search for: unstandard

...nippet from my code: modelResults <- extractModelParameters("C:/PilotStudy/Mplus_Input/Test", recursive=TRUE) #extractModelParameters reads all the output files from the Test folder and create the following variables in R for each file read: #C..PilotStudy.Mplus_Input.Test.rep1.out.unstandardized.est #C..PilotStudy.Mplus_Input.Test.rep2.out.unstandardized.est #C..PilotStudy.Mplus_Input.Test.rep3.out.unstandardized.est modelResultsTemp <- as.data.frame(modelResults) MeansTempC1 = rep(NA ,9) counter = 1 for (f in 1:3) { i=31 for (g in 1:3) { OutputFileName <- paste("modelR...

...e a look ... Thanks a lot. Regards, Matthieu FUNCTION ---------- The function takes the performance on two tasks and estimate the rarity (the p-value) of the difference between the patient's two scores, in comparison to the difference i the controls subjects. A standardized and an unstandardized version are provided (controlled by the parameter standardized: T vs. F). Also, for congruency with the original publication, both the raw data and summary statistics could be used for the control group. ################################################## # Bayesian (un)Standardized Dif...

I would like to obtain Confidence Intervals for the estimates (unstandardized 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 To...

Dear list members, In sem, std.coef() will give me standardized coefficients from a sem model. But is there a trick so that path.diagram can use these coefficients rather than unstandardized ones? Thanks Steve Powell From: John Fox <jfox_at_mcmaster.ca> Date: Wed 28 Feb 2007 - 14:37:22 GMT Dear Tim, See ?standardized.coefficients (after loading the sem package). Regards, John John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525...

Hi all, is it possible to run factor analysis in R such that the routine returns - unstandardized factor scores (according to the original scale) - rotated factor scores (these may be standardized) So far I have only found the possibility to return standardised unrotated factor scores. Thank you very much, Ursula ==================================================== NFO Infratest Ursula...

Hello - Does anybody know how to get the SEM package in R to return standardized path coefficients instead of unstandardized ones? Does this involve changing the covariance matrix, or is there an argument in the SEM itself that can be changed? Thank you, Tim [[alternative HTML version deleted]]

...ng that mlWin and lmer generate the same exact random effects but different results for the standardized random effects. Now, my prior post showed exactly how lme calculates the standardized random effects, so this is now totally transparent. What I would recommend you do is this 1) Calculate the unstandardized random effects in mlWin 2) Calculate the standardized random effects in mlWin 3) Divide the mlWin unstandarized random effects by the standarized random effects. This will show what denominator is used to standardize the random effects. Basically, just replicate what I did in my prior email us...

...a response model. This model should predict the probability that someone is responding to a direct mailing. I used the sem package for this. When I have my coefficients I want to know how well my model predicts the probability of response. How can I calculate these probabilities? I tried to use the unstandardized coefficients, just like a regression coefficient in the following formula: Y = b1*x1 + b2*x2 But then I have values larger than 1, so that aren't probabilities. Does anyone dealt with this problem before? You can be of great help to me!! Kind regards, Tryntsje [[alternative HTML version...

...occupancy in wetlands. I have 4 species and 20 possible patch and landscape variables, which I've been testing in smaller groups. > zib.out<-obs.error(y=painted,m=numvis,bp=zvars,pcovar=7) I get the following error message, with all species, all variable groups, standardized and unstandardized data... Error in optim(par = rnorm(pcovar + qcovar), fn = obs.error.LL, method = "L-BFGS-B", : L-BFGS-B needs finite values of fn I have a large sample size, so we're not sure that this is related to a convergence problem, but we can't figure out what this e...

...nificantly stronger. I compare strength by simply comparing standardized beta weights, correct? How do I compare if one predictor is significantly stronger than the others? I thought about comparing confidence intervals, but if I understand correctly the confidence intervals are calculated from the unstandardized beta weights, which in this case would not help me, correct? (2) The strength of the same predictor over different models. I want to say whether x1 affects y1 - y9 equally strong or not. How would I do this? I hope that I provided all information that is needed. Thank you T [[alternative HTM...

