similar to: probability values ?

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Hello, Is there a simple way of stacking/merging two dataframes in R? I want to stack them piece-wise, not simply add one whole dataframe to the bottom of the other. I want to create as follows: x.frame: aX1 bX1 cX1 ... zX1 aX2 bX2 cX2 ... zX2 ... ... ... ... ... aX99 bX99 cX99 ... zX99 y.frame: aY1 bY1 cY1 ... zY1 aY2 bY2 cY2 ... zY2 ... ... ... ... ... aY99 bY99 cY99 ...

Hi there, Just wondering if anyone has any tips for using arrays? I am trying to convert the following SAS code to R: data A2; set A1; by subject_id; retain BX1-BX10 i; array b(1:10) BX1-BX10 ; if first.subject_id then do ; do j=1 to 10; b(j) = .; end; i=1; end; b(i) = BX; i = i+1; if last.subject_id then

Hi, (R version 2.15.0) I am running a pgm with 1 response (earlier standardized Y) and 44 independent vars (Xi) from the same data =a2: When I run the 'lm' function on single Xi at a time, the beta coefficient for let's say X1 is = -0.08 (se=0.03256) But when I run the same Y with 44 Xi-s with the 'step' function (because I left direction parameter empty, I assume a backward

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it

Dear all: Y=a+bX1+cX2 In the Gee (Generalized Estimation Equation) arguments: The arument Corstr has sveral choices: "independence" "fixed" "stat_M_dep" "non_stat_M_dep" "exchangeable" "AR-M" "unstructured" What does each term mean? How do I choose among them? How do I know the correlation structure of

I have a program which is doing a few thousand runs of lm(). Suppose it is a simple model y = a + bx1 + cx2 + e I have the R object "d" where d <- summary(lm(y ~ x1 + x2)) I would like to obtain Var(x2) out of "d". How might I do it? I can, of course, always do sd(x2). But it would be much more convenient if I could snoop around the contents of summary.lm and

Dear all, I am trying to fit a mixed-effects non linear regression, but I have some trouble with it. My data are a balanced panel of 904 subjects with 8 observations (at regular periods) per subject. The functional form of my model is Y=Aexp(-BX1)X2 +e. I want to allow parameters A and B to vary among subjects and also include an autocorrelation term. I have already fitted a standard nonlinear

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

Hi, Suppose I've got a matrix, and the first few elements look like "x1 + x3 + x4 + x5 + x1:x3 + x1:x4" "x1 + x2 + x3 + x5 + x1:x2 + x1:x5" "x1 + x3 + x4 + x5 + x1:x3 + x1:x5" and so on (have got terms from x1 ~ x14). If I want to replace all the x1 with i7, all x2 with i14, all x3 with i13, for example. Is there an easy way? I tried to put what I want

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

Hi, I'm looking for a super-duper fast mean/sum binning implementation available in R, and before implementing z = binnedMeans(x y) in native code myself, does any one know of an existing function/package for this? I'm sure it already exists. So, given data (x,y) and B bins bx[1] < bx[2] < ... < bx[B] < bx[B+1], I'd like to calculate the binned means (or sums)

Hi All, simple question: do you know how to graph the following object/matrix in a 'surface manner': [,1] [,2] [,3] [,4] [,5] [,6] [1,] -0.154 -0.065 0.129 0.637 0.780 0.221 [2,] 0.236 0.580 0.448 0.729 0.859 0.475 [3,] 0.401 0.506 0.310 0.650 0.822 0.448 [4,] 0.548 0.625 0.883 0.825 0.945 0.637 [5,] 0.544 0.746 0.823 0.877 0.861 0.642 [6,] 0.262 0.399

The file TENSILE.DAT from the Hand et al "Handbook of Small Data Sets" looks like this: 0.023 0.032 0.054 0.069 0.081 0.094 0.105 0.127 0.148 0.169 0.188 0.216 0.255 0.277 0.311 0.361 0.376 0.395 0.432 0.463 0.481 0.519 0.529 0.567 0.642 0.674 0.752 0.823 0.887 0.926 except that my mail client has replaced the tab separators by blanks. If I paste this data into R 2.2.1 what I get is

Hi, I have the following file, and I need to work out the linear regression for each sample. I tried the model(*) and receive the error message (**): > data=split(mydata,rep(1:(nrow(mydata)/6),each=6)) > arrang.linear=lapply(data,lm,formula=KA~PA) Erro em storage.mode(y) <- "double" : invalid to change the storage mode of a factor Al?m disso: Warning message: In

Full_Name: Mike Ulrich Version: 2.9 OS: Mac OSX Submission from: (NULL) (69.169.178.34) The help documentation for loadings() lists more then one parameter. The function call only expects one parameter. The digits, cutoff, and sort parameters are not used in the function. ## S3 method for class 'loadings': print(x, digits = 3, cutoff = 0.1, sort = FALSE, ...) ## S3 method for class

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

I installed Remedy a few weeks ago 5.01.02 patch 1313 because that is what my work provided. worked great for a little bit (openSUSE 10.3 64 bit then now has a heart attack and dies when I try to open a ticket click open new from AR control pannel or if I click an already created one from AR control pannel or from AR Central Ticketing system, enter store number then click already created ticket

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