Dear R users This is a stats question rather than R question. For continuous predictors, we get estimates of slopes and their se and t values (slope/se) in R ouptput. If we have a model with more than one continuous variable (i.e., multiple regression), we get slope, se and t value for each continuous predictor. Here is my question - is there any way I can calculate r value or correlation (partial correlation?) between each continuous predicator and response, using these parameters in R output? If I just have one response and one predictor, I would use cor.test(x, y) or cor(x, y) function but I was wondering whether I could calculate correlation coefficient for each predictor from a statistical model.
Cohens d (dimensionless effect size) is very easy to calculate from t values and sample size when I have categorical (dichotomous) predictors, but I could not get r (correlation coefficient; another dimensionless effect size) when I have continuous predictors using t values or related statistics from models. Thank you very much Shinichi ______________________________________________ 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
