similar to: Calculating critical values

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Dear Colleagues, Occasionally I deal with computer-generated (i.e., websurvey) data files that haven't quite worked correctly. When I try to read the data into R, I get something like this: Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 26 did not have 648 elements ...is there any way to get R to tell me how many elements line 26 *did* have? That

Hello, I have searched and failed for a program or script or method to conduct a power analysis for a MANOVA. My interest is a fairly simple case of 5 dependent variables and a single two-level categorical predictor (though the categories aren't balanced). If anybody happens to know of a script that will do this in R, I'd love to know of it! Otherwise, I'll see about writing one

Dear colleagues, I run R on a few different machines, and view graphs and the like by tunnelling X through SSH to my local machine. This is useful for me because my local machine can't easily handle some of the data sets I work with. However, when an ssh connection dies, the tunnelled X session also dies, which breaks R's device connection, generating this error: > Error: X11

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Hello, This is a follow up question to my previous one http://tolstoy.newcastle.edu.au/R/e4/help/08/02/3600.html I am attempting to model relationship satisfaction (MAT) scores (measurements at 5 time points), using participant (spouseID) and couple id (ID) as grouping variables, and time (years) and conflict (MCI.c) as predictors. I have been instructed to include random effects for the

Full_Name: Zivan Karaman Version: 2.7.2 OS: Windows XP Submission from: (NULL) (195.6.68.214) I'm puzzled by the readRegistry function. Shouldn't the "hive" argument be something like c("HLM", "HCR", "HCU", "HU", "HCC", "HPD") rather than c("HLM", "HCR", "HCU", "HU",

Dear colleagues, I've been running some principal components analyses, which generate tables of loadings that I'm interested in looking at. print(f1$rot$load,cutoff=.4) is what I use, and it gives me what I want. However, I'm now interested in comparing these loadings across a few data sets. In other words, I would like R to match the loadings on rownames() and display them next

Hello, I frequently have to export a large quantity of data from some source (for example, a database, or a hand-written perl script) and then read it into R. This occasionally takes a lot of time; I'm usually using read.table("filename",comment.char="",quote="") to read the data once it is written to disk. However, I *know* that the program that generates

Dear Colleagues, I've been searching for a post or article or something which explains why having na.rm=FALSE or na.action=na.fail as the default is a better choice than TRUE or na.omit. I understand the basic argument: it does not make sense to average a nonexistance into an aggregate, and removing them implicitly leads to accidental pairwise deletion in some cases, and sum(x) /

Dear colleagues, Has anybody any experience using the sem package to fit structural equation models using a fitting function other than ML? I have heard tell that OLS may provide better estimates when using standardized matrices generated from small sample sizes, so I was interested in comparing the two for a few models. However, ML appears to be hard-coded into the source for sem...but maybe

HLM question? Is there a minmum number of observations required for a category..I have individusals in work teams.I have incomplete data for all the teams ..sometimes I only have data for one person in a team.I assume that HLM can't work here! But what would be the mimimal.at the moment I have a sample of about 240 in about 100 teams with teamsizes form 2 to 5. Any advice? Thanks

Dear list In HLM, one can specify a covariate at one of the "levels". For example, if the data structure are repeated observations nested within students nested within schools, school size might be a covariate that is used at level 3, but not at the other levels. In HLM this is rather easy to do. However, how can one specify a covariate in R for only one of the levels? I have a

Dear R-users, I'm running a lmer model using the lme4 package. My dependent variable is dichotomous and I'm using the "binomial" family. The results are slightly different from the HLM results based on a Bernoulli distribution. I read that a Bernoulli distribution is an extension of a binomial distribution. Is that right? If so, how can I adapt my R model to a Bernoulli

I'm hoping that this is a relatively easy question for someone familiar with the lme4 package. I'm accustomed to using HLM software and writing a simple 2 level [null] equation like this: L1 - Yij = b0 + e L2 - b0 = B00 + u0 The following command in R provides results that are identical to the HLM program. results <- lmer( Y ~ 1 |id , PanelData4) I can't

Hi R users! I am trying to learn some multilevel analysis, but unfortunately i am now very confused. The reason: http://www.ats.ucla.edu/stat/hlm/seminars/hlm_mlm/mlm_hlm_seminar.htm http://www.ats.ucla.edu/stat/sas/seminars/sas_mlm/mlm_sas_seminar.htm and MlmSoftRev. pdf from mlmRev package. >From what i see, the first two links seem to declare the level one variable as a random part (i

Dear Colleagues, Apologies for a long email to ask what I feel may be a very simple question; I figure it's better to overspecify my situation. I was asked a question, recently, by a colleague in my department about pre-aggregating variables, i.e., computing the mean of defined subsets of a data frame. Naturally, I thought of the 'by' and 'tapply' functions, as

Hello, I'm having the following questionable behavior: > summary(m) Min. 1st Qu. Median Mean 3rd Qu. Max. 1 13000 26280 25890 38550 50910 > max(m) [1] 50912 > typeof(m) [1] "integer" > class(m) [1] "integer" ...it seems to me like max() and summary(m)[6] ought to return the same number. Am I doing something wrong? I'm

I''m a new R user and, frankly, this problem has got me stumped. I''m using the 64 bit version of RGui and followed the installation instructions on http://www.rforge.net/rJava/ Here is the result: package ''rJava'' successfully unpacked and MD5 sums checked > library(rJava) Error in utils::readRegistry(key, "HLM", 2) : Registry key

I have successfully installed R (version 2.8.1) and RMySQL (version 0.7-4) on my PC (Windows). Now I have a problem running RMySQL on R. I constantly receive the following error-message: Error in utils::readRegistry("SOFTWARE\\MySQL AB", hive = "HLM", maxdepth = 2) : Registry-Schl?ssel 'S' nicht gefunden Error : .onLoad in 'loadNamespace' f?r