similar to: comparing AIC values of models with transformed, untransformed, and weighted variables

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Dear All, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) Now for a weighted fit: fm2DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal),

Hello, I am a new R user and I am trying to estimate some generalized linear models (glm). I am trying to compare a model with a gaussian distribution and an identity link function, and a poisson model with a log link function. My problem is that while the gaussian model has significantly lower (i.e. "better") AIC (Akaike Information Criterion) most of the coefficients are not

Hello, I am comparing models made with nlme functions and non-nlme functions, based on Akaike's AIC. The AIC values I get for exactly the same model formulation --for example a linear model with no random effects fit with gls and lm, respectively-- do not fit, although the values of the four model parameters are exactly the same. For example: m1 <- gls(height ~ age, data = Loblolly) m2

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

Greetings, On 3/24/11, Akemi Yagi <amyagi at gmail.com> wrote: > I should note, however, that they are not for production use. > > Akemi Thanks Akemi very much for your always relevant and brilliant works. </rant> As an Indian, where "veda" repository of knowledge originated, I have always looked at every member of this list as a repository of knowledge.

http://bugzilla.mindrot.org/show_bug.cgi?id=742 Summary: sftp doesn't honor "Protocol 1" in config file Product: Portable OpenSSH Version: 3.6.1p2 Platform: All OS/Version: Linux Status: NEW Severity: normal Priority: P2 Component: sftp AssignedTo: openssh-bugs at mindrot.org

I'm running: Asterisk CVS-HEAD-07/06/04-17:49:49 built by root@gf-002-pbx-001 on a i686 running Linux I've tried placing files (both ending in .call and not) in the correct format in /var/spool/asterisk/outgoing.... I get _nothing_. No log messages, nothing on the console, zip. Permissions seem to be correct on both the files and the directory, as well (* is running as root, for right

I know that this is not the appropriate place :). You know someone can alternative to gluserfs ?:) -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://supercolony.gluster.org/pipermail/gluster-users/attachments/20090107/63b68a0d/attachment.html>

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

I'm trying to plot and compare weighted histograms and I can't seem find where to start. I have data similar to this: Miles2LAX RADAM2005Pct LAWA2005Pct 35.57 .00123 .00684 24.74 .00118 .00187 27.09 .00965 .00876 16.23 .00587 .00397 { ...

Hello, I'd like to sum the weights of each independent variable across linear models that have been evaluated using AIC. For example: > library(MuMIn) > data(Cement) > lm1 <- lm(y ~ ., data = Cement) > dd <- dredge(lm1, beta = TRUE, eval = TRUE, rank = "AICc") > get.models(dd, subset = delta <4) There are 5 models with a Delta AIC Score of

Was it ever a part of Centos? Supposedly I need it and my colleague is providing me with an rpm, but he implies that 'seems to have been removed from Centos'?

Dear All 1) I'm fitting spatial CAR models using R2Winbugs and although everything seems to go reasonably well (or I think so) the next message appears from WINBUGS 1.4 window: gen.inits() Command #Bugs: gen.inits cannot be executed (is greyed out) The question is if this message means that something is wrong and the results are consequently wrong, or Can I assume it as a simple warning

I have used the glm function to fit a series of models using a poisson error structure. e.g: Model 1: Y is a function of a + bX Model 2: Y is a function of a I have tried to compare models using AIC, but do not get sensible results (lower AICs for the null, intercept only, model despite the alternate model containing highly significant parameters). I found the following explanation in the

Hi, I'm slowly working through Tsay's "Analysis of Financial Time Series" 3rd ed. ?I'm trying to replicate Table 2.1 on p.47, which gives PACF, AIC, and BIC for the monthly simple returns of the CRSP value-weighted index. The data: http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/m-ibm3dx2608.txt > da <-

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

>>>>> Ben Bolker >>>>> on Sun, 3 Jun 2018 17:33:18 -0400 writes: > Is it generally known/has it been previously discussed here that the > $aic() component in GLM-family objects (e.g. results of binomial(), > poisson(), etc.) does not as implemented actually return the AIC, but > rather -2*log-likelihood + 2*(model_has_scale_parameter)

Hi. I'm fairly new to R and have a quick question regarding AIC, logLik and numbers of parameters. I see that there has been some correspondence on this in the past but none of the threads seem to have been satisfactorily resolved. I have been trying to use R to obtain AIC for fitted models and then to extrapolate to AICc. For example, using simple x-y regression data, I fitted a

Hi everyone, How can i obtain R2 for SAR model? and how can i validate the results, can i use the coefficients directly in a simple formula like y=b0+b1*x1+... or do i have to use the complicated formula for SAR (the one with the weight matrix and rho and...)?   Thanks for any help!  [[alternative HTML version deleted]]