search for: nagelkerk

...categorical one and the predictive variables are weather conditions. I have 250 different places where I need to fit the model. In some of these places I have strong correlations between some of the variables so I need to deal with this problem. I found a work similar than mine where they use tha Nagelkerke R2 coefficient of determination to deal with multicollinearity. Has anyone ever used this coefficient of determination in R? I need to know how to use it and interpreted in my work. If anyone can help me I´d really appreciate it. Greetings Lucas. [[alternative HTML version deleted]]

...ested Andrea is whether you ever established why your R2 was 1. I have had a similar situation previously. My main issue though, which I'd be v grateful for advice on, is why I am obtaining such negative values -0.3 for Somers Dxy using validate.cph from the Design package given my value of Nagelkerke R2 is not so low 13.2%. I have this output when fitting 6 variables all with p-values<0.01 I am wondering what the interpretation should be. I know my Nagelkerke R2 isn't very good but I compare my results with the example from ?validate.cph and although I have a better R2 (13% v 9%) the S...

...model would be quite complex and undertake several steps (stepwise forward selection with LR-Test statistics, incorporating interaction effects etc.). The final prediction at the end however would be through a glm object (called fit.glm). Then, I think so, it would be no problem to calculate a Nagelkerke R squared measure and an AUC value (for example with the pROC package) following the script: BaseRate <- table(Data$Y[[1]])/sum(table(Data$Y)) L(0)=Likelihood(Null-Model)= (BaseRate*log(BaseRate)+(1-BaseRate)*log(1-BaseRate))*sum(table(Data$Y)) LIKM <- predict(fit.glm, type="response...

Hello I found some small postings dated to 22 Oct 2008 on the message subject. Recently, I have been working with binary logistic regressions. I didn't use the design package. Yet, I needed the "fit" indices. Therefore, I wrote a small function to output the Nagelkerke's R, and the Cox-&-Snell R from a fitted model. I am no professional programmer by far, yet, I hope, that the code is okay and that it may be of some use to others -- or subject to useful improvement. Please let me know, if you find errors. Regards, S?ren Rcsnagel <- function(mod) {...

Hello All, as I?m new to R and survival analysis, I?ve got a question about the Design::validate function: My Code: cox <- cph(Surv(t,status) ~ var1 + var2 + var3, data=data, x=TRUE, y=TRUE, surv=TRUE) cox.val <- validate(cox, B=10, dxy=TRUE, pr=TRUE); My output (cox.val): index.orig training test Dxy -0.3639222921368090891

hello dear list! since we want to do a model analysis and some people would like to see pseudo-R^2 values for different types of glm of a logistic regression, i've decided to write a function that computes either nagelkerkes normed pseudo-R or cox & snells pseudo-R. however, i am not clear as in the decisive step, i need to calculate the log of (maximum likelihood estimates of model divided by mle of null model). i am well aware of the functions stats::mle and stats::logLik as well as of Design::lrm. however,...

...hub.io/intro_modelplotr.html ) as well as more info on how to use modelplotr ( https://modelplot.github.io/vignette_modelplotr.html ) Questions, remarks, suggestions? Please let uw know via github! --------------------- Hope you like to share this with your R audience! Kind regards, Jurriaan Nagelkerke jurriaan.nagelkerke at gmail.com [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

...hub.io/intro_modelplotr.html ) as well as more info on how to use modelplotr ( https://modelplot.github.io/vignette_modelplotr.html ) Questions, remarks, suggestions? Please let uw know via github! --------------------- Hope you like to share this with your R audience! Kind regards, Jurriaan Nagelkerke jurriaan.nagelkerke at gmail.com [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

...: spatial.gls<-gls(Y~X+Z, correlation=corExp(form=~long+lat)) I know that in this kind of models one possible measure of effect size is a pseudo R^2 value, so I have been calculating it like this: null.gls<-gls(Y~1) yN<-length(Y) lL.B<-logLik(spatial.gls) lL.0<-logLik(null.gls) Nagelkerke<-(1-exp((lL.B-lL.0)*(-2/yN)))/(1-exp((lL.0)*(2/yN))) Nagelkerke[1] However, the value that is produced is extremely close to 1 (>0.97), very different from the R^2 produced by a simple linear model model: no.spatial.lm<-lm(Y~X+Z) or equivalently by the pseudo R^2 as calculated above o...

