R [Gamma-Verteilungsskala-Parameter und t-Test]

Question

Ich habe alle Variablen in einer Matrix kombiniert und möchte Zeile für Zeile eine Simulation durchführen. Aber ich fand heraus, der Code funktioniert nur für neun Proben und nicht alle von ihnen (45). Ich habe versucht, die Schleife iteriert aber wegen dieser zwei Linien, so Problem auftritt.

#minus the population mean to ensure the true of null hypo 
      gamma1<-gamma1-16/9*all_combine1[ss,4] 
      gamma2<-gamma2-16/9 

kann jemand pls help .... bitte

#For gamma disribution with equal skewness 1.5 

#to evaluate the same R function on many different sets of data 
library(parallel) 

nSims<-100 #number of simulation 
alpha<-0.05 #significance level 

#set nrow =nsims because wan storing every p-value simulated 
matrix3_equal <-matrix(0,nrow=nSims,ncol=3) 
matrix4_unequal<-matrix(0,nrow=nSims,ncol=3) 
matrix5_mann <-matrix(0,nrow=nSims,ncol=3) 
#set empty vector of length to store p-value 
equal3<-c(rep(0,nrow(all_combine1))) 
unequal4<-c(rep(0,nrow(all_combine1))) 
mann5<-c(rep(0,nrow(all_combine1))) 

#for gamma distribution with equal skewness 
# 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 
shp<-rep(16/9,each=45) 

#scale parameter for sample 1 
#scale paramter for sample 2 set as constant 1 
scp1<-c(1,1.5,2,2.5,3) 
scp1<-rep(scp1,9) 

#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),rep(sample_sizes[2,],5),shp,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,16/9,all_combine1[ss,4]) 
     gamma2<-rgamma(n,16/9,1) 

     #minus the population mean to ensure the true of null hypo 
     gamma1<-gamma1-16/9*all_combine1[ss,4] 
     gamma2<-gamma2-16/9 

     #extract p-value out and store every p-value into matrix 
     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) 

} 

Das ist mein result.Clearly es laufen nicht erfolgreich für jeden loop.TT

 m n sp(skewness1.5) scp1 equal3 unequal4 mann5 
[1,] 10 10  1.777778 1.0  9  9  6 
[2,] 10 25  1.777778 1.5  94  93 95 
[3,] 25 25  1.777778 2.0 100  100 100 
[4,] 25 50  1.777778 2.5 100  100 100 
[5,] 25 100  1.777778 3.0 100  100 100 
[6,] 50 25  1.777778 1.0  3  8  6 
[7,] 50 100  1.777778 1.5 100  100 100 
[8,] 100 25  1.777778 2.0 100  100 100 
[9,] 100 100  1.777778 2.5 100  100 100 
[10,] 10 10  1.777778 3.0 100  100 100 
[11,] 10 25  1.777778 1.0  3  4  6 
[12,] 25 25  1.777778 1.5  99  99 100 
[13,] 25 50  1.777778 2.0 100  100 100 
[14,] 25 100  1.777778 2.5 100  100 100 
[15,] 50 25  1.777778 3.0 100  100 100 
[16,] 50 100  1.777778 1.0  3  4  1 
[17,] 100 25  1.777778 1.5 100  100 100 
[18,] 100 100  1.777778 2.0 100  100 100 
[19,] 10 10  1.777778 2.5 100  100 100 
[20,] 10 25  1.777778 3.0 100  100 100 
[21,] 25 25  1.777778 1.0  4  3  5 
[22,] 25 50  1.777778 1.5 100  99 100 
[23,] 25 100  1.777778 2.0 100  100 100 
[24,] 50 25  1.777778 2.5 100  100 100 
[25,] 50 100  1.777778 3.0 100  100 100 
[26,] 100 25  1.777778 1.0  8  9 10 
[27,] 100 100  1.777778 1.5 100  100 100 
[28,] 10 10  1.777778 2.0 100  100 100 
[29,] 10 25  1.777778 2.5 100  100 100 
[30,] 25 25  1.777778 3.0 100  100 100 
[31,] 25 50  1.777778 1.0  2  3  2 
[32,] 25 100  1.777778 1.5 100  100 100 
[33,] 50 25  1.777778 2.0 100  100 100 
[34,] 50 100  1.777778 2.5 100  100 100 
[35,] 100 25  1.777778 3.0 100  100 100 
[36,] 100 100  1.777778 1.0  7  7  5 
[37,] 10 10  1.777778 1.5  88  87 90 
[38,] 10 25  1.777778 2.0 100  100 100 
[39,] 25 25  1.777778 2.5 100  100 100 
[40,] 25 50  1.777778 3.0 100  100 100 
[41,] 25 100  1.777778 1.0  7  7  6 
[42,] 50 25  1.777778 1.5 100  100 100 
[43,] 50 100  1.777778 2.0 100  100 100 
[44,] 100 25  1.777778 2.5 100  100 100 
[45,] 100 100  1.777778 3.0 100  100 100 

