similar to: Add1 w/ coef estimates?

I'm having another problem with lmer(), this time something simpler (I think) involving the coef() function for a model with varying coefficients. Here's the R code. It's a simple model with 2 observations per group and 10 groups: # set up the predictors n.groups <- 10 n.reps <- 2 n <- n.groups*n.reps group.id <- rep (1:n.groups, each=n.reps) # simulate the varying

Hi, I am currently working with the sem package in R, to create pathway diagrams. Id like to use the standardized path coeffcients. To get these, I use std.coef. However, using this yields only the standardized coefficients, but does not give me the standard error. Does someone know how to get std.coef to show the standard error of the standardized path coefficients as well? Thanks, Bastiaan

I was hoping that there might be some way to attach significance code like the ones from summary.lm to a dataframe. Anyone know how to do something like that. Here is the function i'd like to add that functionality to: add1.coef <- function(model,scope,test="F",p.value=1,order.by.p=FALSE) { num <- length(model$coefficients) add <- add1(model,scope,test=test) sub <-

Hello, I am modeling some survival data wih cph (Design). I have modeled a predictor which showed non linear effect with restricted cubic splines. I would like to retrieve the se(coef) for other, linear, predictors. This is just to make nice LateX tables automatically. I have the coefficients with coef(). How do I do that? Thanks, David Biau. [[alternative HTML version deleted]]

On Thu, 19 Apr 2007, brech at delphioutpost.com wrote: > Full_Name: Christian Brechbuehler > Version: 2.4.1 Patched (2007-03-25 r40917) > OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS > Submission from: (NULL) (24.61.47.236) > > > Splus and R have different ideas about what qr.coef(qr()) should return, > which is fine... but I believe that R has a bug in that it is not

Hi, I was wondering if someone could please help me. I am doing a logistic regression to compare size at maturity between 3 seasons. My model is: fit <- glm(Mature ~ Season * Size - 1, family = binomial, data=dat) where Mature is a binary response, 0 for immature, 1 for mature. There are 3 Seasons. The Season * Size interaction is significant. I would like to compare the size at 50%

Dear Bill, Thanks for pointing this difference out -- I was unaware of it. I think that the difference occurs in model.matrix.default(), which coerces character variables but not logical variables to factors. Later it treats both factors and logical variables as "factors" in that it applies contrasts to both, but unused factor levels are dropped while an unused logical level is not. I

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

Hello, I am trying to simulate binary outcome data for a logistic regression Monte Carlo study. I need to eventually be able to manipulate the structure of the error term to give groups of observations a random effect. Right now I am just doing a very basic set up to make sure I can recover the parameters properly. I am running into trouble with the code below. It works if you take out the object

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

1. The weibull is the only distribution that can be written in both a proportional hazazrds for and an accelerated failure time form. Survreg uses the latter. In an ACF model, we model the time to failure. Positive coefficients are good (longer time to death). In a PH model, we model the death rate. Positive coefficients are bad (higher death rate). You are not the first to be confused

I am still fairly new to R and have a fairly rudimentary question. I am trying to name a vector of coefficients retrieved from a multilevel model using the coef function. I guess the default name is "Intercept" and I cannot figure out how to rename it. I have tried the using the code below to name the column of coefficients ind.y derived from an lme model. Unfortunately, the

> Hello, > > I have a question about a discrepancy between the > reported F statistics using anova() and add1() from > adding an additional term to form nested models. > > I found and old posting related to anova() and > drop1() regarding a glm with a dispersion parameter. > > The posting is very old (May 2000, R 1.1.0). > The old posting is located here. >

Hello, I am working on reproducing someone's analysis which was done in Stata. The analysis is estimation of a standard Heckman sample selection model (Tobit-2), for which I am using the sampleSelection package and the selection() function. I have a few problems with the estimation: 1) The reported standard error for all estimates is Inf ... vcov(selectionObject) yields Inf in every

Dear R People: I have the output from an arima model fit in an object xxx. I want to verify that the ma1 coefficient is there, so I did the following: > xxx$coef ar1 ar2 ma1 intercept 1.3841297 -0.4985667 -0.9999996 -0.1091657 > str(xxx$coef) Named num [1:4] 1.384 -0.499 -1 -0.109 - attr(*, "names")= chr [1:4] "ar1" "ar2"

David, Thanks. The reason add1.lm (and drop1.lm) do not support offsets is that lm did not when they were written, and the person who added offsets to lm did not change them. (I do wish they had not added an offset arg and just used the formula as in S's glm.) That is easy to add. For the other point, some care is needed if 'x' is supplied and the upper scope reduces the number

I am using R 2.1.1 under Mac OS 10.3.9. Two related problems (see notes 1. and 2. below) are illustrated by results of the following: y <- rnorm(10) x <- z <- 1:10 is.na(x[9]) <- TRUE lm0 <- lm(y ~ 1) lm1 <- lm(y ~ 1, weights = rep(1, 10)) add1(lm0, scope = ~ x) ## works ok add1(lm1, scope = ~ x) ## error lm2 <- lm(y ~ 1, offset = 1:10) add1(lm0, scope = ~ z) ##

I'd like to fit and explore a collection of hierarchical loglinear models that might range from the independence model, ~ 1 + 2 + 3 + 4 to the saturated model, ~ 1 * 2 * 3 * 4 I can use add1 starting with a baseline model or drop1 starting with the saturated model, but I can't see how to get the model formulas or terms in each model as a *list* that I can work with further. Consider

Should the F statistic be the same when using add1() on models created by lm and glm(family=gaussian)? They are in the single-degree-of-freedom case but not in the multiple-degree-of-freedom case. MASS:addterm shows the same discrepancy. It looks like the deviance (==residual sum of squares) gets divided by the number of degrees of freedom for the term twice in add1.glm. Using anova() on the

Hi out there Is there a way to get the estimated coefficients in a logistic / Cox regression without having to specify a 'formula' but by only giving the model matrix? Example for Cox regression: ## predictors n <- 50 q1 <- rnorm(n) q2 <- rgamma(n, 2, 2) Z <- cbind(q1, q2) ## response ttf <- rexp(n) tf <- round(runif(n)) ## compute estimates res <- coxph(Surv(ttf,