similar to: Calculation of r squared from a linear regression

There appears to be an issue with the decreasing=TRUE option on the order() function that indicates either a bug or perhaps a design flaw (potentially flawed because I would suggest the majority of users would expect different behaviour). # demonstration of problem: x <- c(2,1,3,4,5) order(x) order(x, decreasing=TRUE) order(x) correctly reports the order as: 2 1 3 4 5 I expected the result

Hi, all: I met "Non-conforming parameters for function %*%" problem, when I run the Jags model in R. My model is like this: model{ for(i in 1:n){ for(j in 1:t[i]){ et[i,j]<-yt[i,j]-beta0+betax*xt[i,j]+betat*t[i,j] } for(a in 1:t[i]){ for(b in 1:t[i]){ sigma[i,a,b]<-pow(rho0,abs(t[a]-t[b])) } } phi[i]<-

We currently have a Toshiba Perception EX and I would like to start moving toward VOIP. Is there anyway we can run these two systems in parrallel? Better yet, is there anyway we can run fully on asterisk using the current PBX hardware? The current PBX has a mix of analog, digital and electronic cards in it. I tried to google for advice but I didn't find anything that pertained to this.

Dear all, I cannot find to explicitly get the R-squared or adjusted R-squared from summary(lm()) Thanks a lot! [[alternative HTML version deleted]]

The coxph function in R is not working for me when I use a continuous predictor in the model. Specifically, it fails to converge, even when bumping up the number of max iterations or setting reasonable initial values. The estimated Hazard ratio from the model is incorrect (verified by an AFT model). I've isolated it to the "x1" variable in the example below, which is log-normally

Hi all, To follow up on an older thread, it was suggested that the following would produce confidence intervals for the estimated BLUPs from a linear mixed effect model: OrthoFem<-Orthodont[Orthodont$Sex=="Female",] fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) fm1.s <- coef(fm1OrthF.)$Subject fm1.s.var <- fm1OrthF. at bVar$Subject fm1.s0.s <-

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Dear R-Users, Is it possible to get the covariance matrix from an lme model that did not converge ? I am doing a simulation which entails fitting linear mixed models, using a "for loop". Within each loop, i generate a new data set and analyze it using a mixed model. The loop stops When the "lme function" does not converge for a simulated dataset. I want to

How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure

Dear all, I have three concerns: 1) I am running models with the lme4 package. I cannot find a way to pull out a vector of the fitted values and the residuals. Does anybody know how to do it? 2) How can I nest a random effect variable into a "two-level" fixed effect variable? 3) Suppose I have the following model: y = a + b|c + d + error, where 'a' is a fixed effect, 'c'

Hi all, Could you teach me about the behavior of xm commands when ''ACMError'' occurred? I am testing the behavior of xm commands with wrong arguments. When I tested xm commands related to security (xm *label/*policy), I found that some of them. - return 0, - show ACMError''s Traceback messages. Are these results specifications or bugs? Example: # xm cfgbootpolicy

Hey Folks: New to R and learning as I go, but really, I know just enough to get myself into trouble. I've waded through everything up till now, and don't see anything in the search that is directly helpful for the POS that I've created. The GOAL: All I want in the world is a program that performs 1-way ANOVA's on every column in a data set (taking the first column as the

Hello: I am trying to understand why glm() does not replicate the results in Dobson, "Introduction to Generalized Linear Models," pp. 17-20. I set up the following model. The variable CONDT is assumed as Poisson and the objective is to estimate the expected value. The data (chronic medical conditions among women in Australia) is as follows: CONDT <- c(0, 1, 1, 0, 2, 3, 0, 1,

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise

Hello, Could somebody please tell me what am I doing wrong in following? I try extract coefficients (using arm-package) from the lmer frunction, but I get the following warning: a<-data.frame(coef(res)) Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "coef.mer" into a data.fram I think I have done it before

My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

I am having a problem extracting from "mer" objects.    I have constructed my problem using existing datasets.   Using the following commands:   require(lme4) fm1 <- lmer(Yield ~ 1 + (1 | Batch), Dyestuff) fixef(fm1) I get the following error message: "Error in UseMethod("fixef") : no applicable method for "fixef""   I know that "fixef" is in