Hi Jeem, First, please send questions like this to the help list, not me. I assume that you are in a similar position to sjtan who has been sending almost exactly the same questions. The problem is not in the loops (which look rather familiar to me) but in your initial assignments at the top. For instance: scale parameter=(1,1.5,2,2.5,3) produces an error which has nothing to do with the loops. This is a very basic mistake, for: scale_parameter<-c(1,1.5,2,2.5,3) fixes it. I think if you learn a bit about basic R coding you will be able to fix these problems yourself. Jim
On Tue, Apr 19, 2016 at 4:03 AM, <uk31429 at student.umt.edu.my> wrote:
Greeting dr jim,
I am student from Malaysia. I am doing R simulation study. For your
information, I have been written a code relating to 2 gamma distribution
with equal skewness.
skewness=1.0
shape parameter=16/9
scale parameter=(1,1.5,2,2.5,3)
Below are my coding, however, the code have some error and yet after
trying this and that for a whole day, i couldn't spot the mistake. The
output should be greater or smaller than 0.05 while no exceeding too much
.But the for loop only able to function for scale parameter 1 .I try to
apply another for loop for the scale parameter, but the output become
worsen, as the simulation will even get hang. all of the output is 100.
Please, could you give me some advice ?
#For gamma disribution with equal skewness 1.5
rm(list=ls())
nSims<-100
alpha<-0.05
#here we declare the random seed generator
set.seed(3)
## 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
d1<-matrix(c(1,1.5,2,2.5,3),ncol=1)
scp<-rep(d1,9)
#create a matrix combining the forty five cases of combination of sample
sizes,shape and scale parameter
all<- cbind(rep(sample_sizes[1,],5),rep(sample_sizes[2,],5),scp)
# name the column samples 1 and 2 and standard deviation
colnames(all) <- c("m", "n","scp")
#set empty vector of length to store p-value
equal3<-rep(0,nrow(all))
unequal4<-rep(0,nrow(all))
mann5<-rep(0,nrow(all))
#set nrow =nsims because wan storing every p-value simulated
#for gamma distribution with equal skewness
matrix3_equal <-matrix(0,nrow=nSims,ncol=3)
matrix4_unequal<-matrix(0,nrow=nSims,ncol=3)
matrix5_mann <-matrix(0,nrow=nSims,ncol=3)
# this loop steps through the all_combine matrix
for(ss in 1:nrow(all))
{ #generate samples from the first column and second column
m<-all[ss,1]
n<-all[ss,2]
for ( sim in 1:nSims)
{
#generate 2 random samples from gamma distribution with equal
skewness
gamma1<-rgamma(m,16/9,all[ss,3])
gamma2<-rgamma(n,16/9,1)
#minus population mean from each sample to maintain the equality of
null #hypotheses (population mean =scale parameter *shape
parameter)
gamma1<-gamma1-16/9*all[ss,3]
gamma2<-gamma2-16/9
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)
}
g<-cbind(all, equal3, unequal4, mann5)
I will be really appreciated for any respond .Thanks.
Your sincerely
Jeem
Computational Mathematics
School of Informatics and Applied Mathematics
Universiti Malaysia Terengganu
21030 Kuala Terengganu, Terengganu, Malaysia
Phone : 017-7799039
Email : uk31429 at student.umt.edu.my