similar to: GAM without intercept reports a huge deviance

Hi all, This is pretty basic but I am not an expert and I couldn't find anything in the forum or my statistics book about it. I was reading a paper and the authors were using both "explained deviance" and "explained variance" as synonyms. They were describing a GAM regression. Is that right? I performed an analysis in R to take a look to the output of GAM regression and I

A maybe trivial and stupid question: In the case of a lm or glm fit, it is quite informative (to me) to have a look to the null deviance and the residual deviance of a model. This is generally provided in the print method or the summary, eg: Null Deviance: 658.8 Residual Deviance: 507.3 and (a bit simpled minded) I like to think that the proportion of deviance 'explained' by the

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

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Hi Everyone, I'm working on building some classification trees, and up to this point I've been using rpart. However, I recently discovered the tree package, and found that it had some useful functions (in particular deviance(), which I would really like to use for my project). I can't seem to find an equivalent function for rpart. I've considered using tree() in place of

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:

It seems that the standardized deviance residulas, that one gets on plots of a glm.object x with plot(x) are calculated as r <- residuals(x) s <- sqrt(deviance(x)/df.residual(x)) w <- weights(x) hii <- lm.influence(x)$hat r.w <- if (is.null(w)) r else (sqrt(w) * r) rs <- r.w/(s * sqrt(1 - hii)) This implies that, for example, for binomial B(ni,pi) data the devaince residials

Hi all, I'd like to fit different models using a loop together with the jags package. to do this, I load the following packages "runjags", "R2jags" and "coda" and use the function "jags" to fit my models. The problem is that the following a windowx with the following message appear between each model fitting inside my loop : the following objects in

I have used packages rpart, mvpart and tree for classification and regression trees. I want to calculate fraction of null deviance explained by each node and variable in the tree. For instance, at the first split, this would be (1 - (sum of residual deviance in each of the two leaves)/deviance at the root). In the subsequent splits, this formula is slightly different. There probably is a function

Hi, I am doing a regression tree using the package 'rpart' but could not able to calculate the residual mean deviance. Please help. Narayan [[alternative HTML version deleted]]

Dear all, I would like to get glm() return its results (at least the deviance) for some given parameter values (ie without actually fitting the model). I tried to set `maxit = 0' but this does not work, eg: > glm(y ~ x, start = c(1, 1), maxit = 0) Error in glm.control(...) : maximum number of iterations must be > 0 Any idea? Thanks in advance. Emmanuel Paradis

dear List: glm(a~b+c,family=binomial,data=x)->fit deviance(fit) returns the same as the residual deviance. I don't not know much about logistic regression.Some book tells that: " Deviance (likelihood ratio statistic): Deviance = -2log( likelihoodof the currentmodel /likelihoodof thesaturated model) Note: (1). The current model is the model of interest. (2). The saturated model

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

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

Dear all, I am trying to calculate some measure of the amount of variability in the response variable that is explained by a model fitted in lmer m1<-lmer(response-var ~ Condition+(1|Site/Area/Transect),family="binomial") . I've seen from the literature that the precentage of explained deviance is a common measure. How can I calculate it? Thanks a lot for your help, I hope this

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

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),

I have experimented with the cheese data example from McCullagh&Nelder, page 175. With a proportional odds model they obtain a residual deviance of 20.31. Estimating the same model with polr(MASS) gives a residual deviance of 762.11 !, while using ordglm(gnlm) gives a deviance of 523.94. Can anybody explain these differences? The data frame with the data are: > cheese Cheese N

Dear List, If I got a Cox model based on training set, then how should I calculate the Cox log partial likelihood for the test data? Actually I am trying to calculate the deviance on test dataset to evaluate the performance of prediction model, the equation is as follows: D = -2{L(test)[beta_train] - L(test)[0]}. It means using the beta coefficients got from training set to calculate the

Hello all. I have heard over and over that CART and its various tree-like brethren are "non-parametric" techniques. When I read the chapter in Chambers and Hastie on tree-based models it states that tree-based models can be generalized (GTMs) in a manner similar to GLMs by specifying a different deviance function to distributions other than the gaussian error distribution ( section