similar to: Try reproduce glmm by hand

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

Hello, I'm trying to fit some points with a 8-degrees polynom (result of lm is stored in pfit). In most of the case, it is ok but for some others, some coefficients are "NA". I don't really understand the meaning of these "NA". And the problem is that I can't perform a derivation (pderiv<-as.function((deriv(polynomial(pfit$coefficients))))) on pfit due to the

Hello, I have 3 columns : a, b and a*b I would like to find the pair (a,b) so that a*b is the minimum but not from the points I measured but from the fit of the curve (I have more points that the ones given below but I fit only on this part because I know that the minimum a*b is in this interval). I thought doing it this way : - to fit a*b=f(a) abfit<-lm(ab ~ poly(a,8,raw=T)) - to use the

It is a complex function, functions are quoted frequently, you may read from bottom up The independent variable for final fit is q %%Rg0 is a function of L and b Rg0sq<-function(L,b)L*b/6*(1-3/2*b/L+3/2*(b/L)^2-3/4*(b/L)^3*(1-exp(-2*L/b))) %%alpha is a defined function alpha<-function(x)(1+(x/3.12)^2+(x/8.67)^3)^(0.176/3) %%w is a defined function

Hi, I am doing a binomial GLMM with a random intercept using the formula below, but I always get the same warning message. > m01 <- lmer(pres~ HT + DN + dtree + DNm + cmnhi + cmxes + cplan + craan + lfphal0100 + lfov0100 + lfop0100 + (1|plot), family=binomial, data=vphal, verbose=TRUE) 0: 6309.9448: 0.459924 -5.20747 -0.378722 0.558779 -0.200922 -0.0488451 -0.397844 0.367916 -2.09820

Hello R-list, I am trying to calculate a ridge regression using first the *lm.ridge()* function from the MASS package and then applying the obtained Hoerl Kennard Baldwin (HKB) estimator as a penalty scalar to the *ols()* function provided by Frank Harrell in his Design package. It looks like this: > rrk1<-lm.ridge(lnbcpc ~ lntex + lnbeerp + lnwinep + lntemp + pop, subset(aa,

Dear R community, I have two questions regarding fitting GLMM using maximum likelihood method. The first one arises from trying repeat an analysis in the Breslow and Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random effects, one subject specific, and one observation specific. Thus if we count random effects, there are more parameters than observations. When I try to run the

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

Hi all, For the simple linear regression, I want to find the input "x" value so that the lower confidnece limit is a specific number, say 0.2. In other words, I want to find the value of x so that the lower confidence bound crosses the horizontal line 0.2. Is there a simple way (an R function) that can do this? Thanks. Hanna [[alternative HTML version deleted]]

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

Hi all, I am trying to plot the one-sided confidence limits for the regression line. It seems it is ok to use predict function to compute the two sided confidence limits. Does any one know a easy way to compute the one sided confidence limits? Thank you very much in advance. Hannah [[alternative HTML version deleted]]

I saw a similar question but I still don't fully understand how to implement optim. Can someone help me out with this? Thanks. Keun-Hyung > vol<-rep(c(0.03, 0.5, 2, 4, 8, 16, 32), 3) > time<-rep(c(2,4,8),each=7) > p.mated<-c(0.47, 0.48, 0.43, 0.43, 0.26, 0.23, "null", 0.68, 0.62, 0.64, 0.58, 0.53, 0.47, + 0.24, 0.8, 0.79, 0.71, 0.56, 0.74, 0.8, 0.47) >

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

Can GLMM take formula derived from another object? foo <- glm (OVEN ~ h + h2, poisson, dataset) # ok bar <- GLMM (OVEN ~ h + h2, poisson, dataset, random = list (yr = ~1)) #error bar <- GLMM (foo$formula, poisson, dataset, random = list (yr = ~1)) #Error in foo$("formula" + yr + 1) : invalid subscript type I am using R2.1.0, lme4 0.8-2, windows xp. Below is a dataset if you

Is there an easy way to get confidence intervals from "glmm" in Jim Lindsey's library(repeated)? Consider the following slight modification of an example from the help page: > df <- data.frame(r=rbinom(10,10,0.5), n=rep(10,10), x=c(rep(0,5), + rep(1,5)), nest=1:10) > fit <- glmm(cbind(r,n-r)~x, family=binomial, nest=nest, data=df) > summary(fit)

Dear R-list, I thought that I would let some of you know of a free R package, glmm.ADMB, that can handle mixed models for overdispersed and zero-inflated count data (negativebinomial and poisson). It was built using AD Model Builder software (Otter Research) for random effects modeling and is available (for free and runs in R) at: http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html I

Hi all, I have searched for a long time to find out R program about V ariable S election for GLMM (Generalized Linear Mixed Model). I saw several great R packages for V ariable S election. I  also found several R packages for GLMM. But, I did not find yet R package about V ariable S election for GLMM even though sevel  papers about it have been published.   In fact,  I need V ariable 

Hello all, In a previous posting regarding glmm.admb it is stated that glmm.admb can handle 2 nested random effects. I can only fit a single random term at the moment, and wondered if anyone could provide me with some information on how to specify a model with 2 (nested or cross-classified) random terms? Thanks, Jarrod.

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan