I have been having problems with a randomization test.
essentially the goal is to use an original dataset and create a new data set with a pre-specified number of data points removed at random points. then to perform a glm.nb model on the new data set and store the coefficients and statistics from an anova table of the model in a number of arrays . this process is repeated a number of times (say 1000) so that i can perform descriptive stats and so look at the power of the original model as a function of sample size.
The section of code i am having problems with is :
while (countn<repetitions) {
countn<-countn+1
x<-1:33
shit<-unique(sample(x, no)) # randomly selects the data points to be removed
density.random1<-density.2
density.random2<-density.random1[-shit,] #cretes new dataset
random.model<-(glm.nb(Total~log(Dose)*time2+time2.sq+offset(log(no.adults)
), data=density.random2, na.action=na.omit, control = glm.control(maxit=100)) #performs model
random.coefficients<-coefficients(random.model)
interceptn[countn]<-random.coefficients[1]
log.dosen[countn]<-random.coget<-random.coefficients[2]
time2n[countn]<-random.coefficients[3]
time2.sqn[countn]<-random.coefficients[4]
log.doseXtime2n[countn]<-random.coefficients[5]
random.anova<-anova.glm(random.model, test="Chisq")
sign.diffn[countn]<-random.anova[5,5]
signdiff[countn]<-random.anova[5,5]
}
The problem that i am having is that every so often a data set will be created that will generate the following error that stops the function at the point of the glm.nb function:
Error: NA/NaN/Inf in foreign function call (arg 1)/In addition: Warning message: Step size truncated due to divergence
I have a number of questions about this.
1/ how can i prevent it from exiting the function. i have tried "try" and this will not resolve the issue, if i place it at the glm.nb function it results in an error:
Step size truncated due to divergence Error in "[<-"(`*tmp*`, countn, value = random.coefficients[1]) : incompatible types
Is it possible to create an "if" step, ie. if error ignore and don't perform the assignment of data to the arrays else continue?
2/ given that a data set that would generate this error will be a valid dataset what should i do about the coefficients etc that are generated, ignoring those datasets would result in selection on my results.
3/ what is the actual cause of the error in the first place with respect to the data and the model
any assistance would be very much appreciated.
i have searched through the archives and could not find a solution. I have to admit that i do not adequately understand error capture and handling in R, and have been unable to find any documentation that gives a good explanation of it.
cheers,
colin
---------------------- Dr Colin Bleay Dept. Biological Sciences, University of Bristol, Woodlands rd., Bristol, BS8 1UG. UK
Tel: 44 (0)117 928 7470 Fax: 44 (0)117
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