search for: myscore

Greetings, I'm learning R and I'm stuck on a basic concept: how to specify a logical condition once and then perform multiple transformations under that condition. The program below is simplified to demonstrate the goal. Its results are exactly what I want, but I would like to check the logical state of gender only once and create both (or any number of) scores at once.

Dear R users Happy New year to all for a start. Below is some data that I ran a factor analysis on. Using $score prints the scores for each of the three factors. However, I would like to access those factors as variable for new computations. How do I do that? In SPSS we just call fact1_1, fact2_1 etc.. Thanks for your suggestions. V ============ v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6) v2

Hi all, I have a difficulty to calculate the PCA scores. The PCA scores I calculated doesn't match with the scores generated by R, mypca<-princomp(mymatrix, cor=T) myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings) Does anybody know how the mypca$scores were calculated? Is my formula not correct? Thanks a lot! Phoebe [[alternative HTML version deleted]]

...with the "old" comorbidity measure, Charlson's Comorbidity Index. I have nearly 10,000 deaths and 36 candidate comorbidities. I wish to compare the discrimination of the two comorbidity measures, i.e. I have two non-nested Cox models. I get the following output with > rcorrp.cens(myscore.lp, charlson.lp, Surv(time, dead), method=1): x1 = My comorbidity score, x2 = Charlson [,1] Dxy "-0.0605" S.D. "0.00648" x1 more concordant "0.4697" x2 more concordant "0.5302" n "1.369e+0...

...th the "old" comorbidity measure, Charlson's Comorbidity Index. I have nearly 10,000 deaths and 36 candidate comorbidities. I wish to compare the discrimination of the two comorbidity measures, i.e. I have two non-nested Cox models. I get the following output with > rcorrp.cens(myscore.lp, charlson.lp, Surv(time, dead), method=1): x1 = My comorbidity score, x2 = Charlson [,1] Dxy "-0.0605" S.D. "0.00648" x1 more concordant "0.4697" x2 more concordant "0.5302" n "1.369...