similar to: Major difference in the outcome between SPSS and R statistical programs

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 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),

Dear all, We are trying to fit a non linear model to dispersal data. It seems that sometimes when the fit of the model of the data is not very good we start getting singular gradient errors. However if we modify slightly the data this won't occurr. We have also tried changing the initial parameter values and the algorithm for fitting in nls but didn't help. So we ended up programming a

Dear R Users, I would like to use R to fit a Weighted Least Square model for a binary outcome, say Y. The model is the one widely used for a binary dependent variable when the logistic model has not been proposed. Does anyone know how to specify the weight as the square root of 1/(E(Y)(1-E(Y)) in lm() or any other regression functions? I know that varPower() in the package of gls() can provide

Hi! I'm pretty raw when working with the R models (linear or not). I'm wondering has anybody worked with the NLME library and dichotomous outcomes. I have a binary outcome variable that I woul like to model in a nested (multilevel) model. I started to fit a logistic model to a NLS function, but could not suceed. I know there are better ways to do it in R with either the LRM or GLM wih

Hi there Within sampleSelection, I'm trying to calculate the marginal effects for variables that are present in both the selection and outcome models. For example, age might have a positive effect on probability of selection, but then a negative effect on the outcome variable. i.e. Model<-selection(participation~age, frequency~age, ...) Documentation elsewhere describes one method for

Hi everyone, I think the following error is slightly odd: > fred <- new.env() > happy <- function() fred > happy() <environment: 0x8a425b8> > attr(happy(), "foo") <- 1:10 Error: invalid (NULL) left side of assignment But naturally this works > attr(fred, "foo") <- 1:10 > fred <environment: 0x8a425b8> attr(,"foo") [1] 1

Hi Folks, I'd be grateful for suggestions about approaching the following kind of data. I'm not sure what general class of models it is best situated in (that's just my ignorance), and in particular if anyone could point me to case studies associated with an R approach that would be most useful. Suppose I have data of the following kind. Each "subject" is observed at say 4

Hello everyone, I want to do a meta-analysis of case-control studies on which an OR was computed based on a continuous exposure. I have found several several packages (metafor, rmeta, meta) but unless I misunderstood their main functions, it seems to me that they focus on two-group comparisons (binary independent variable), and do not have the option of using a continuous independent variable.

Hi, I just completed a really big rsync described earlier. Ie about 13,945 directories transfered about 600GB of data. Of 13,945 directories, 13,9441 directories transfer with matching du -b sizes of the preimage to the size of the destination machine image. of the 4 remaining directories i found source vs destination in bytes --------- a) 20480 vs 34922496 b) 28672 vs

Dear R-nlme expert We need two pieces of information about the fitting of a nlme model which we cannot extract from the R help files and would be most grateful if you could help us. We fit an energy allocation growth model with 4 parameters to individual growth curves using the nlme routine. We thus have repeated age and size measurements of individuals and therefore allow for random

Hi, I am sharing my learning outcomes. Recently I downloaded Kali Linux 64-bit Version 2018.1 and ran it on my HP laptop with the integrated Intel Dual Band Wireless-AC 8260 Wireless Network Card. I wanted to test if I could hack the Wi-Fi WPA password on Ruckus R700 Access Point (AP) and the Aztech DSL8900GR(AC) Wireless Router. So I started using the Reaver WPA cracking tool. I understand

I am trying to model data in which subjects are followed through time to determine if they fall, or do not fall. Some of the subjects fall once, some fall several times. Follow-up time varies from subject to subject. I know how to model time to the first fall (e.g. Cox Proportional Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model the data if I include the data for those

Dear R-gurus: I have a question about lmer. Basically, I have a dataset, in which each observation records number of trials (N) and number of events (Y) given a covariate combination(X) and group id (grp_id). So, my dataset is in tabular form. (in case my explanation of tabular form is unclear, please see the link:

Hi dear R-help memebers, When building a CART model (specifically classification tree) using rpart, it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) variables - while other predictors are relevant for other outcome variables (y's only). *How can it be estimated, which explanatory variable is "used" for which of

Hello R community, I basically created a normal distribution with mean 2500 and standard deviation = 450 with a sample of size 50 and assigned that to a variable named genvar2 with the following command: genvar2<-rnorm(mean=2500, sd=450, n=50) Now, the output of genvar2 generates de following: [1] 2478.126 2671.259 2163.879 2440.796 2702.234 1871.514 2525.127 2830.688 [9] 2704.148 3464.478

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

Dear List, I ran into some trouble by calculating differences. For me it is important that differences are either 0 or not. So I don't understand the outcome of this calculation 865.56-(782.86+0+63.85+18.85+0) [1] -1.136868e-13 I run R version 2.71 on WinXP I could solve my problem by using round() but I would like to know the reason. Maybe someone can help me? Thanx

Dear R users I am analysing data from a group of twins and their siblings. The measures that we are interested in are all correlated within families, with the correlations being stronger between twins than between non-twin siblings. The measures are all calculated from survey answers and by definition have limited ranges (e.g. -5 to +5), though within the range they are approximately normally

Dear list, While trying to identify the root of a problem I am having with garbage collected variables, I have come across the following oddity: depending on whether --verbose is set or not, I obtain different results. I have made a small standalone example to demonstrate it. The example is very artificial, but I had a hard time reproducing reliably the problem. So when I do: (the content of