Hi, Im trying to replace some SAS statistical functions by R (batch calling). But Ive seen that calling R in a batch mode (under Unix) takes about 2or 3 times more than SAS software. So its a great problem of performance for me. Here is an extract of the calculation:
stoutput<-file("res_oneWayAnova.dat","w"); cat("Param|F|Prob",file=stoutput,"\n"); for (i in 1:n) { p<-list_param[[i]] aov_<-aov(A[,p]~ A[,"wafer"],data=A); anova_<-summary(aov_); if (!is.na(anova_[[1]][1,5]) & anova_[[1]][1,5]<=0.0001) res_aov<-cbind(p,anova_[[1]][1,4],"<0.0001") else res_aov<-cbind(p,anova_[[1]][1,4],anova_[[1]][1,5]); cat(res_aov, file=stoutput, append = TRUE,sep = "|","\n"); }; close(stoutput); A is a data.frame of about (400 lines and 1800 parameters). Im a new user of R and I dont know if its a problem in my code or if there are some tips that I can use to optimise my treatment. Thanks a lot for your help. Françoise Pfiffelmann Engineering Data Analysis Group -------------------------------------------------- Crolles2 Alliance 860 rue Jean Monnet 38920 Crolles, France Tel: +33 438 92 29 84 Email: [EMAIL PROTECTED] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.