sub.test {Trendtwosub}

sub.test function This function calculates the simulated p-value of comparing the trend in subsamples from two independent samples.

Description

sub.test function This function calculates the simulated p-value of comparing the trend in subsamples from two independent samples.

Usage

sub.test(sam1, sam2, fn.rep2)

Arguments

sam1

the first sample.

sam2

the second sample

fn.rep2

the total number of bootstrap repetitions needed for calculating the simulated p-value.

Value

critical.value the critical value of the test based on the alpha level provided

chi-stat the chisqure type test statistics value from the sample provided.

pvalue the simulated p-value.

References

Wang, Y., Stapleton, A. E., & Chen, C. (2018). Two-sample nonparametric stochastic order inference with an application in plant physiology. Journal of Statistical Computation and Simulation, 88(14), 2668-2683.

Examples

attach(seedwt.multi.subsample)
Lev.TN<-levels(TreatmentName);
Lev.Line<-levels(Line);
n<-dim(seedwt.multi.subsample)[1];
level.show=c(1:8);fn.rep3=10^2;
line.name<-Lev.Line[1]; t1.name<-Lev.TN[1];t2.name<-Lev.TN[3];
### To compare the GA treatment and the PACGA treatment from line B73
par(mfrow=c(1,2))
idx<-subset((TreatmentName==t1.name)*(Line==line.name)*(1:n),Env %in% level.show)
idx2<-subset((TreatmentName==t2.name)*(Line==line.name)*(1:n),Env %in% level.show)
boxplot(seedwt[idx]~Env[idx],xlab="ENV levels",ylab=paste('seedwt from',t1.name),
         ylim=c(0,12),cex.lab=1.5,cex.axis=1.8);
boxplot(seedwt[idx2]~Env[idx2], xlab="ENV levels",ylab=paste('seedwt from',t2.name),
         cex.lab=1.5,cex.axis=1.8);
mtext( paste ("Line Name:",line.name), side = 3,outer = TRUE, cex = 2.2,line = -3)
temp.sw1<-seedwt[idx];lab<-Env[idx]; uni.lab<-unique(lab)
sam.1<-lapply(1:length(uni.lab), function(x) temp.sw1[lab==uni.lab[x]])
temp.sw2<-seedwt[idx2];lab2<-Env[idx2]; uni.lab2<-unique(lab2)
sam.2<-lapply(1:length(uni.lab2), function(x) temp.sw2[lab2==uni.lab2[x]])
print(paste("working with line ",line.name,'and treatment',t1.name ,'vs',t2.name ))
resu<-sub.test(sam.1,sam.2,fn.rep2=fn.rep3);
dev.off()
## This will show a similar result as the first experiment of section 5 in the paper.