Always keep the mailing list in cc.
The code runs for each row in the data. However I get the feeling that
there is a mismatch between what you think that is in the data and the
actual data.
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
2016-04-18 10:35 GMT+02:00 tan sj <sj_style_1...@outlook.com>:
> Thanks but it seem like the problem of looping through data is still the
> same....i am really wondering where is the mistake....
>
> ________________________________________
> From: Thierry Onkelinx <thierry.onkel...@inbo.be>
> Sent: Monday, April 18, 2016 7:21 AM
> To: tan sj
> Cc: r-help
> Subject: Re: [R] R [coding : do not run for every row ]
>
> You can make this much more readable with apply functions.
>
> result <- apply(
> all_combine1,
> 1,
> function(x){
> p.value <- sapply(
> seq_len(nSims),
> function(sim){
> gamma1 <- rgamma(x["m"], x["sp(skewness1.5)"], x["scp1"])
> gamma2 <- rgamma(x["n"], x["scp1"], 1)
> gamma1 <- gamma1 - x["sp(skewness1.5)"] * x["scp1"]
> gamma2 <- gamma2 - x["sp(skewness1.5)"]
> c(
> equal = t.test(gamma1, gamma2, var.equal=TRUE)$p.value,
> unequal = t.test(gamma1,gamma2,var.equal=FALSE)$p.value,
> mann = wilcox.test(gamma1,gamma2)$p.value
> )
> }
> )
> rowMeans(p.value <= alpha)
> }
> )
> cbind(all_combine1, t(result))
> 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
>
>
> 2016-04-18 9:05 GMT+02:00 tan sj <sj_style_1...@outlook.com>:
>> 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.onkel...@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_1...@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@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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