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

Displaying 20 results from an estimated 100 matches similar to: "Major difference in the outcome between SPSS and R statistical programs"

2008 Aug 01
1
Major difference in the outcome between SPSS and R statisticalprograms
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
2008 Aug 04
1
Major difference in multivariate analyses SPSS and R
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),
2008 Aug 05
0
P values in non linear regression and singular gradients using nls
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
2011 Jun 28
0
Weighted Least Square Model for a Binary Outcome
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
2003 May 11
1
NLME - multilevel model using binary outcome - logistic regression
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
2010 Jan 03
1
Interpreting coefficient in selection and outcome Heckman models in sampleSelection
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
2002 Oct 11
1
Odd outcome of attr with environments (PR#2148)
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
2008 Mar 11
0
"Longitudinal" with binary covariates and outcome
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
2012 Apr 04
1
meta-analysis, outcome = OR associated with a continuous independent variable
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.
2004 Dec 20
1
outcome of big rsync. Puzzling
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
2009 Aug 17
0
weighting nlme in multivariate outcome
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
2018 Mar 19
1
[LEARNING OUTCOME] Wi-Fi WPA Hacking Tool is Totally Useless on New Wireless Routers
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
2005 Jul 29
6
Binary outcome with non-absorbing outcome state
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
2009 Apr 10
1
How to handle tabular form data in lmer without expanding the data into binary outcome form?
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:
2010 Dec 14
1
rpart - how to estimate the “meaningful” predictors for an outcome (in classification trees)
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
2012 May 01
2
How to Export an R outcome to an Excel Spreadsheet
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
2008 Nov 11
1
simulate data with binary outcome and correlated predictors
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
2008 Jul 17
5
calculate differences - strange outcome
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
2010 Jun 22
1
Generalised Estimating Equations on approx normal outcome with limited range
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
2008 Jul 20
1
garbage collection, "preserved" variables, and different outcome depending on "--verbose" or not
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