[R] R [coding : do not run for every row ]
Thierry Onkelinx
thierry.onkelinx at inbo.be
Mon Apr 18 08:54:56 CEST 2016
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 op 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
>>
>
>
>
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>
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