...sem(model, data=A) you should be able to compute the y variable like: attach(data) data$y<-v1*lam1+v2*lam2+v3*lam3+v4*lam4 #change the loading name with the actual loading (number) or extract them from the objectiveML object (they are located in model.sem[[15]]) Note that those loadings are unstandardized and that the resulting variable will not be standardized. Hope it helps Regards, Marko -- Marko Ton?i? Assistant Researcher University of Rijeka Faculty of Humanities and Social Sciences Department of Psychology Sveu?ili?na Avenija 4, 51000 Rijeka, Croatia

A short function I wrote for the purpose of evaluating scales made up of a number of questionnaire items. It provides Cronbach's Alpha, both unstandardized and based on standardized items. It also provides item statistics which include item-total correlations (corrected) and item-removed alpha. Thought some of you might find it useful. I would also appreciate any programming tips or suggestions for improving the function as I am still relativel...

...;> 0.07342198 -0.39650356 -0.36569488 -0.09435788 > >>coefficients(lm.ridge(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) >>ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA >> 0.07342198 -0.39650356 -0.36569488 -0.09435788 The equivalent from SPSS >>is attached. The unstandardized coefficients in SPSS look nothing like >>those in R. The standardized coefficients in SPSS match the >>lm.ridge()$coef numbers very closely indeed, suggesting that the same >>algorithm may be in use. >> >> I have put the dataset file, which is the untouched or...

....36569488 -0.09435788 > coefficients(lm.ridge(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA 0.07342198 -0.39650356 -0.36569488 -0.09435788 The equivalent from SPSS is attached. The unstandardized coefficients in SPSS look nothing like those in R. The standardized coefficients in SPSS match the lm.ridge()$coef numbers very closely indeed, suggesting that the same algorithm may be in use. I have put the dataset file, which is the untouched original I received from the authors, in this D...

...69488 -0.09435788 > coefficients(lm.ridge(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA > 0.07342198 -0.39650356 -0.36569488 -0.09435788 The equivalent from SPSS is attached. The unstandardized coefficients in SPSS look nothing like those in R. The standardized coefficients in SPSS match the lm.ridge()$coef numbers very closely indeed, suggesting that the same algorithm may be in use. > > I have put the dataset file, which is the untouched original I received from the authors,...

....36569488 -0.09435788 > coefficients(lm.ridge(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA 0.07342198 -0.39650356 -0.36569488 -0.09435788 The equivalent from SPSS is attached. The unstandardized coefficients in SPSS look nothing like those in R. The standardized coefficients in SPSS match the lm.ridge()$coef numbers very closely indeed, suggesting that the same algorithm may be in use. I have put the dataset file, which is the untouched original I received from the authors, in this D...

Hi Simon, Yes, if I uses coefficients() I get the same results for lm() and lm.ridge(). So that's consistent, at least. Interestingly, the "wrong" number I get from lm.ridge()$coef agrees with the value from SPSS to 5dp, which is an interesting coincidence if these numbers have no particular external meaning in lm.ridge(). Kind regards, Nick ----- Original Message -----

All, I'm a newbie to R and I am interested in seeing a simple example of calling a 3rd party Visual Studio generated DLL from RStudio. Does anyone have a simple example which also walks through the preliminary steps of setting up the INCLUDE path and the library path to either a DLL or LIB file ? I have tried to find an easy example, but thus far had no luck finding an example using Rcpp

Hi, I'm using CentOS 5 on all our desktops here (work & home), and I'm quite happy with it. There's one detail I'd like to change. GIMP comes in version 2.2. There have been some changes in version 2.4, and it's also been around for quite some time. There are quite some functions in 2.4 that I'd like to use. As far as I understand, building GIMP 2.4 would involve

...gt;> 0.07342198 -0.39650356 -0.36569488 -0.09435788 > >>coefficients(lm.ridge(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) >>ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA >> 0.07342198 -0.39650356 -0.36569488 -0.09435788 The equivalent from SPSS >>is attached. The unstandardized coefficients in SPSS look nothing like >>those in R. The standardized coefficients in SPSS match the >>lm.ridge()$coef numbers very closely indeed, suggesting that the same >>algorithm may be in use. >> >> I have put the dataset file, which is the untouched origin...