search for: vcv

Hi, I am trying to replicate the R's result for VCV matrix of estimated coefficients from linear model as below data(mtcars) model <- lm(mpg~disp+hp, data=mtcars) model_summ <-summary(model) MSE = mean(model_summ$residuals^2) vcov(model) Now I want to calculate the same thing manually, library(dplyr) X = as.matrix(mtcars[, c('disp',...

...intercept at the end whereas vcov will have it at the beginning so you will need to permute the rows and columns to get them to be the same/ On Wed, Sep 4, 2024 at 3:34?PM Daniel Lobo <danielobo9976 at gmail.com> wrote: > > Hi, > > I am trying to replicate the R's result for VCV matrix of estimated > coefficients from linear model as below > > data(mtcars) > model <- lm(mpg~disp+hp, data=mtcars) > model_summ <-summary(model) > MSE = mean(model_summ$residuals^2) > vcov(model) > > Now I want to calculate the same thing manually, > > lib...

...t, I don't get the graphs, which I want. 2. In the graphs given by plot(gam.object), I get confidence intervals that correspond to standard errors that are NOT based on the sandwich estimator, above. How could I get GAM to plot confidence intervals based on the sandwich estimator for the vcv matrix? 3. Some of the terms are interactions. (i.e.: T*var2). I realize that GAM has tensor product capabilities, but (1) frankly I don't understand them yet, and (2) I want my semi-parametric fit to be comparable to polynomial "parametric" fits. So, in addition to the plots...

...(fm1, c(1,1)) Confidence interval ( WALD ) level = 0.95 beta0 Estimate Std.Error X2.value DF Pr(>|X^2|) Lower.CI Upper.CI 1 0 261.8724 6.786701 1488.888 1 0 248.5707 275.1741 The problem above arises because esticon.mer() uses the variance-covariance matrix of the fixed effects (vcv below) in a calculation (cm is a matrix): cm %*% vcv %*% t(cm). The result is: 1 x 1 Matrix of class "dgeMatrix" [,1] [1,] 46.05931 - and taking diag() of that matrix causes the error above... However, things work fine if I use as.matrix() as is: diag(as.matrix(cm %*% vcv %*%...

Dear all, In SAS (GLM and MIXED) estimable functions (linear functions of the parameters) can be specified in the ESTIMATE and CONTRAST statements. Has anyone written a similar "utility" for use in connection with lm? Thanks in advance S?ren H?jsgaard -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I think vcov() gives estimates of VCV for coefficients. I want estimate of SD for residuals On Tue, Dec 24, 2024 at 7:24?PM Ben Bolker <bbolker at gmail.com> wrote: > > vcov(). ? > > > On Tue, Dec 24, 2024, 8:45 AM Christofer Bogaso <bogaso.christofer at gmail.com> wrote: >> >> Hi, >> >...

Hi all, Can anyone please guide me how to draw a Concentration ellipsoid for a bivariate system with a bivariate normal dist. having a VCV matrix : Sigma <- matrix(c(1,2,2,5), 2, 2) I would like to draw in using GGPLOT. Your help will be highly appreciated. Thanks, -- View this message in context: http://www.nabble.com/Concentration-ellipsoid-tp25315705p25315705.html Sent from the R help mailing list archive at Nabble.com.

?deviance ?anova Bert On Tue, Dec 24, 2024 at 6:22?AM Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > > I think vcov() gives estimates of VCV for coefficients. > > I want estimate of SD for residuals > > On Tue, Dec 24, 2024 at 7:24?PM Ben Bolker <bbolker at gmail.com> wrote: > > > > vcov(). ? > > > > > > On Tue, Dec 24, 2024, 8:45 AM Christofer Bogaso <bogaso.christofer at gmail.com>...

...ding required package: MASS Loading required package: mvtnorm > > mammaldata <-read.csv("R.Mammal_data.csv", header = TRUE) > mammaltree <-read.nexus("BEphylotree.nex") > mammal <- comparative.data(phy = mammaltree, data = mammaldata, names.col = Taxon, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE) #names.col? Error in phy$node.label[which(newNb > 0) - Ntip] : only 0's may be mixed with negative subscripts -- Nicole A Thompson E3B Columbia University, NYCEP nat2103 at columbia.edu 480.522.4212

...y~s(x,k=3),knots=list(dumb.knots)) plot(dumb.example) dumb.example2 = gam(y~s(x,k=3)) plot(dumb.example2) Dumb example 1 is the same as dumb example 2, but it shouldn't be. Once I figure out how to do this, I'll take the fitted coefficients from each model and average them, then take the vcv's from each model and average them, and add a correction to account for within and between imputation variability, then plug them into a gamObject$coeffient and gamObject$Vp matrix, plot/summarize, and have my result. Comments welcome on whether or not this would be somehow incorrect would...

I've been comparing variables among objects (taxa) related by known trees, using phylogentically independent contrasts in the ape package, and want to move on to more complex models e.g. by using gls with appropriate correlation terms. My trees contain lots of (hard) polytomies and information about ancestors, which I've been including- creating fully dichotomous trees by using zero branch

...tiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how variable they are between each other. For this to work in the context of a penalized spline model, the knots need to be specified identically for each model (this is assisted by context knowledge), and each model needs to have the same numbe...

...m(Purchased ~ Gender, data = Dat, family = binomial()) How I can get Standard deviation of forecasts as head(predict(Model, type="response", se.fit = T)$se.fit) My question: given that in Logistic regression, logit link is used, how R calculate SE for the predicted probability from the VCV matrix of estimated coefficients? Does R uses some approximation like delta rule?

vcov(). ? On Tue, Dec 24, 2024, 8:45 AM Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi, > > I have below GLM fit > > clotting <- data.frame( > u = c(5,10,15,20,30,40,60,80,100), > lot1 = c(118,58,42,35,27,25,21,19,18), > lot2 = c(69,35,26,21,18,16,13,12,12)) > summary(glm(lot1 ~ log(u), data = clotting, family = gaussian)) > >

...title of the message etc... Sorry.## I am running following phylogenetic analyses with the caper package: data=read.table(file="data.txt",header=T,sep="\t") tree = read.nexus("Tree.nex") primate = comparative.data(phy=tree, data=data,              names.col=Species, vcv=TRUE, na.omit=FALSE, warn.dropped=TRUE) PGLM_brain=pgls(ln_brain~ln_body,data=primate, lambda="ML")     summary(PGLM_brain) Now I would like the get the phylogenetic residuals of this model, which I should be able to do with the phyres function: resbrain=phyres(PGLM_brain) But then I...

...quot;, "PH", "R", "SB","SC", "SPBF", "SPW", "SS", "SSB", "SSS", "SW", "SWF", "WB", "WLH", "WW") > names(data) <- species > cor.mat <- vcv.phylo(treeX, cor=TRUE) > regr <- compar.cheverud(data, cor.mat) > regr$rhohat [1] 5.541462 > 1 - var(regr$residuals)/var(data) [,1] [1,] -1.333095 I don't understand why the autoregression coefficient falls outside the interval -1 to 1, or why the calculation for obtai...

Hi, I need to correct for ar(1) behavior of my residuals of my model. I noticed that there are multiple gls models in R. I am wondering if anyone has experience in choosing between gls models. For example, how should one decide whether to use lm.gls in MASS, or gls in nlme for correcting ar(1)? Does anyone have a preference? Any advice is appreciated! Thanks, -- Tom [[alternative HTML