similar to: post hoc test after lme

Hi, consider this: -------------- estr <- c("2^4", "alpha[1]") eexp <- expression(2^4, alpha[1]) ## Is it possible to get 'eexp' starting from 'estr'? The closest I could ## get was: do.call(expression, lapply(estr, as.name)) ## but it is not quite the same; e.g. the following behave differently: library(lattice) xyplot(1:10 ~ 1:10, scales

I am new to R. I have a data set like this (given below is a fictional dataset): AgeCat FINWT 1 98 2 62 1 75 3 39 4 28 2 47 2 66 4 83 1 19 3 50 I need to calculate the weighted distribution of the variable AgeCat. In SAS i can do: proc freq data=ageval; tables agecat; weight finwt; run; What or is there an equivalent in R? TIA, Krishnan -- Krishnan Viswanathan 1101 High Meadow Dr

I have a set of data that includes various data columns. One if the survival time and another if a continuous variable of ages. I want to put the ages into intervals so that I can then perform the Kalpan Meier test. I am trying to use the following code to build a column with the age group numbers in agecatagory<-c( ) for (i in 1:137) { { if(age[i]<=46) {agecat[i]<-1} if(age[i]>46

Dear, I want to fit an inverse gaussion distribution to a data set. The predictor variables are gender, area and agecategory. For each of these variables I've defined a baseline e.g. #agecat: baseline is 3 data<-transform(data, agecat=C(factor(agecat,ordered=TRUE), contr.treatment(n=6,base=3))) The variable 'area' goes from A to F (6 areas: A,B,C,D,E,F) How can i

Hi everyone, my apologies in advance if I'm overlooking something simple in this question. I am trying to use R's survey package to make a direct method age-adjustment to some complex survey data. I have played with postStratify, calibrate, rake, and simply multiplying the base weights by the correct proportions - nothing seems to hit the published numbers on the nose. I am trying to

I have a variable which is roughly age categories in decades. In the original data, it came in coded: > str(xxx) 'data.frame': 58271 obs. of 29 variables: $ issuecat : Factor w/ 5 levels "0 - 39","40 - 49",..: 1 1 1 1... snip I then defined issuecat as ordered: > xxx$issuecat<-as.ordered(xxx$issuecat) When I include issuecat in a glm model, the result

R 2.8.1 windows XP I am getting an error message that I don't understand when I try to run GLM. The error only occurs when I have all independent variables in the model. When I drop one independent variable, the model runs fine. Can anyone help me understand what the error means and how I can correct it? Thank you, John > fit11<-glm(AAMTCARE~BMI+BMIsq+SEX+jPHI+jMEDICAID+factor(AgeCat)+

Hi, I'm working on some social networks and I managed to create the graphs with labels and edges weight, but I would also like to change the size of the vertices according to the age of the persons in the network and the shape according to the gender. Now for the age, I have people with ages between 20 and 78, and I would like to have 4 categories (sizes): 20-35, 36-50, 50-65, >65. I have

Dear Colleagues, We are attempting to create trees using R with our Ruby on Rails application. However, we are running into a problem involving the creation of the graphic. We would like them to be in either jpg or png format so that users can save, but due to a lack of control over our sever we are unable to start X11 server. Is there a way to create these images without using X11 server?

I am doing group wise plots by using the following commands; it shows errors that I do not know how to fix it. Please help. xyplot(within.2.special.care ~ agecat| mco.cms.ind, neuro, panel = function(x,y){ + panel.grid() + panel.xyplot(x,y) + panel.loess(x,y, span =1)}) Error: couldn't find function "xyplot" [[alternative HTML version deleted]]

I am new in using R. I want to know all about building decision tree model in R. Few options which I searched are rpart and rattle to build a decision tree.Both the functions are giving me splits which are statistically appropriate. But I am not able to figure out how to change those splits as per my business requirement. for example : the automatic split of Age by using rattle is > 30 and

Hello R-experts, I have a set of data as follows: age time 1 28 1 2 53 2 3 53 3 4 36 4 5 54 4 6 46 4 7 45 5 8 31 6 9 53 7 10 35 7 11 62 8 12 19 8 13 43 2 14 51 3 15 45 0 16 48 2 17 49 3 18 57 2 19 45 3 20 27 10 21 33 12 22 29 14 23 46 16 24 45 19 25 43 2 26 28 6 27 50 7 28 28 4

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

I've posted the following to R-help on May 15. It has reproducible R code for real data -- and a real (academic, i.e unpaid) consultion background. I'd be glad for some insight here, mainly not for myself. In the mean time, we've learned that it is to be expected for anova(*, "marginal") to be contrast dependent, but still are glad for advice if you have experience. Thank

Hi everyone, I'm using lmer and estimable (from packages lme4 and gmodels respectively) and have the disconcerting happening that when I run exactly the same code, I get different results! In checking this out by running the code 50x, it seems to be that answers may be randomly deviating around those which I get from another stats package (GenStat, using the linear mixed models functionality

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Hi, I am having trouble using the lme function to perform a nested ANOVA with a random nested factor. My design is as follows: Location (n=6) (Random) Site nested within each Location (n=12) (2 Sites nested within each Location) (Random) Dependent variable: sp (species abundance) By using the aov function I can generate a nested ANOVA, however this assumes that my nested

Dear R-helpers, When I try > anova(txtE2.lme, txtE2.lme1) Model df AIC BIC logLik Test L.Ratio p-value txtE2.lme 1 10 8590 8638 -4285 txtE2.lme1 2 7 8591 8624 -4288 1 vs 2 6.79 0.0789 > latex(anova(txtE2.lme, txtE2.lme1)) Error: object "n.group" not found I don't even see n.group as one of the arguments of latex() I checked to see >

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,

R Fans-- I am having problems with the following code. It worked under R 2.6.0 but not in 2.7.0. > library(Epi) > df <- read.table( "c:/Documents and Settings/Troy S/My Documents/debug_chisq_080613b.txt") > summary(df) cvd agecat Min. :0.0000 (0,40] :1 1st Qu.:0.0000 (40,60]:2 Median :0.0000 Mean :0.3333 3rd