[R] need help to find type I error rate for modified F statistic

[Rui Barradas]

Hello,

Without trying it, I very much doubt that your code runs: First you use 
'asim' and create it after.
So I've moved asim <- 1000 up some lines.
And it still doesn't work.

1. Run your own code before posting it.
2. Learn to use indentation.
3. Learn to use spaces to make your code easier to read.

Hope this helps,

Rui Barradas

P.S. At a glance, it seems you are dividing 'num' by J and only then 
subtracting 1.
Missing parenthesis? Impossible to say. Follow the above first.

R.B.

Em 29-05-2012 07:06, umai88 escreveu:
> Hello everyone, I want to calculate type I error rate for modified F
> statistic for one way robust anova. I need to find the j  group trimmed
> mean and winsorized sum of squared deviations. Here I attached my code for
> j=2 to make it simple. Originally I have j=4. Hope you can help. I need to
> run it for 1000 times
>
> My problem is:
>   i) the value of F-test obtain from my simulation below is in negative
> value..There might be something wrong in my coding
> ii) after obtain F value, how i can proceed to obtain type I error rate?
>
> h=0
> g=0
> n=15
> alpha=0.1
> k=floor(alpha*n)+1
> r=k-(alpha*n)
> i=k+1
> m=n-k
> J=2
>
> trim1<-rep(NA,asim)
> trim2<-rep(NA,asim)
> pw<-rep(NA,asim)
> qw<-rep(NA,asim)
> px<-rep(NA,asim)
> qx<-rep(NA,asim)
> ssd1<-rep(NA,asim)
> ssd2<-rep(NA,asim)
> madN1<-rep(NA,asim)
> madN2<-rep(NA,asim)
> lo.w<-rep(NA,asim)
> up.w<-rep(NA,asim)
> lo.x<-rep(NA,asim)
> up.x<-rep(NA,asim)
> hw<-rep(NA,asim)
> hx<-rep(NA,asim)
> xbar.t<-rep(NA,asim)
> num<-rep(NA,asim)
> df2<-rep(NA,asim)
> denom<-rep(NA,asim)
> F<-rep(NA,asim)
>
> asim<-1000
> for(j in 1:asim)
> {
> print(j)
> set.seed(j)
>
> a<-rnorm(15,0,1)
> b<-rnorm(15,0,1)
>
> w<-a*exp(h*a^2/2)
> x<-b*exp(h*b^2/2)
>
> matw<-sort(w)
> matx<-sort(x)
>
> trim1[j]=1/((1-2*alpha)*n)*(sum(matw[i:m])+r*(matw[k]+matw[n-k+1]))
> trim2[j]=1/((1-2*alpha)*n)*(sum(matx[i:m])+r*(matx[k]+matx[n-k+1]))
>
> madN1[j]<-mad(w)
> madN2[j]<-mad(x)
>
> lo.w[j]<-length(which(w-median(w)<(-2.24*madN1[j])))
> up.w[j]<-length(which(w-median(w)>(2.24*madN1[j])))
> lo.x[j]<-length(which(x-median(x)<(-2.24*madN2[j])))
> up.x[j]<-length(which(x-median(x)>(2.24*madN2[j])))
>
> pw[j]<-up.w[j]+2
> qw[j]<-n-up.w[j]-1
> px[j]<-up.x[j]+2
> qx[j]<-n-up.x[j]-1
>
> ssd1[j]<-lo.w[j]*(matw[lo.w[j]+1]-trim1[j])^2 +
> (matw[pw[j]:qw[j]]-trim1[j])^2 + up.w[j]*(matw[n-up.w]-trim1[j])^2 -
> ((lo.w)*(matw[lo.w+1]-trim1[j])+ (up.w)*(matw[n-up.w]-trim1[j]))^2
> ssd2[j]<-lo.x[j]*(matx[lo.x[j]+1]-trim2[j])^2 +
> (matx[px[j]:qx[j]]-trim2[j])^2 + up.x[j]*(matx[n-up.x]-trim2[j])^2 -
> ((lo.x)*(matw[lo.x+1]-trim2[j])+ (up.x)*(matx[n-up.x]-trim2[j]))^2
>
> hw[j]<-n-lo.w[j]-up.w[j]
> hx[j]<-n-lo.x[j]-up.x[j]
> H[j]=hw[j]+hx[j]
>
>
> xbar.t[j]<-(hw[j]*trim1[j]+hx[j]*trim2[j])/H[j]
> num[j]<-( (trim1[j]-xbar.t[j])^2 + (trim2[j]-xbar.t[j])^2)^2 )/J-1
> df2[j]<-H[j]-J
> denom[j]<-(ssd1[j]+ssd2[j])/df2[j]
> F[j]<-num[j]/denom[j]
> }
>
> Graduate student,
> Department of Mathematics&  Statistics, UPM
>
>
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