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...Gaussian curve(the data closely resembles one except a few bars). This is my code : #!/usr/bin/Rscript out_file = "irc_20M_opencl_test.png" png(out_file) scan("my.csv") -> myvals hist(myvals, breaks = 50, main = "My Distribution",xlab = "My Values") pdens <- density(myvals, na.rm=T) plot(pdens, col="black", lwd=3, xlab="My values", main="Default KDE") dev.off() print(paste("Plot was saved in:", getwd())) the problem here is that I a jagged distribution, you can see the result : http://s15.postimage.org/...

I am trying to fit the generalised linear model for the negative binomial, but the results which come out are attached below. When we fit this model using few covariates, the model converge. Does it mean that this family is fitted differently from other glm? or the number of zeros in my response variable has a limiting factor? Thanks Bruno fit <- glm.nb(pfde~SEX+...., data=data1) Warning

The opus code default compiles on -o2 optimization level. I would like to change it to -o3. I have tried doing the changes in makefile.unix . The change is not getting reflected. I am building the code in Code composer studio for TI processor C6000. Could anybody help me with this -------------- next part -------------- An HTML attachment was scrubbed... URL: