Dear Max and Greg Thank you for your help. Unfortunately I was not able in getting what I need using the functions you suggested. I believe it can be a result of my inexperience with the packages caret and rms. Therefore, I provide more information about my problem and wish you can again provide me some help.
I already have a single linear regression model fitted to my data (n = 150). The coefficients of the parameters have been determined using ordinary least squares. In the next step I want to use another data set (n = 174) to obtain the validation statistics. For other multivariate linear regression models I have been using the function lmCV() as follows: > set.seed(123) > CV = lmCV(a~b+c, my.data, segments=10, repl=100, segment.type="random") where the k segments are selected randomly (I need to use a known seed). CV$predicted gives me the predicted values of "a" as a function of "b" and "c" for all the n = 174 observations in each of the 100 replications. However, it does not work for single linear regression models. I may have made a mistake, but I could not found any function such as $predicted to get all the 100 predicted values for each of the 174 observations. Hope you can help me once again. Best regards, ----- Bc.Sc.Agri. Alessandro Samuel-Rosa Postgraduate Program in Soil Science Federal University of Santa Maria Av. Roraima, nº 1000, Bairro Camobi, CEP 97105-970 Santa Maria, Rio Grande do Sul, Brazil -- View this message in context: http://r.789695.n4.nabble.com/Repeated-cross-validation-for-a-lm-object-tp4394833p4411252.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
