similar to: Book recommendations: Multilevel & longitudinal analysis

Sorry to ask this but does anyone knows a good text that helps with the basic programming side of R. I understand the statistics behind most of R but i have real difficulty with the data types and how R views them. As a part of that could you help me with another problem. I know how to read data in from a file in terms of read.table but how do i single a column out or even just download a

Hi, all. I'm trying to use some linear regression models in which both the dependent and independent variables are measured with some error. To make things worse, while the errors in the dependent variable are uniform, the errors in the independent (or explanatory, or "x") variables can be heteroskedastic. I've been looking at the book _Measurement Error Models_ by Fuller

Greetings- I'm working through Pinheiro and Bates' _Mixed Effects Models in S and S-Plus_ using R (1.3.1 for linux). On page 13 (okay, so I haven't got that far :)) is: plot.design( ergoStool) which returns on my system: > plot.design(ergoStool) Error: couldn't find function "plot.design" any ideas? Thanks.

Dear all, I have the following question. I have to fit the hierarchical model for the hypothesis concern the individual-level effects by controlling for the individual -level attributes and national-level contextual effects on individuals by using R. O have to obtain the estimates of the impact of the second-level (national: GDP per capita) effects on individuals ( in this instance the impact

I'm trying to use the source command to run commands from a file. For instance: source("do.R"), where do.R is a file in the same directory in which I am running R. The contents of do.R are: ls() print("hello") sum(y1) mean(y1) After source("do.R"), all I see is: > source("do.R") [1] "hello" I'm using the X11 version of R for

Longitudinal and Multilevel Data in R and Stan ICPSR short course: May 28 to June 1, 2018 May 28: Introduction to R by John Fox May 29 to June 1: Longitudinal and Multilevel Data in R and Stan by Georges Monette Sponsored and organized by ICPSR, University of Michigan and held at York University in Toronto, Ontario Course description:https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0226

Hi folks: Does anyone know of a way to do (linear) hypothesis tests of parameters after fitting a maximum-likelihood model w/ optim? I can't seem to find anything like a Wald test whose documentation says it applies to optim output. Also, thanks again to everyone who gave me feedback on the robustness of ML estimation in R! Peter ********************************

A newbie question: I'm trying to decide whether to run a maximum likelihood estimation in R or Stata and am wondering if the R mle routine is reasonably robust. I'm fairly certain that, with this data, in Stata I would get a lot of complaints about non-concave functions and unproductive steps attempted, but would eventually have a successful ML estimate. I believe that, with the

In discussing permutation tests, _Modern Applied Statistics With S Plus_ suggests this (IMHO) rather elegant approach to generating a distribution of t-tests: d<- ...some data... ttest <- function(x) mean(x)/sqrt(var(x)/length(x)) n<-1000 res<-numeric(n) for(i in 1:n) res[i] <- ttest(x<-d*sign(runif(10)-0.5) The problem is that the sign(runif) results in random permutation of

I'm used to statistical languages, such as Stata, in which it's trivial to pass a list of variables to a function & have that function modify those variables in the existing dataset rather than create copies of the variables or having to replace the entire dataset to change a few variables. In R, I suppose I could paste together the right instructions in a function and then execute

Apologies if this has been posted about recently, but I haven't been on this listserve for a while. I ran into a problem trying to compile Hmisc (required package for rms) and have found a solution that I'd like to share. The compile fails with the message: /usr/bin/ld: cannot find -lquadmath libquadmath exists on my Debian Squeeze system (up-to-date, recently installed): ldconfig -p

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

Hi Folks, Could anyone point me to a good reference on linear regression models? Specifically, I am trying to gain an intuitive feel for how the standard error values are calculated for the parameter estimates. My understanding is that these are computed using the variance-covariance matrix computed from the input data matrix. Although I think I understand the math, I still don't have a

Hi everyone I have the following situation: In a longitudinal study, subjects fill out a questionnaire every year (repeated measurements over time). Also, the subjects are nested within departments. There is an intervention going on over time. The outcome variable is continuous. Now I'd like to analyse two things: 1. Is there a significant change over time? I think this is done by a

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers

On Wed, 4 Jan 2006, Peter Muhlberger wrote: One comment in advance: please use a more meaningful subject. I would have missed this mail if a colleague hadn't pointed me to it. > I'm someone who from time to time comes to R to do applied stats for social > science research. [snip] > I would also prefer not to have to work through a > couple books on R or S+ to learn how to

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

Dear R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

Hi I was asked if lme can use FIML (Full Information Maximum Likelihood) instead of REML or ML but I don't know the answer. Does anybody know if this is implemented in R? Thanks Francisco

I must have messed up my R environment, but don't know how or how to undo it. The problem is this: I paste the following into R: test<-function() { print("hello") } And I see this: > test<-function() + { + .print("hello") + } > test() Error in test() : couldn't find function ".print" > When I do the same in a fresh environment, I see