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tan sj

2016-Apr-17 17:59 UTC

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

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>


	[[alternative HTML version deleted]]

Thierry Onkelinx

2016-Apr-18 06:54 UTC

head link

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

Dear anonymous,

The big mistake in the output might be obvious to you but not to
others. Please make clear what the correct output should be or at
least what is wrong with the current output.

And please DO read the posting guide which asks you not to post in HTML.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey


2016-04-17 19:59 GMT+02:00 tan sj <sj_style_1125 at
outlook.com>:> 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
>>
>
>
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

tan sj

2016-Apr-18 07:05 UTC

head link

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

Hi, i am sorry, the output should be values between 0 and 0.1 and not supposed
to be 1.00, it is because they are type 1 error rate. And now i get output 1.00
for several samples,rhis is no correct. The loop do not run for every row. i do
not know where is my mistake.  As i use the same concept on normal distribution
setup, i get the result.

Sent from my phone

On Thierry Onkelinx <thierry.onkelinx at inbo.be>, Apr 18, 2016 2:55 PM
wrote:
Dear anonymous,

The big mistake in the output might be obvious to you but not to
others. Please make clear what the correct output should be or at
least what is wrong with the current output.

And please DO read the posting guide which asks you not to post in HTML.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey


2016-04-17 19:59 GMT+02:00 tan sj <sj_style_1125 at
outlook.com>:> 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
>>
>
>
>
>         [[alternative HTML version deleted]]
>
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