search for: upsattar

...ights=w) If I run these codes then I got the following error message, Error in survreg(Surv(y, y >= -5, type = "left") ~ x + : Invalid distribution object Does anybody can help me in identifying the error(s) in these code please? Advance thanks for your time. Abdus Sattar upsattar@yahoo.com ____________________________________________________________________________________ [[elided Yahoo spam]] [[alternative HTML version deleted]]

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

Hello Everybody: I am searching for an R package for fitting Generalized Estimating Equations (GEE) with weights (i.e. Weighted Estimating Equations). From the R documentation I found "geese(geepack)" for fitting Generalized Estimating Equations. In this documentation, under the paragraph “weights” it has been written, “an optional vector of weights to be used in the fitting process.

Hello R-Users: I am want to use tobit regression for left censored panel/longitudinal data. Could you please provide me the name of "library" and/or "package" that will give me option of fitting tobit regression model for longitudinal data? Thank you. Sattar __________________________________________________ [[alternative HTML version deleted]]

Hello R-users: Could you please tell me how can I extract the "df (degree of freedom)" and "estfun (estimating functions)" for the following lmer (or lmer2) model? wtd.mixed<-lmer(ddimer~race+steroid+psi+sofa+apache + (1|subject), method="ML", data=final, cluster="id", weights=w) I tried the following codes: - for the degree of freedom (erorr

Dear R users: I am using survreg for modeling left censored longitudinal data. When I am using the following code for fitting the tobit model I am getting some output with an warning message(highlighted with red color): > survreg(Surv(y, y>=0, type='left')~x + frailty(id), cytokine.data, weight=w, dist='gaussian', scale=1) Call: survreg(formula = Surv(y, y >= 0, type

Hello R-users: I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!)

Dear R-user: I have a left censored longitudinally measured data set with 4 variables such as sub (which is id), x (only covariate), y (repeatedly measured response) and w (weights) (note, ?-5? indicates the left censored value in the attached data set). I am using following R codes (?survival? library and ?survreg? package) for fitting a random effect tobit model for the left censored