similar to: adding deviances as meta-analysis?

Dear all I'm building logistic regression models using glm() and then validating their predictive ability on a test set using predict() to get the probabilities. I understand how to attain the deviance of the model fitted to the training set. But is there a way to get the deviance within the test set? Cheers Tom -- Thomas Allen Department of Biochemistry University of Otago 710 Cumberland

- Deze mail is doorgezonden omdat iemand dacht dat u wellicht interesse zou kunnen hebben. Mocht u na het lezen van deze mail geen interesse hebben dan kunt u de boodschap verwijderen, u ontvangt geen verdere berichten meer tenzij u daar uitdrukkelijk om verzoekt. Computervrienden De club computervrienden gaat zich lanceren in mei 2002. Voordat deze datum wordt bereikt willen wij het volgende

I'm running a categorical data analysis with a two-way design of nominal by ordinal structure like the Political Ideology Example (Table 9.5) in Agresti's book Categorical Data Analysis. The nominal variable is Method while the ordinal variable is Quality (Bad, Moderate, Good, Excellent). I rank/quantify Quality with another variable QualityR (1, 2, 3, 4), and run the following:

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

Running a binary logit model on the data df <- data.frame(y=sample(letters[1:3], 100, repl=T), x=rnorm(100)) reveals some residual deviance: summary(glm(y ~ ., data=df, family=binomial("logit"))) However, running a multinomial model on that data (multinom, nnet) reveals a residual deviance: summary(multinom(y ~ ., data=df)) On page 203, the MASS book says that "here the

Hi: I'm working with multinomial models with library nnet, and I'm trying to get the explained deviance (pseudo R^2) of my models. I am assuming that: pseudo R^2= 1 - dev(model) / dev (null) where dev(model) is the deviance for the fitted model and dev(null) is the deviance for the null model (with the intercept only). library(nnet) full.model<- multinom(cbind(factor1,

I am comparing the Splus and R fits of a simple glm. In the following, foo is generated from rbinom with size = 20 p = 0.5. The coefficients (and SE's0 of the fitted models are the same, but the estimated deviances are quite different. Could someone please tell me why they are so different? I am using R version 1.3.1 and Splus 2000 release 3 on windows 2000. ++++++++++++++++++++++ foo

Hey,I have recently set up Dovecot on my server but I have trouble with using it. I'm using Dovecot 1.0.rc15, SQL password and user database with the SQL config files provided on the home page. I'm using Thunderbird to connect to Dovecot. When trying to connect it gives me an internal server error: User not found Is there anything else I need to do before I can use Dovecot? Here's

Dear R-users, To obtain the percentage of deviance explained when fitting a gam model using the mgcv library is straightforward: summary(object.gam) $dev.expl or alternatively, using the deviance (deviance(object.gam)) of the null and the fitted models, and then using 1 minus the quotient of deviances. However, when a gamm (generalizad aditive mixed model) is fitted, the

I want to subscribe myself to the mailing list. And I have a question. Where can i ask it?? I want to make Linux Fedora Core 2 my file server I already connected it to a Windows2000 Domain. But Now when users login they have to acces their own user folders. Without having to login again. So that they login on windows they can acces it directley. greet Dylan van Loenhout

An idea would be like for WMA 9 lower bitrates (32-42-48Kbps) to use a 'crystallizer'; which is basically a harmonics booster focussed at transposing sharp tones some octaves higher. Eg: A file has been recorded @ 20khz computer (or 10khz real life) to preserve space. While playing back the file sounds a bit mushy, almost as if someone was speaking through a cardboard wall. The higher

yeah, tnx very much, i think the problem is solved now, i thought idd it was something with the mp3's but i didn't knew what. >Hi: > >Well, I've tuned in and not struck the problem as yet. But I'm guessing >that the problem is that the problematic songs aren't at the same sampling >rate. The three songs I've heard so far are at 32kbps 11.025khz stereo.

Dear all, I'm trying to fit a statistical model to series of measurements. Unfortunately, my knowledge of statistics is rather limited, so I'm a bit at loss of what is going on with the model. First of all, I've prepared a histogram. Then, I've tried to fit a Poisson model to express the relation between the middle points of classes (mids) and the corresponding frequencies

Need help with setup/config. Current situation: (dovecot version : 1.1.7) we have system users and virtual users. (with different domains) Virtual users have procmail as LDA Now we want "deliver" to become the LDA I'm confused reading http://wiki.dovecot.org/LDA/Sendmail sendmail.mc: divert(-1) divert(0)dnl include(`/usr/share/sendmail-cf/m4/cf.m4')dnl

Hi All, Currently I am investigating how interesting OGG is for me. The most important requirements are: - Decoding on embedded hardware, ARM7 or ARM9. - Mixing multiple audio streams/files on embedded hardware First of all how difficult is it to decode OGG on an ARM7/9 processor? I read that OGG decoding needs floating point support? Secondly I would like to know how many MIPS are needed to

Hi, I'm having problems logging in with a w2k prof and server into my Samba3 domain. I've found some faults in the log file but i can't find the answers. If somebody could help me it would be a great help. extracts from the log file with debug level 5 [2003/12/15 17:12:20, 3] lib/util_seaccess.c:se_access_check(267) [2003/12/15 17:12:20, 3] lib/util_seaccess.c:se_access_check(268)

Hello I have a problem when fitting a mixed generalised linear model with the lmer-function in the Matrix package, version 0.98-7. I have a respons variable (sfox) that is 1 or 0, whether a roe deer fawn is killed or not by red fox. This is expected to be related to e.g. the density of red fox (roefoxratio) or other variables. In addition, we account for family effects by adding the mother

Hi -- I have found that by default the function print.glm() uses 3 significant figures when printing out null and model deviances and the aic. Of course, this is not wrong. But if a person fitted two nested models and compared the resulting deviances obtained from print.glm(), the resulting hypothesis test could indeed be wrong because of this rounding. The function summary() applied to a glm

Dear R users, I read your methods of extracting the variance explained by each predictor in different places. My question is: using the method you suggested, the sum of the deviance explained by all terms is not equal to the deviance explained by the full model. Could you tell me what caused such problem? > set.seed(0) > n<-400 > x1 <- runif(n, 0, 1) > ## to see problem

Dear list, I'm wondering if anyone can help me calculate the deviance of either a zeroinfl or hurdle model from package pscl? Even if someone could point me to the correct formula for calculating the deviance, I could do the rest on my own. I am trying to calculate a pseudo-R-squared measure based on the R^{2}_{DEV} of [1], so I need to be able to calculate the deviance of the full and null