...s, and for the posting of nice examples and benchmarks." -----Original Message----- From: Ronaldo Reis Jr. [mailto:chrysopa at insecta.ufv.br] Sent: Wednesday, August 27, 2003 10:04 AM To: R-Help Subject: Re: [R] how to calculate Rsquare Can anybody send these articles for me? > NagelKerke, N. J. D. (1991) "A note on a general definition of the > coefficient of determination", Biometrika 78: 691-2. > > Cox, D. R. and Wermuth, N. (1992) "A comment on the coefficient of > determination for binary responses", The American Statistician 46: > 1-4...

...) logistic regression using R and have made great progress using multinom() from the nnet library. My response variable has three categories, and there are two different possible predictors. I'd like to use the likelihoods of certain models (ie, saturated, fitteds, and null) to calculate Nagelkerke R-squared values for various fitted models. My question today is simple: once I have fitted a model using multinom(), how do I find the likelihood (or log likelihood) of my fitted model? I understand that this value must be part of the $deviance or $AIC components of the fitted model, but my...

Dear R help My problem is very similar to the analysis detailed here. If we use the mayo dataset provided with the survivalROC package the estimate for Somer's Dxy is very negative -0.56. The Nagelkerke R2 is positive though 0.32. I know there is a difference between explained variation and predictive ability but I am surprised there is usch a difference given that even a non predictive model should have Dxy around 0. Am I doing something wrong or is there an interpretation that makes sense ? Th...

I am using GLM to calculate logit models based on cross-sectional data. I am now down to the hard work of making the results intelligible to very average readers. Is there any way to calculate a psuedo analoque to the R^2 in standard linear regression for use as a purely descriptive statistic of goodness of fit? Most of the readers of my report will be vaguely familiar and more comfortable with

Greetings, I am using both the lrm() {Design} and glm( , family=binomial()) to perform a a logisitic regression in R. Apart from the typical summary() methods, what other methods of diagnosing logistic regression models does R provide? i.e. plotting an 'lm' object, etc. Secondly, is there any facility to calculate the R^{2)_{L} as suggested by Menard in "Applied Logistic

...08-04-24]: "Bei bestimmten statistischen Modellen, z.B. bei Maximum-Likelihood-Sch?tzungen, existiert das Bestimmtheitsma? R^2 nicht"). Any comments on that? The German Wikipedia page mentions McFadden's pseudo-coefficient of determination, the English Wikipedia page the one of Nagelkerke. I know there are others, too. Is there a general agreement on which "coefficient of determination" (or goodness-of-fit measure in general) to use for a regression model with autocorrelated errors? Is there a possibility to compare (non-graphically) the standard regression mode...

...ning for OLS and GLS ... - Could the R2 value calculated with the OLS model (using lm) constitute a potential proxy of the variance explained by the GLS model ? (the answer is probably no) - Is a R-squared based on sqrt(cor(obs, predicted)) a better approach ? - What about pseudo R-squared like Nagelkerke's ? Suggestions for any better approach are welcome ! Thanks in advance, Arnaud

...41 m8 0.04 0.23 0.13 0.28 m9 0.07 0.31 0.12 0.36 The first column represents r^2 values obtained by linear regression models, then comes McFadden pseudo r^2, maximum likelihood pseudo r^2, and Cragg and Uhler's pseudo r^2. As far as I understand, Cragg and Uhler's pseudo r^2 is similar to Nagelkerke, which, in contrast to the other pseudo r^2 values, offers a range between 0 and 1, so that should be my comparison. And the values are between 3 and 7 times higher than the original r-square values. My dependent variables self reported single questionnaire items of a screening instrument for men...

I do not see an R-squared value after preforming the glm regression. Is there a separate command for this? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Obtaining-R-squared-value-in-Logistic-Regression-tp4648954.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have something like this: > x <- 1:10 > y2 <- 30+5*x+rnorm(x,sd=3) > y <- c(y1,y2) > x <- c(x,x) > plot(x,y) > x <- 1:10 > y1 <- 1+5*x+rnorm(x,sd=2) > y2 <- 30+5*x+rnorm(x,sd=5) > y <- c(y1,y2) > x <- c(x,x) > f <- factor(rep(c("a","b"),c(10,10))) > m <- lm(y~x+f) > anova(m) Analysis of Variance

Dear all, How do I make R show the R-squared (deviance explained by the model) in a logistic regression? Below is how I write my syntax. Basically I want to investigate density-dependence in parasitism of larvae. Note that in the end I perform a F-test because the dispersion factor (residual deviance / residual df) is significantly higher than 1. But how do I make R show the