Statistics Help @ Talk Stats Forum - Regression Analysis
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Linear regression, linear models, nonlinear regressionenSat, 27 May 2017 03:47:09 GMTvBulletin60http://www.talkstats.com/images/misc/rss.pngStatistics Help @ Talk Stats Forum - Regression Analysis
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Deviance residuals in model fitted via MCMC
http://www.talkstats.com/showthread.php/69611-Deviance-residuals-in-model-fitted-via-MCMC?goto=newpost
Sat, 27 May 2017 02:39:13 GMTI fitted a regression model in a Bayesian approach via MCMC. The JAGS code for the model is

I want to calculate the deviance residuals for this model and make simulated envelopes. The problem is that the deviance residuals involves the square root of each contribution in the likelihood and here in some cases the likelihood is positive and anothers negative.

Since that deviance is equal to sum of deviance residuals squared, and the deviance of my model is negative, I don't know what to do.

Any help?
]]>Regression Analysisaskazyhttp://www.talkstats.com/showthread.php/69611-Deviance-residuals-in-model-fitted-via-MCMCClustering
http://www.talkstats.com/showthread.php/69593-Clustering?goto=newpost
Wed, 24 May 2017 08:25:33 GMTi) T/F:Principal Components Analysis can be used to create a low dimensional projection of the data for use with clustering.
For this one above, I...i) T/F:Principal Components Analysis can be used to create a low dimensional projection of the data for use with clustering.

For this one above, I say its false since PCA doesn't have clustering. Can someone please confirm this?

ii) T/F: Factor Analysis and Principal Components Analysis have the same objective of modeling the correlation structure in multivariate data.

For this one above, PCA is an exetension of FA, however does this mean this is True? PCA is different from FA, so I think its false

Would greatly appreciate any reply. Tried to put some effort this time. Thanks!
]]>Regression Analysismaryjkluther23http://www.talkstats.com/showthread.php/69593-ClusteringSAS question
http://www.talkstats.com/showthread.php/69592-SAS-question?goto=newpost
Wed, 24 May 2017 08:20:21 GMTHi all, I was stuck on the following. I explained it below, my approach, and would appreciate any help. Thanks so much.
1) T/F: Common factors...Hi all, I was stuck on the following. I explained it below, my approach, and would appreciate any help. Thanks so much.

1) T/F: Common factors estimated using maximum likelihood estimation with a PROMAX rotation are orthogonal.

ANSWER: This is FALSE since PROMAX requires: "With principal component analysis and rotation: You can do an oblique rotation first (oblimin, promax), and examine the component correlation matrix. When should I use rotated component with varimax and when to use maximum likelihood with promax In case of factor analysis?. Available from: https://www.researchgate.net/post/Wh...actor_analysis [accessed May 25, 2017]."

2) T/F: Common factors estimated using Iterated Principal Factor Analysis with a VARIMAX rotation are orthogonal.

Please advise. I'm confused if this changes anything, but would really appreciate any feedback. Thanks
]]>Regression Analysismaryjkluther23http://www.talkstats.com/showthread.php/69592-SAS-question