similar to: automation question

I am trying to load binary files in the following fashion load("pred/Pred_pres_a_indpdt") load("pred/Pred_pres_b_indpdt") load("pred/Pred_pres_c_indpdt") load("pred/Pred_pres_d_indpdt") load("pred/Pred_pres_e_indpdt") load("pred/Pred_pres_f_indpdt") but I would like to set up a for loop to replace the letters a:f Here is what I have so

This code works: x<-letters[1:6] ycols<-23:28 xcols<-rep(c(3,4,5,8),each=length(ycols)) somertime<-function(i,j)somers2(Pred_pres_a_indpdt[,i,,], population[,j]) results<-mapply(somertime,xcols,ycols) How can I make variable "h" work? x<-letters[1:6] ycols<-23:28 xcols<-rep(c(3,4,5,8),each=length(ycols))

Sorry I have a very simple question: I used somers2 function from Design package: > z<- somers2(x,y, weights=w) results are: >z C Dxy n Missing 0.88 0.76 500 0.00 Now I want to call only the value of C to be used in further analyses, but I fail to do it. I have tried: > z$C NULL > z[,C] Error in z[,C]: incorrect number of dimensions and some other silly

I have a series of columns that need to be evaluated in various tables. I need to apply a function in the following manner somers2(name1,name2). name1 is a vector of x inputs for the function which correspond to a vector of y inputs in name2. y<-rep(c(3,4,5,8),6) z<-rep(c(23,24,25,26,27,28),4) name1<-sprintf("Pred_pres_%s_indpdt[,%s,,]",x,y)

Dear R-Gurus: This afternoon I was installing the "Hmisc" package. I use R in Linux (Fedora Core 1 (yarrow)) on a Compaq presario with 128 MB RAM laptop. Opening an R session as a root user (superuser), I issued install.packages("Hmisc") and waited. R downloaded the package, installation was going on, and on the standard output I could see that a list of

Hi! I?m trying to calculate de C index (concordance probability) through the somers2 function (library Hmisc). I?m interesting on including the sampling effort as a weight factor for the evaluation of model predictions with real data. I?ve some questions about that: first of all I?m not really sure if I can include sampling effort as a weight factor. Since the weight factor should be a numeric

I have a large dataset where each Subject answered seven similar Items, which are binary yes/no questions. So I've always used Subject and Item random effects in my models, fit with lmer(), e.g.: model<-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1| Item_ID),data,binomial) But I recently realized something. Most of the variables that I've tested as fixed effects are properties

Dear all, I am now doing the logistic regression using R. (glm, family=binomial). Besides the standardize summary statistics generated from R, I am also interested in some more informations concerning the model fitting / prediction etc; Particularly I am interested in "pseudo R squar" and "C statistic". I searched the R- help and could only get very limited information. (Post

Dear colleagues in R, Has anybody implemented the 1) (Goodman & Kruskal) lambda or the 2) (Thiel's) uncertainty coefficient statistics (in the asymmetric and symmetric forms), or is anyone aware that they might reside in some package? A search in the R archives does indicate that they are (somehow) part of the CoCo package, but I would rather not start learning how to transform my