Answer 1

ich Ihre Fehler glauben liegt bei diesen Zeilen:

##store the result 
    equal[ss]<- mean(matrix2_equal[,1]<=alpha) 
    unequal[ss]<-mean(matrix5_unequal[,2]<=alpha) 
    mann[ss]<- mean(matrix8_mann[,3]<=alpha) 

matrix2_equal [, 1] < = alpha gibt einen Wert von true oder false zurück, also Mittelwert (matrix2_equal [, 1] < = Alpha) gibt grundsätzlich den% des Wahren von Ihrem Modell zurück. Dies könnte das sein, was man wollte:

equal[ss]<- mean(matrix2_equal[matrix2_equal[,1]<=alpha, 1]) 

FYI: R： coding why show 0.00 in result

Answer 2

@ Dave2e Auskommentierung die beiden Linien folgendes Ergebnis geben: Diese Frage zu Ihrem früheren Post von zusammenhängt.

m n scp equal3 unequal4 mann5 
[1,] 10 10 1.0  8  8  9 
[2,] 10 25 1.5  16  36 23 
[3,] 25 25 2.0  83  82 78 
[4,] 25 50 2.5 100  100 100 
[5,] 25 100 3.0 100  100 100 
[6,] 50 25 1.0  3  5  7 
[7,] 50 100 1.5  82  86 79 
[8,] 100 25 2.0  98  92 91 
[9,] 100 100 2.5 100  100 100 
[10,] 10 10 3.0  76  72 77 
[11,] 10 25 1.0  1  3  3 
[12,] 25 25 1.5  44  42 37 
[13,] 25 50 2.0  94  96 92 
[14,] 25 100 2.5 100  100 100 
[15,] 50 25 3.0 100  100 100 
[16,] 50 100 1.0  4  4  3 
[17,] 100 25 1.5  72  54 56 
[18,] 100 100 2.0 100  100 100 
[19,] 10 10 2.5  65  60 57 
[20,] 10 25 3.0  90  98 95 
[21,] 25 25 1.0  2  2  5 
[22,] 25 50 1.5  48  61 50 
[23,] 25 100 2.0  95  96 93 
[24,] 50 25 2.5 100  99 98 
[25,] 50 100 3.0 100  100 100 
[26,] 100 25 1.0  5  6  2 
[27,] 100 100 1.5 100  100 95 
[28,] 10 10 2.0  50  49 49 
[29,] 10 25 2.5  79  92 85 
[30,] 25 25 3.0  99  99 99 
[31,] 25 50 1.0  6  3  6 
[32,] 25 100 1.5  58  76 54 
[33,] 50 25 2.0  94  91 90 
[34,] 50 100 2.5 100  100 100 
[35,] 100 25 3.0 100  100 100 
[36,] 100 100 1.0  3  3  4 
[37,] 10 10 1.5  22  20 13 
[38,] 10 25 2.0  45  70 55 
[39,] 25 25 2.5  97  97 95 
[40,] 25 50 3.0 100  100 100 
[41,] 25 100 1.0  5  5  5 
[42,] 50 25 1.5  62  48 52 
[43,] 50 100 2.0 100  100 100 
[44,] 100 25 2.5 100  100 100 
[45,] 100 100 3.0 100  100 100