search for: houwelingen

Hello, I was looking for a way to evaluate the goodness-of-fit of a logistic regression model. After googling, I found that I could use "resid(fit, 'gof')" method implemented in the rms package. However, since I am not used to the "le Cessie-van Houwelingen normal test statistic," I do not know which statistic from the returned from the "resid(fit, 'gof')" call that I could use to evaluate the goodness of fit. When I ran the "resid(fit, 'gof')", I got the following results: ###################################...

R-experts: In 2002, Hans Van Houwelingen et al. published a tutorial on how to do meta-regression in Statistics in Medicine. They used the classic BCG dataset of Colditz to demonstrate correct methodology and computed the results using PROC MIXED in SAS. In trying to duplicate the results presented in this paper, I have discovere...

...s of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620 The above link indicated they should be approximately equal which in my case they are not, any suggestions or is there a package function people would recommend in R for use with a logistic regression model? Thanks in advance, -Rob Schutt ---------------------...

...nibus test is unique > and has more power usually. To get it: > > f <- update(f, x=T, y=T) > resid(f, 'gof') # uses residuals.lrm The documentation of the Design package, section residuals.lrm, says <CITE> Details For the goodness-of-fit test, the le Cessie-van Houwelingen normal test statistic for the unweighted sum of squared errors (Brier score times n) is used. </CITE> It is not clear to me whether the test implemented is for the statistic with a constant kernel described in S. le Cessie and J.C. van Houwelingen. A goodness-of-fit test for binary regr...

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

...diagnotic tests. Since I do not have any experience with SAS, Im having a hard time trying to do so. There is an appendix at the original text with some step by step (in SAS syntax) on how to do it but Im stuck on a more advanced stuff which I cant find a solution. In a step it is mentioned a Van Houwelingen (Statist. Med. 2002; 21:589–624) approach to incorporate between and within study variances and using the restricted maximum likelihood estimation. This is the script (or function) so far.... bivar<-function(TP,FP,FN,TN){ # find a way to sum 0.5 to each null cell if any Se<-T...