R help - Feb 1998 - R-beta: is there a way to get rid of loop?

Here is a programming question.  The code I am using is quite slow and I
was wondering if there is a way to get rid of the for loop.

I am dealing with "interaction" in 2x2 table, and am using
Edwards's G_I
(Likelihood, p. 194).

I label the cells in the table as follows

stim		response
		"y"	"n"		total
--------------------------------
y		hit	miss		nsignal
--------------------------------
n		false	correct		nnoise
		alarm	rejection
---------------------------------

Gsens<-function(nhit,nmiss,nfa,ncr)
{
# Edwards's G_I, measure of interaction, is measure of sensitivity
(xlogx(nhit)+xlogx(nmiss)+xlogx(nfa)+xlogx(ncr))-(xlogx(nhit+nmiss)+xlogx(nfa+ncr))-(xlogx(nhit+nfa)+xlogx(nmiss+ncr))+xlogx(nhit+nmiss+nfa+ncr)
}

BTW I can't figure out how to break the line at 80 cols.  I tried \
but it didn't work (error message).

The question is about this function.  It calculates 95% CI based on Monte
Carlo simulation.  [Edwards gives no ref on the sampling distrib of G_I]
The number of signal trials is binomial random
variable with p=.5, so it is simulated that way.  This is fine, I can get
a vector of 2000 ns numbers that way.  However, for each ns I want an
associated binomial variate with p=phit.  Is there a better way to do this
than by looping?

Gsens.ci.mc<-function(nhit,nmiss,nfa,ncr)
{
#monte carlo simulated 95% CI
nsignal<-nhit+nmiss
nnoise<-nfa+ncr
phit<-nhit/nsignal
pfa<-nfa/nnoise
ns<-rbinom(2000,nsignal,.5)
nn<-nsignal+nnoise-ns
nh<-NULL
nf<-NULL
for(i in 1:2000)
	{
	nh<-c(nh,rbinom(1,ns[i],phit))
	nf<-c(nf,rbinom(1,nn[i],pfa))
	}
g<-Gsens(nh,ns-nh,nf,nn-nf)
quantile(g,c(.025,.975), na.rm=TRUE)
}

Thanks very much for any help!

Bill Simpson

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Bill Simpson <wsimpson at uwinnipeg.ca> writes:
> associated binomial variate with p=phit.  Is there a better way to do this
> than by looping?
> 
> Gsens.ci.mc<-function(nhit,nmiss,nfa,ncr)
> {
> #monte carlo simulated 95% CI
> nsignal<-nhit+nmiss
> nnoise<-nfa+ncr
> phit<-nhit/nsignal
> pfa<-nfa/nnoise
> ns<-rbinom(2000,nsignal,.5)
> nn<-nsignal+nnoise-ns
> nh<-NULL
> nf<-NULL
> for(i in 1:2000)
> 	{
> 	nh<-c(nh,rbinom(1,ns[i],phit))
> 	nf<-c(nf,rbinom(1,nn[i],pfa))
> 	}
> g<-Gsens(nh,ns-nh,nf,nn-nf)
> quantile(g,c(.025,.975), na.rm=TRUE)
> }
Yes. 

[prepare to slap forehead...]

nh<-rbinom(2000,ns,phit)
nf<-rbinom(2000,nn,pfa)

This will generate NA's whereever ns==0, but you had that problem anyway.

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907

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