________________________________________
From: Thierry Onkelinx <thierry.onkelinx at 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_1125 at 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.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
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