similar to: manipulating large data and lmer

Apologies if this has been asked before. I am having trouble understanding the R mailing list never mind R! I am relatively new to R having migrated from Minitab and SPSS. I have managed to do some more complicated statistics such as hierarchical partitioning of variance on an 80,000 record dataset but have to admit that drawing a simple bar plot I could do by hand is proving extremely

Hi, I am having trouble with a large dataset I am importing from SPSS. The problem is I have to merge two datasets (which seems to be working OK) then select rows based on attributes. I have a column with either blank cells, B or E entered. I want to select all rows with E. I have other columns with numerical data which I will then do analyses on. data[column==" E"] does not

I am trying to draw a bar chart with standard error bars for two groups. I was struggling in R so have now managed to get rcmdr up and running on my mac (although the help frequently crashes, everything else seems fine, if a bit sluggish; wouldn't use it for stats). This has allowed me to very easily create a plot of the means and standard errors BUT-does anyone know if it is

This seems like a problem that others may have run into before. I got a merged dataset from SPSS. It has ~750 columns X 3400 rows. Most of the cell entries are NA. There are about 375 rows that contain observations on all variables and I want to extract this subset from the original list (of lists). I'm pretty sure I can do it programmatically in R but it will take me a few hours to write

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model with the lmer function including some main effects, a two-way interaction and a random effect. Now I am searching how I could incorporate an interaction between the random effect and one of the fixed effects. I tried to express the interaction in:

First off, Marc Schwartz posted this link earlier today, read it. http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-are-p_002dvalues-not-di splayed-when-using-lmer_0028_0029_003f Second, your email is not really descriptive enough. I have no idea what OR is, so I have no reaction. Third, you're comparing estimates from different methods of estimation. lmer will give standard errors that

Dear all, During my pre-R era I tried (yes, tried) to understand mixed models by working through the 'rat example' in Sokal and Rohlfs Biometry (2000) 3ed p 288-292. The same example was later used by Crawley (2002) in his Statistical Computing p 363-373 and I have seen the same data being used elsewhere in the litterature. Because this example is so thoroughly described, I thought

Hi all I have a hopefully not too stupid question about multi-level / mixed-effects modeling. I was trying to test a strategy from Crawley's 2013 R Book on a data set with the following structure: - dependent variable: CONSTRUCTION (a factor with 2 levels) - independent fixed effect: LENGTH (an integer in the interval [1, 61]) - random effects with the following hierarchical structure: MODE

Dear Help: I am trying to follow Professor Bates' recommendation, quoted by Professor Crawley in The R Book, p629, to determine whether I should model data using the 'plain old' lm function or the mixed model function lmer by using the syntax anova(lmModel,lmerModel). Apparently I've not understood the recommendation or the proper likelihood ratio test in question (or both) for I

Dear collegues, I have used R statistical program, package 'lmer', several times already. I never encountered major differences in the outcome between SPSS and R. ...untill my last analyses. Would some know were the huge differences come from. Thanks in advance, Ronald In SPSS the Pearson correlation between variable 1 and variable 2 is 31% p<0.001. In SPSS binary logistic

I am modelling (at least trying to) the seasonal component of a variable using lmer. I think I am just about getting the hang of building the models but want to see what the fitted values look like. I need to plot 2 lines on the same graph - the original data ( copy of dataframe below) and the fitted values. I am doing this to a) start to understand how to use R and b) start to understand how to

Dear colleagues, I know SPSS can not compute linear mixed models. I used 'R' before for computing multivariate analyses. But, I never encountered such a major difference in outcome between SPSS and 'R': In SPSS the Pearson correlation between variable 1 and variable 2 is 31% p<0.001. In SPSS binary logistic regression gives us an Odds Ratio (OR)=4.9 (95% CI 2.7-9.0),

If you are indeed using lme and not lmer then the needed function is VarCorr(). However, 2 recommendations. First, this is a busy list and better emails subject headers get better attention. Second, I would recommend using lmer as it is much faster. However, VarCorr seems to be incompatible with lmer and I do not know of another function to work with lmer. Hence, a better email subject header

Dear All I am considering moving from SPSS to R as my stats environment of choice. I have read around and everything looks favourable. There is just one issue on which I have been unable to find information. Many clients ask me to send them output (tables, graphs, etc) as an spss output file (ie .spo). I haven't asked them why, I've just said yes. I know R can produce graphics as

Dear R users, I've notice that there are two ways to conduct a binomial GLM with binomial counts using R. The first way is outlined by Michael Crawley in his "Statistical Computing book" (p 520-521): >dose=c(1,3,10,30,100) >dead = c(2,10,40,96,98) >batch=c(100,90,98,100,100) >response = cbind(dead,batch-dead) >model1=glm(y~log(dose),binomial)

Hi All, My paper "R for SAS and SPSS Users" received a bit more of a reaction than I expected. I posted the link (http://oit.utk.edu/scc/RforSAS&SPSSusers.pdf) about 12 days ago on R-help and the equivalent SAS and SPSS lists. Since then people have downloaded it 5,503 times and I've gotten lots of questions along the lines of, "Surely R can't do for free what [fill in

Hello, Is this a bug in the lmer routine? > library(lme4) > ### test case based on rats data from Crawley > a<-rnorm(36);b<-rep(1:3,each=12);c<-rep(1:2,each=6,3);d<-rep (1:3,each=2,6) > > ### mixed model works when c & d are numeric, lmer assumes they are factors > m <- lmer(a ~ b + (1|c/d)) > > ### but bails out when they are actually

Dear R forum, Please refer to my query regarding "Error with function". I forgot to mention that I am using "plyr" library. Sorry for inconvenience. Regards Katherine --- On Tue, 23/4/13, Katherine Gobin <katherine_gobin@yahoo.com> wrote: From: Katherine Gobin <katherine_gobin@yahoo.com> Subject: [R] Error with function To: r-help@r-project.org Date:

Dear R forum Please refer to my query captioned Error with function. I had missed in bracket ")" in the return statement and hence I was getting the error. I has struggled for more than 2 hours to find out the problem and only then has posted to the forum. I sincerely apologize to all for consuming your valuable time. Thanks for the efforts at your end. Regards Katherine --- On

Dear list members: I have the following data: group <- rep(rep(1:2, c(5,5)), 3) time <- rep(1:3, rep(10,3)) subject <- rep(1:10, 3) p.pa <- c(92, 44, 49, 52, 41, 34, 32, 65, 47, 58, 94, 82, 48, 60, 47, 46, 41, 73, 60, 69, 95, 53, 44, 66, 62, 46, 53, 73, 84, 79) P.PA <- data.frame(subject, group, time, p.pa) The ten subjects were randomly assigned to one of two groups and measured