similar to: lm() variance covariance matrix of coefficients.

Hi, I installed the package Hmisc with the command install.packages("Hmisc") without errors. When I try to load the library with command library(Hmisc) I get the error > library(Hmisc) Error in library(Hmisc) : there is no package called 'Hmisc' > version _ platform i386-pc-linux-gnu arch i386 os linux-gnu system i386,

Hi ! I am trying to get at the covariance of the predictions of a linear model. Suppose the we have: > x<-runif(1000) > y<-2 + 25x*x +rnorm(1000) > lm1 <-lm(y~x, data = data.frame(y = y, x=x)) > x.pred <-runif(10) > y.hat <- predict(lm1, newdata = data.frame(x=x.pred)) I was wondering how to get an estimate of the covariance of y.hat which would be a 10 x 10

Dear all, I tried to use "step" function to do model selection, but I got an error massage. What I don't understand is that data as data.frame worked well for my other programs, how come I cannot make it run this time. Could you please tell me how I can fix it? ***************************************************************************************************

Hi list, can I extract the cov.unscaled ("the unscaled covariance matrix") from a gls fit (package nlme), like with summary.lm? Background: In a fixed effect meta analysis regression the standard errors of the coefficients can be computed as sqrt(diag(cov.unscaled)) where cov.unscaled is (X'WX). I try do do this with a gls-fit. Thanks, Sven

Hello, I was wondering if I can plot two curves I get from "density(data)" into one plot. I want to compare both. With the following commad, I just get one curve plotted: plot( density(mydata) ) Sorry for this stupid question but I could not find a solution until now... Antje

Hi, does anyone know which of the scaled or unscaled version of the variance-covariance matrix of regression coefficients (as produced by ls.diag) is the correct one, or when one is better than the other? I am interested in univariate linear regression (lm). Thank you very much. Dipl.-Psych. Johannes Ullrich Philipps-Universit?t Marburg Fachbereich

Hello, Is there a way to obtain the variance-covariance matrix of the estimated parameters from GLM? my.glm<-glm(mat ~X,family = binomial, data =myDATA) out1<-predict(my.glm,se.fit = TRUE) std<-out1$se.fit se.fit is for getting the standard errors of the estimated parameters (\betas). Is there a way to get the variance-covariance matrix of the estimated parameters? Many thanks,

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Dear all, I want to design a histogram and I need to have the frequency at certain points. For example I have the following 2 columns: *X Y* 0.1 25 0.4 22 0.45 11 0.55 21 I want the chart to have 4 columns. First column is from 0.0-0.1 (on X) and frequency is 25. Next colum is wider and form 0.1-0.4 with 22 frequency. Next column is narrow with 11 frequency and the last column is

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

Hi, I have a erdos-renyi game with 6000 nodes and probability 0.003. g1 = erdos.renyi.game(6000, 0.003) How to create a Watts Strogatz game with the same probability. g1 = watts.strogatz.game(1, 6000, ?, ?) What should be the third and fourth parameter to this argument. -- View this message in context: http://r.789695.n4.nabble.com/Watts-Strogatz-game-tp3491922p3491922.html Sent from the R

Win2k, R1.6.2. I've been using Splus 6.1 and wanted to try the same regression analysis in R. Using "names( blah.lm )" in R yields [1] "coefficients" "residuals" "effects" "rank" [5] "fitted.values" "assign" "qr" "df.residual" [9] "xlevels"

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

I was reading about the project drawer feature in Textmate, which is Mac only. Is there a similar feature in a Windows based text editor that works with R. This feature sounds really useful. Thanks, Graham [[alternative HTML version deleted]]

In "lm" command, we can use "vcov" option to get variance-covariance matrix. Does anyone know how to get variance-covariance matrix in nlrq? Thanks, Kate [[alternative HTML version deleted]]

Hi All, I am fitting a tree to censored survival data using the rpart package and wanted to better understand the results. I am trying to interpret the output from the tree. I am interested in understanding what "yval" is for a survival tree. I see in the output of summary, the phrase "estimated rate". The estimated rate is 1 for the entire tree, and more of less for each

R 2.2.0 windows XP How can I perform a regression analyses using a vector of means, a variance-covariance matrix? I looked at the help screen for lm and did not see any option for using the afore mentioned structures as input to lm. Thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC, University of Maryland School of Medicine Claude D.

Hi All, Please pardon me if I am missing something obvious here. How do I get the Kaplan-Meier estimate function that is created by survfit and plotted by the code. fit <- survfit(Surv(time, status) , data=aml) plot(fit) That is, I need a function that will give me the survival estimate at a given time: \hat{S}(t). Thanks in advance. Ritwik Sinha ritwik.sinha at gmail.com | +12033042111 |

Hi, Suppose I have a multivariate response Y (n x k) obtained at a set of predictors X (n x p). I would like to perform a linear regression taking into consideration the covariance structure of Y within each unit - this would be represented by a specified matrix V (k x k), assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of

Hi R users I have a dataset which represents points that are market by patients as the source of pain. Basically the patients indicates by a cross on a chest pictures where he/she thinks is the source of pain. The data was then digitalized by divinding the chest into small squares and each square was give value 1 if it was the center 2 if it was touched by the markings and 3 if it was not