similar to: different outcomes of P values in SPSS and R

Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match

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

Hi, all. I have a simple design I'm comparing to output from SPSS. the design is 1 repeated measure (session) and 1 between measure (cond). my dependent measure is rl. here is the data I'm using (in a data.frame): mig <- data.frame(subj=factor(rep(subj,3)), cond=factor(rep(cond,3)), session=factor(c(rep(1,nsubj),rep(2,nsubj),rep(3,nsubj))),

Hi, I am simulating an event that has 15 possible outcomes and I have a vector 'pout' that gives me the probability of each outcome - different outcomes have different probabilities. Does anyone know a simple way of simulating the outcome of my event? If my event had only two possible outcomes (0/1) I would pick a uniform random number between 0 and 1 and use it to choose between the two

Hi everyone, I'm trying to import .sps (SPSS portable file) file. the read.spss function (library foreign) doesn't allow to import such files. should I import in spss and then save as sav file? there is not other solutions available? what I mostly like from spss file is that they have variable labels. want is really wish to keep are the variable.labels from the spss file; so, if there is a

I will like to analyse a binary cross over design using the random effects model. The probability of success is assumed to be logistic. Suppose as an example, we have 4 subjects undergoing a crossover design, where the outcome is either success or failure. The first two subjects receive treatment "A" first followed by treatment "B". The remaining two subjects receive

This is the named article: http://ije.oxfordjournals.org/content/37/5/1161.long maybe it can help you to help me... :-( -- View this message in context: http://r.789695.n4.nabble.com/INSTRUMENTAL-VARIABLES-WITH-BINARY-OUTCOMES-tp4642361p4642363.html Sent from the R help mailing list archive at Nabble.com.

Hello, In a study exploring transgenerational transmission of anxiety disorder we investigate whether infants react to experimentally induced mood changes of their mothers. We measured the time that an infant needed to cross a cliff (=crossing time) depending on whether his mother had previously undergone a mood induction (treatment) or not (control). The treatment is thus a

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

Good Afternoon I have noticed results similar to the following several times as I have used R over the past several years. My .csv file has a header row and 3073 rows of data. > rskreg<-read.table('D:/data/riskregions.csv',header=T,sep=",") > dim(rskreg) [1] 2722 13 > rskreg<-read.csv('D:/data/riskregions.csv',header=T) > dim(rskreg) [1] 3073

Dear List: I have searched the archives and my R books and cannot find a method to transform a continuous variable into a binary variable. For example, I have test score data along a continuous scale. I want to create a new variable in my dataset that is 1=above a cutpoint (or passed the test) and 0=otherwise. My instinct tells me that this will require a combination of the transform

Hi all, I came across this article on LinuxMedNews (http://www.linuxmednews.com) this morning: http://www.linuxmednews.com/linuxmednews/1076524250/index_html This refers to an integrated data management and analysis system (OIO), which includes R and utilizes the RSessionDA package (Greg Warnes). More information is available here for those interested:

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

Dear collegues, I have used R statistical program, package 'lmer', several times already. I never encountered major differences in the outcome between SPSS and R. ...untill my last analyses. Would some know were the huge differences come from. Thanks in advance, Ronald In SPSS the Pearson correlation between variable 1 and variable 2 is 31% p<0.001. In SPSS binary logistic

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

Dear List, I recently got the chance to interview Jon Peck of SPSS Inc, a pioneering technical statistician working since 1983 (when there were only two substantial statistical software companies as per him ;) (not anymore ;) and currently he is a Principal Software Engineer and Technical Advisor at SPSS. Jon talks of SPSS Inc's involvement with the Open Source, of scripting languages

I have data from several sensors that recorded data at hourly intervals during seven months. I want to separate daily variation from the trend, and also be able to zoom in on only one month of data. I have not been able what functions to use, I can not figure out from the help for 'ts' how to use hourly data. I guess this is routine-work for a lot of people so I hope someone can

I really like to use R for all my graphs, and as I work with stable isotopes I want to have a proper chemical notation in my plots I have looked at ?plotmath, but didn't find the answer and also searched the R website. ------------------------------------------------------------------------ -- plot(1:10,xlab=expression(^{14}*C)) # I want to have a superscript with nothing in front, but it

Dear R-gurus, I would like to zoom in a plot, e.g. I select a region on the x-axis and then I would like the ranges on the y-axis to change accordingly. Is it possible to do this with existing functions, or do I have to invent some data selection before plotting? See below a short example, where I select ylim with trial and error, which I want to avoid. Cheers, Henrik Andersson