similar to: X'W in Matrix

Dear list, how can I compute the inverse of the X'WX matrix ("inverse of the weighted sum of squares and crossproducts matrix") from an object of class "lm" from a weigthed linear regression? Thanks, Sven

I run a multinomial regression on a data set with an outcome that has three values. First, I build an initial model, b.mod. Then I run a loop to bootstrap the coefficients. For the initial model, using "predict()", I can print the wrong/false predictions table. But how do I get this table (that is, the predictions) for the bootstrapped model? Thanks for hints, *S* df <-

Hi, I am working on a project about hospital efficiency. Due to the high multicolinearlity of the data, I want to fit the model using ridge regression. However, I believe that the data from large hospital(indicated by the number of patients they treat a year) is more accurate than from small hosptials, and I want to put more weight on them. How do I do this with lm.ridge? I know I just need

I am using the ginv function from MASS and have run across this problem that I do not understand. If I define the matrix A as below, its g-inverse does not satisfy the Moore-Penrose condition A %*% ginv(A) %*% A = A. The matrix A is X'WX in a quadratic regression using some very large dollar values. The much simpler matrix B does satisfy the MP condition. Am I doing something wrong? Is

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

Dear R-help, I have a four-part question about regression, matrices, and sandwich package. 1) In the sandwich package, I would like to better understand the meat() function. >From the bread() documentation, for a simple OLS regression, bread() returns (1/n * X'X)^(-1) That is, for a simple regression (per the documentation on bread()): MyLM <- lm(y ~ x) bread(MyLM)

I understand that the hat matrix is a function of the predictor variable alone. So, in the following example why do the values on the diagonal of the hat matrix change when I go from an unweighted fit to a weighted fit? Is the function hatvalues giving me something other than what I think it is? library(ISwR) data(thuesen) attach(thuesen) fit <- lm(short.velocity ~ blood.glucose)

Hi list, When I type my question in R console using ? sign (example: ?par, when I want to query for par), the following error message popps up: Error in print.help_files_with_topic("C:/PROGRA~1/R/R-24~1.1/library/maps/chm/map") : CHM file could not be displayed I appreciate if any body comes back to me with the solution. Regards, Alireza [[alternative HTML version deleted]]

I am sure this has been covered before, but I can find it in my archives. I loaded up a new server IBM x3650, 2x Intel dual core cpu's I installed additional RAM to take it up to 7 gigs according to http://www.centos.org/product.html#seven CentOS 5 x86 which I am using supports up to 16gigs of RAM. Once I boot up CentOS only see's about 3.2Gigs of Ram. I installed the kernel-PAE

In the Matrix package v. 0.995-11 I see that the dgTMatrix Class for compressed, sparse, triplet-form matrices handles Identically indexed data instances by summing their values, e.g., library(Matrix) (Mt <- new("dgTMatrix", i = as.integer(c(0,0,1,1,4)), j = as.integer(c(0,1,2,2,4)), x = as.double(1:5), Dim = as.integer(c(5,5)))) ## 5 x 5 sparse Matrix of class

In the Matrix package v. 0.995-11 I see that the dgTMatrix Class for compressed, sparse, triplet-form matrices handles Identically indexed data instances by summing their values, e.g., library(Matrix) (Mt <- new("dgTMatrix", i = as.integer(c(0,0,1,1,4)), j = as.integer(c(0,1,2,2,4)), x = as.double(1:5), Dim = as.integer(c(5,5)))) ## 5 x 5 sparse Matrix of class

How does one specify the weights for WLS in the systemfit command ? That is, there is a weight option in lm(), but there doesn't seem to be weight option for systemfit("WLS") Thanks!

Dear All, I've been trying to run a Weighted Least Squares (WLS) regression: Dependent variables: a 60*200 matrix (*Rit*) with 200 companies and 60 dates for each company Independent variables: a 60*4 matrix (*Ft*) with 4 factors and 60 dates for each factor Weights: a 60*200 matrix (*Wit*) with weights for 200 companies and 60 dates for each company The WLS regression I would like to run

Hi! After upgrading from Debian wheezy with (self compiled) dovecot 2.2.15 to Debian jessie with (self compiled) 2.2.24, I observe the following segmentation fault in the logs: Jun 7 09:23:09 imap dovecot: imap(user at example.com): Error: read(<imap client>) failed: read(size=8003) failed: Connection reset by peer (uid=0, box=trash) Jun 7 09:23:09 imap dovecot: imap(user at

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

I would like to obtain Confidence Intervals for the estimates (unstandardized beta weights) of each predictor in a WLS regression: m1 = lm(x~ x1+x2+x3, weights=W, data=D) SPSS offers that output by default, and I am not able to find a way to do this in R. I read through predict.lm, but I do not find a way to get the CIs for multiple independent variables. Thank you Torvon [[alternative HTML

I might be sorry for asking this question :-) I have two equations and I tried to estimate them individually with "lm" and "gls", and then in a system (using systemfit) with "OLS", "WLS" and "SUR". Quite surprisingly (for myself at least) the results are identical to the last digit. Could someone (please!) give a hint as to what am I

Hi, I'm trying to create a new S4 class (myMatrix) which for now just extends dgCMatrix (from package Matrix). Then I want to use "[" which is defined in Matrix. Out of the box with "[" (defined in Matrix) I lose the class information and the result is an object of class dgCMatrix. If I specify a "["-method for myMatrix, it is not used because a signature

Hi all, I understand that gls() uses generalized least squares, but I thought that maybe optimum weights from gls might be used as weights in lm (as shown below), but apparently this is not the case. See: library(nlme) f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris, weights = varIdent(form = ~ 1 | Species)) aa <- attributes(summary(f1)$modelStruct$varStruct)$weights f2 <-

hi! using: Version 1.8.0 Under development (unstable) (2003-09-02) using the Sweave function of the tools package i get this error when latexing with texi2dvi --pdf ! LaTeX Error: File `upquote.sty' not found. if i change the line \usepackage{/users/faculty/ririzarr/R-1.8.0/lib/R/share/texmf/Sweave} to \usepackage{/users/faculty/ririzarr/R-1.7.1/lib/R/share/texmf/Sweave} in the tex file,