similar to: Calculating AICc using conditional logistic regression

--begin inclusion -- I have a matched-case control dataset that I'm using conditional logistic regression (clogit in survival) to analyze. I'm trying to conduct k-folds cross validation on my top models but all of the packages I can find (CVbinary in DAAG, KVX) won't work with clogit models. Is there any easy way to do this in R? -end inclusion -- The clogit funciton is simply a

Hi, I am playing with clogit(case~spontaneous+induced+strata(stratum),data=infert) from clogit help file. This line works. 1. But, why strata(stratum) doesn't have a coefficient like spontaneous and induced? 2. When I remove strata(stratum) from the command, this function seems to keep running forever. Why? 3. I think the equation for clogit looks like P=1/(1+

Dear R-helpers, I am very new to R and trying to run the conditional logit model using "clogit " command. I have more than 4000 observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it

Dear R-help, I'm doing conditional logit regression for a discrete choice model. I want to know whether there's a way to get the fitted probabilities. In Stata, "predict" works for clogit, but it seems that in R "predict" does not. Thank you very much! Best wishes. Sincerely, Min -- Min Chen Graduate Student Department of Agricultural,

I am fitting logistic regression models, by defining my own link function, and would like to get AICc values. Using the glm command gives a value for AIC, but I haven't been able to get R to convert that to AICc. Is there a code that has already been written for this? Right now I am just putting the AIC values into an excel spreadsheet and calculating AICc, likelihood, and AIC

I would like test AICc as a criteria for model selection for a glm using stepAIC() from MASS package. Based on various information available in WEB, stepAIC() use extractAIC() to get the criteria used for model selection. I have created a new extractAIC() function (and extractAIC.glm() and extractAIC.lm() ones) that use a new parameter criteria that can be AIC, BIC or AICc. It works as

Hi, I'm fitting poisson regression models to counts of birds in 1x1 km squares using several environmental variables as predictors. I do this in a stepwise way, using the stepAIC function. However the resulting models appear to be overparametrized, since too much variables were included. I would like to know if there is the possibility of fitting models by steps but using the AICc

How can I extract K (number of parameters) from an AIC calculation, both to report K itself and to calculate AICc? I'm aware of the conversion from AIC -> AICc, where AICc = AIC + 2K(K+1)/(n-K-1), but not sure of how K is calculated or how to extract that value from either an AIC or logLik calculation. This is probably more of a basic statistics question than an R question, but I thank

Hi I get the following error when I try and get the AICc for a gls regression using qpcR: > AICc(gls1) Loading required package: nlme Error in n/(n - p - 1) : 'n' is missing My gls is like this: > gls1 Generalized least squares fit by REML Model: thercarnmax ~ therherbmax Data: NULL Log-restricted-likelihood: 2.328125 Coefficients: (Intercept) therherbmax 1.6441405

Hi I'm trying to replicate Smith et al.'s (http://www.sciencemag.org/content/330/6008/1216.abstract) findings by fitting their Gompertz and logistic models to their data (given in their supplement). I'm doing this as I want to then apply the equations to my own data. Try as a might, I can't quite replicate them. Any thoughts why are much appreciated. I've tried contacting the

Hi, I am using 'best.arima' function from forecast package to obtain point forecast for a time series data set. The documentation says it utilizes AIC value to select best ARIMA model. But in my case the sample size very small - 26 observations (demand data). Is it the right to use AIC value for model selection in this case. Should I use AICc instead of AIC. If so how can I modify

Dear members of the list, I'm fitting poisson regression models using stepAIC that appear to be overparametrized. I would like to know if there is the possibility of fitting models by steps but using the AICc instead of AIC. Best wishes German Lopez

I would like to know if clogit function can be used as below clogit(group~., data=dataframe) When I try to use in above format it takes a long time, I would appreciate some pointers to get multiple combinations tested. set.seed(100) d=data.frame(x=rnorm(20)+5, x1=rnorm(20)+5, x2=rnorm(20)+5, x3=rnorm(20)+5, x4=rnorm(20)+5, x5=rnorm(20)+5, x6=rnorm(20)+5, x7=rnorm(20)+5, x8=rnorm(20)+5,

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

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Hello, I am modelling in glmmADMB count data (I´m using a negative binomial distribution to avoid possitive overdispersion) with four fixed and one random effect. I´m also using MuMIn package to calculate the AICc and also to model averaging using the function dredge. What I do not understand is why dredge calculates a different value of the AICc and degrees of freedom than the function AICc

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Dear R-helpers I have generated a suite of GLMs. To select the best model for each set, I am using the meta-analysis approach of de Luna and Skouras (Scand J Statist 30:113-128). Simply put, I am calculating AIC, AICc, BIC, etc., and then using whichever criterion minimizes APE (Accumulated Prediction Error from cross-validations on all model sets) to select models. My problem arises where I

Hi there, I have recently updated to ver 2.10 (windows - I'm running xp) and find I am having problems with plot arguments, for e.g. Using the errbar function the error bars are now in black despite col="red", the central point is in red though. Axis labels are drawn but not the 'main' title. No errors are reported. Using the plot function directly I was only able to

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias