search for: torvon

...rdized beta weights) of each predictor in a WLS regression: m1 = lm(x~ x1+x2+x3, weights=W, data=D) SPSS offers that output by default, and I am not able to find a way to do this in R. I read through predict.lm, but I do not find a way to get the CIs for multiple independent variables. Thank you Torvon [[alternative HTML version deleted]]

...two questions: (1) Some sources say robust regression take care of both lack of normal distribution and heteroscedasticity, while others say only of normal distribution. What is true? (2) Are there ways of using robust regressions with ordered data, or is that only possible for metric DVs? Thanks Torvon [[alternative HTML version deleted]]

...variables. In a correlation matrix, the first row and the first column usually have variable names. We've been unsuccessful so far to read such a file into qgraph, and haven't found to manually assign names to variables in qgraph. Would you know of a solution to this problem? Thank you, Torvon [[alternative HTML version deleted]]

...I have the feeling that p-values are rounded to the smallest value of "2e-16", because this p-value is very common. Is that true or just chance? If it is true, how do I obtain the "true" unrounded p-values for these regressors? m1 <- lm(y ~ x1+x2+x3+4+x5, data=D) Thank you Torvon [[alternative HTML version deleted]]

Dear R Mailinglist, I want to understand how predictors are associated with a dependent variable in a regression. I have 3 measurement points. I'm not interested in understanding the associations of regressors and the predictor at each measurement separately, instead I would like to use the whole sample in one regression, "pooling" the measurement points. I cannot simply throw them

I run 9 WLS regressions in R, with 7 predictors each. What I want to do now is compare: (1) The strength of predictors within each model (assuming all predictors are significant). That is, I want to say whether x1 is stronger than x2, and also say whether it is significantly stronger. I compare strength by simply comparing standardized beta weights, correct? How do I compare if one predictor is

Hello. Using the polychor function > polychor(data[c(s1,s2)] ) for polychoric correlations of two ordinal variables in R takes a long time for N=7000 (20 minutes+) and significantly slows down my computer. Now, I have a pretty old computer, but it takes about 20 seconds for MPLUS to print out the complete polychoric correlation matrix for all 16 variables, while I am running the R function

Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you

...e symptom causes this problem: 1: In sqrt(1/i) : NaNs produced How would you recommend to deal with these problems? Are results reliable although these errors occur? Again, I have to use a model that fits all 9 models overall best - maybe I should default to maximum likelihood Poisson? Thank you Torvon [[alternative HTML version deleted]]

I have a dataset with plenty of variables and lots of missing data. As far as I understand, R automatically removes subjects with missing values. I'm trying to fit a mixed effects model, adding covariate by covariate. I suspect that my sample gets smaller and smaller each time I add a covariate, because more and more lines get deleted. Is there a way of displaying how many subjects are

A colleague wrote the following syntax for me: D = read.csv("x.csv") ## Convert -999 to NA for (k in 1:dim(D)[2]) { I = which(D[,k]==-999) if (length(I) > 0) { D[I,k] = NA } } The dataset has many missing values. I am running several regressions on this dataset, and want to ensure every regression has the same subjects. Thus I want to drop subjects listwise for

...owing error: > Error in qr.default(x * sqrt(ret$weights)) : > NA/NaN/Inf in foreign function call (arg 1) Google wasn't helpful, maybe you can help me out. If this problem is not obvious to someone who uses R regularly, please let me know what further information to provide. Thank you! Torvon [[alternative HTML version deleted]]

...e right track with the relimp and relaimpo packages? (3) Is there a way to only use standardized betas? I would prefer that because it would enable me to use standardized confidence intervals to reason that x1 has a meaningfully larger influence on y than x2 (if the CIs do not overlap). Thank you! Torvon [[alternative HTML version deleted]]

I have two very strong fixed effects in a LMM (both continuous variables). model <- lmer( y ~ time + x1+x2 + (time|subject)) Once I fit an interaction of these variables, both main effects disappear and I get a strong interaction effect. model <- lmer( y ~ time + x1*x2 + (time|subject)) I would like to plot this effect now, but have not been able to do so, reading through ggplot2 and

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

Hello. I am running a multivariate multilevel mixed effects model, and am trying to understand what the interaction term tells me. A very simplified version of the model looks like this: model <- lmer (phq ~ -1 + as.factor(index_phq) * Neuro + ( -1 + as.factor(index_phq)|UserID), data=data) The phq variable is a categorical depression score on 9 depression items (classified by the variable

Hello, I'm working with RStudio, which does not display enough lines in the console that I can read the summary of my (due to the covariance-matrix rather long) model. There are no ways around this, so I guess I need to export the summary into a file in order to see it ... I'm new to R, and "R save model summary" in google doesn't help, neither does "help(save)"

In a GLMM, one compares the conditional model including covariates with the unconditional model to see whether the conditional model fits the data better. (1) For my unconditional model, a different random effects term fits better (independent random effects) than for my conditional model (correlated random effects). Is this very uncommon, and how can this be explained? Can I compare these models

Hello. I'm am trying to follow a recommendation to deal with a dependent variable in a linear regression. I read that, due to the positive trend in my dependent variable residual vs mean function, I should 1) run a linear regression to estimate the standard deviations from this trend, and 2) run a second linear regression and use 1 / variance as weight. These might be terribly stupid

Hello. I am running 9 poisson regressions with 5 predictors each, using glm with family=gaussian. Gaussian distribution fits better than linear regression on fit indices, and also for theoretical reasons (e.g. the dependent variables are counts, and the distribution is highly positively skewed). I want to determine pseudo R^2 now. However, using the pR2() of the pscl package offers drastically