similar to: Interpretation of VarCorr results

Hi! I have a table representing both absolute and relative frequency, for example (code to get example data under the signature): Italy Germany absolute 100 105 relative 40.51 41.18 How can I print a different number of decimal digits? I try to transform to as.character, but cells result aligned to left and I don't like this solution. At the

Folks In trying to use lmer for a hierarchical model, I encountered the following message: Error in UseMethod("VarCorr") : no applicable method for 'VarCorr' applied to an object of class "mer" foo.mer <- lmer(y ~ TP + (TP|M),data=joe.q) > head(joe.q[,1:5]) TP M AB Trt y 1 1 Jan A NN 19.20002 2 1 Jan A NN 19.06378 3 1 Jan A NN

Dear R-help list members I gathered longitudinal data on fish behaviour which I try to analyse using a multi level model for change. Mostly, I am following Singer & Willett (2003), who provide also the S/R code for their examples in the book (e.g. http://www.ats.ucla.edu/stat/Splus/examples/alda/ch4.htm). Of course I am interested in change over time, but I am also very much interested in

I have a loop that I would like to use to extract the "stddev" for each itteration so I can average the "stddev" for all the runs. It would be helpful to know how to extract the "stddev" for each run from the VarCorr. Thanks MCruns<-1000 sighatlvec<-rep(NA,MCruns) sighatbvec<-rep(NA,MCruns) sighatevec<-rep(NA,MCruns) for(mc in 1:MCruns) {

Hi! I need to import in R some SAS dataset (sas7bdat). I found two functions to do it: "read.ssd" from the package "foreign" and "sas.get" from "Hmisc". df = read.ssd(libname = path2data, sectionnames = "sasSmallDataset", tmpXport = path2data, tmpProgLoc = path2data, sascmd = path2sas) sas.get(libraryName = path2data, member =

I am trying to construct a two-way table where, instead of printing the two-way frequencies in the table, I would like to print the values of a third variable that correspond to the frequencies. For example, the following is easily constructed in R > fact1 <- factor(sample(LETTERS[1:3],10,replace=TRUE)) > fact2 <- factor(sample(LETTERS[25:26],10,replace=TRUE)) > fact3

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

Hello! Someone know what are the difference between R and S-PLUS in the density() function? For example, I would like to reply this simple S-PLUS code in R, but I don't understand which parameter I should modify to get the same results. S-PLUS CODE: density(1:1000, width = 4) R-CODE: density(1:1000, bw = 4, window = "g", n = 50, cut = 0.75) I obtain the same x values, but

Dear all, I have a repository file (let's call it repo.txt) that contain two columns like this: # tag value AAA 0.2 AAT 0.3 AAC 0.02 AAG 0.02 ATA 0.3 ATT 0.7 Given another query vector > qr <- c("AAC", "ATT") I would like to find the corresponding value for each query above, yielding: 0.02 0.7 However, I want to avoid slurping whole repo.txt

Hey, what is the R function for the mathematical limit ? e.g. to calculate and return the amount that the expression X^2 +X +2 approach as X approach 2 (X-> 2) thanks hassan [[alternative HTML version deleted]]

For a Monte Carlo study I need to extract from an lme model the estimated standard deviation of a random effect and store it in a vector. If I do a print() or summary() on the model, the number I need is displayed in the Console [it's the 0.1590195 in the output below] >print(fit) >Linear mixed-effects model fit by maximum likelihood > Data: datag2 > Log-likelihood:

My data: I have raw data points that form a logit style curve as if they were a time series. Which is to say they form 3 distinct lines with 3 distinct slopes in backwards z pattern. A certain class of my data looks essentially flat to the eye with marginal oscillation. What is important to me is the x value at which the state change is occurring, in other words, the break point Use of

quantreg package is used. *fit1 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 inp4 inp5 -0.191528450 0.005276347 0.021414032 0.016034803 0.007510343 0.005276347 inp6 inp7 inp8 inp9 0.058708544

Please help me interpret the following results. The full model (Schwa~Dialect*Prediction*Reduction) was reduced via both update() and step(). The minimal adequate model is: ancova<-lm(Schwa~Dialect+Prediction+Reduction+Dialect:Prediction) Schwa is response variable Dialect is factor, two levels ("QF","SF") Prediction is factor, two levels ("High","Low")

Hi, is there a function or an easy way to convert a variable with continuous values into a categorial variable (with x levels)? here is what I mean: I want to transform x: x <- c(3.2, 1.5, 6.8, 6.9, 8.5, 9.6, 1.1, 0.6) into a 'categorial'-variable with four levels so that I get: [1] 2 2 3 3 4 4 1 1 so each element is converted into its rank- value / categorial-value (in

Dear List: Below is the validation output of a fitted ordinal logistic model using the bootstrap in the rms package. My interpretation is that most of the corrected indices indicate little overfitting, however the slope seems to indicate that the model is too optimistic. Given that most of the corrected indices seem reasonable, would it be appropriate to use this model on future data if the

Hello, I've had no luck finding an R package that has the ability to estimate a Tobit model allowing for heteroskedasticity (multiplicative, for example). Am I missing something in survReg? Is there another package that I'm unaware of? Is there an add-on package that will test for heteroskedasticity? Thanks for your help. Cheers, Alan Spearot -- Alan Spearot Department of Economics

Dear all, I am a relative novice with R, so please forgive any terrible errors... I am working with a GLM that describes a response variable as a function of a categorical variable with three levels and a continuous variable. These two predictor variables are believed to interact. An example of such a model follows at the bottom of this message, but here is a section of its summary table:

Hello R community, I have a question regarding correlation and regression analysis. I have two variables, x and y. Both have a standard deviation of 1; thus, correlation and slope from the linear regression (which also must have an intercept of zero) are equal. I want to probe two particular questions: 1) Is the slope significantly different from zero? This should be easy with the lm

Hello, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables.