 equal unequal mann
1   10  10        1.777778  1.0  0.36    0.34 0.34
2   10  25        1.777778  1.5  0.84    0.87 0.90
3   25  25        1.777778  2.0  1.00    1.00 1.00
4   25  50        1.777778  2.5  1.00    1.00 1.00
5   25 100        1.777778  3.0  1.00    1.00 1.00
6   50  25        1.777778  1.0  0.77    0.77 0.84
7   50 100        1.777778  1.5  1.00    1.00 1.00
8  100  25        1.777778  2.0  1.00    1.00 1.00
9  100 100        1.777778  2.5  1.00    1.00 1.00
10  10  10        1.777778  3.0  1.00    1.00 1.00
11  10  25        1.777778  1.0  0.48    0.30 0.55
12  25  25        1.777778  1.5  0.99    0.99 1.00
13  25  50        1.777778  2.0  1.00    1.00 1.00
14  25 100        1.777778  2.5  1.00    1.00 1.00
15  50  25        1.777778  3.0  1.00    1.00 1.00
16  50 100        1.777778  1.0  0.97    0.97 1.00
17 100  25        1.777778  1.5  1.00    1.00 1.00
18 100 100        1.777778  2.0  1.00    1.00 1.00
19  10  10        1.777778  2.5  1.00    1.00 1.00
20  10  25        1.777778  3.0  1.00    1.00 1.00
21  25  25        1.777778  1.0  0.63    0.63 0.71
22  25  50        1.777778  1.5  0.99    0.99 0.99
23  25 100        1.777778  2.0  1.00    1.00 1.00
24  50  25        1.777778  2.5  1.00    1.00 1.00
25  50 100        1.777778  3.0  1.00    1.00 1.00
26 100  25        1.777778  1.0  0.83    0.90 0.88
27 100 100        1.777778  1.5  1.00    1.00 1.00
28  10  10        1.777778  2.0  1.00    1.00 1.00
29  10  25        1.777778  2.5  1.00    1.00 1.00
30  25  25        1.777778  3.0  1.00    1.00 1.00
31  25  50        1.777778  1.0  0.71    0.66 0.81
32  25 100        1.777778  1.5  1.00    1.00 1.00
33  50  25        1.777778  2.0  1.00    1.00 1.00
34  50 100        1.777778  2.5  1.00    1.00 1.00
35 100  25        1.777778  3.0  1.00    1.00 1.00
36 100 100        1.777778  1.0  0.99    0.99 1.00
37  10  10        1.777778  1.5  0.65    0.65 0.71
38  10  25        1.777778  2.0  1.00    1.00 1.00
39  25  25        1.777778  2.5  1.00    1.00 1.00
40  25  50        1.777778  3.0  1.00    1.00 1.00
41  25 100        1.777778  1.0  0.90    0.89 0.96
42  50  25        1.777778  1.5  0.99    0.99 1.00
43  50 100        1.777778  2.0  1.00    1.00 1.00
44 100  25        1.777778  2.5  1.00    1.00 1.00
45 100 100        1.777778  3.0  1.00    1.00 1.00
>



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