similar to: MITOOLS: Error in eval(expr, envir, enclos) : invalid 'envir' argument

Hey there I am estimating a multilevel model using lmer. I have 5 imputed datasets so I am using mitools to pool the estimates from the 5 > > datasets. Everything seems to work until I try to use > MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until

Hi folks, I have five imputed data sets and would like to apply the same recoding routines to each. I could do this sort of thing pretty easily in Stata using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

Hi all, I am trying to use the functions in the plm package with multiply imputed datasets. I had tried to combine the datasets using the imputationList() function of mitools. plm, however, requires a data argument, and I don't know where to point it to. I'd appreciate any help people might have. A (possible) fuller description of the problem and code is in a previous

R 2.2.0 windows XP I am beginning to explore the mitools package contributed by Thomas Lumley (thank you Thomas) and I have a few questions: (1) In the examples given in the mitools documentation, the only family argument used is family=binomial. Does the package support family=gaussian and other link functions? I ran the with function with family=gaussian and I obtained results, but I am not

I am working with a largish dataset of 25k lines and I am now tying to use predict. pred = predict(cuDataGlmModel, length + meanPitch + minimumPitch + maximumPitch + meanF1 + meanF2 + meanF3 + meanF4 + meanF5 + ratioF1ToF2 + rationF3ToF1 + jitter + shimmer + percentUnvoicedFrames + numberOfVoiceBreaks + percentOfVoiceBreaks + meanIntensity + minimumIntensity + maximumIntensity +

I'm stumbling my way through manipulating data in multiply imputed datasets, and have run into a problem translating code I used to run on my pre-imputed dataset to multiple datasets. The imputation runs just fine, as does the reading of the mi data sets into an imputationList. I run into trouble, though, when I try to construct a scale across all the data sets. Is there a simple way to do

What does missInfo compute and how is it computed? There is only 1 observation missing the ethnic3 variable. There is no other missing data. N=1409 > summary(MIcombine(mod1)) Multiple imputation results: with(rt.imp, glm(G1 ~ stdage + female + as.factor(ethnic3) + u, family = binomial())) MIcombine.default(mod1) results se (lower upper)

I have a for-loop in my code that calls another .R file: source("estimation.R") This file runs through without any problems, so the program completes the loop one time. However, when the loop starts a second time and it comes time to call the file "estimation.R" again, program stops and prints the following error message: "Error in eval.with.vis(expr, envir, enclos) :

Dear all I tried to use regression to predicted mu data, but it has error like this: > IWJR.complete x y [1,] 33.17635 2.4705021 [2,] 81.61225 3.3815620 [3,] 65.47392 1.6518975 [4,] 57.97806 1.6110785 [5,] 76.05528 2.1601246 [6,] 41.36090 1.5498132 [7,] 68.77844 2.8078691 [8,] 55.57040 2.1183063 [9,] 41.29287 1.8015709 [10,] 65.43935 2.3483183 [11,] 22.44821

I am absolutely new to R and I am aware of only a few basic command lines. I was running a robust regression in R, using the following command line library (MASS) rfdmodel1 <- rlm (TotalEmployment_2004 ~ MISSISSIPPI + LOUISIANA + TotalEmployment_2000 + PCWhitePop_2004 + UnemploymentRate_2004 + PCUrbanPop2000 + PCPeopleWithACollegeDegree_2000 + PCPopulation.of.or.over.65.years.of.age_2004)

> datafilename="E:/my documents/r/sex/bysex1.csv" > data.sex=read.table(datafilename,header=T) > data.sex y.sex.age.region.c.n 1 1980,F,A,N,-18.15,13.61 2 1980,F,A,N,-18.61,13.04 3 1980,F,A,N,-18.81,12.32 4 1990,F,A,N,-21.12,11.7 5 1990,F,A,N,-20.77,11.58 6 1990,F,A,N,-21.6,13.34 7 1990,F,A,N,-21.78,12.6 > model.anova<-aov(c~age*sex,data=data.sex)

Hi.. i have an expression of the form: model1<-nls(y~beta1*(x1+(k1*x2)+(k1*k1*x3)+(k2*x4)+(k2*k1*x5)+(k2*k2*x6)+(k3*x7)+(k3*k4*x8)+(k3*k2*x9)+(k3*k3*x10)+ (k4*x11)+(k4*k1*x12)+(k4*k2*x13)+(k4*k3*x14)+(k4*k4*x15)+(k5*x16)+(k5*k1*x17)+(k5*k2*x18)+(k5*k3*x19)+

Hi R-listers, I am trying to make a trellis boxplot with the HSuccess (y-axis) in each Rayos (beach sections) (x-axis), for each Aeventexhumed (A, B, C) - nesting event. I am not able to do so and keep receiving: Error in eval(expr, envir, enclos) : object 'Rayos' not found Please advise, Jean require(plyr) resp <- read.csv("ABC Arribada R File Dec 12 Jean

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Hi there I'm trying to fit a logistic regression model to data that looks very similar to the data in the sample below. I don't understand why I'm getting this error; none of the data are proportional and the weights are numeric values. Should I be concerned about the warning about non-integer successes in my binomial glm? If I should be, how do I go about addressing it? I'm

I'm running an LSODA to generate some graphs, but I need to stop at a certain point and use those values to generate another LSODA output. This is working fine, but when I try to run the second LSODA, I get the "Error in eval(expr, envir, enclos) : object 'N' not found". Any ideas what can be causing this? I have no object 'N' anywhere in the script. I made an

Hi all, I am hardly an expert, so I expect that this code is not the easiest/ most efficient way of getting where I want. Any suggestions in that direction would also be helpful. I am working on panel analysis with five imputed datasets, generated by Amelia. To do panel analysis, it seemed that the plm package was the best, providing a convenient wrapper for fixed and random effects

Dear all, I have a couple of questions regarding the survival:::cch function. 1) I notice that Prentice and Self-Prentice functions are giving identical standard errors (not by chance but by programming design) while their estimates are different. My guess is they are both using the standard error form from Self and Prentice (1986). I see that standard errors for both methods are

----- begin included message In case cohort study, we can fit proportional hazard regression model to case-cohort data. In R, the function is cch() in Survival package Now I am working on case cohort analysis with time dependent covariates using cch() of "Survival" R package. I wonder if cch() provide this utility or not? The cch() manual does not say if time dependent covariate is