search for: nagelkerkes

Dear R community. I´m working with a generalized linear model which the response variable is a 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

I am interested 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

Hi there, I really tried hard to understand and find my own solution, but now I think I have to ask for your help. I already developed some script code for my problem but I doubt that it is correct. I have the following problem: Image you develop a logistic regression model with a binary outcome Y (0/1) with possible preditors (X1,X2,X3......). The development of the final model would be

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

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, I...

Hi there! We would like to share the news that our package *modelplotr *has recently been added to CRAN with your audience. Here's a some text for the introduction: --------------------- *Title:* *modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models * *Visual:* https://modelplot.github.io/img/modelplotr_CRAN-topviz-1.gif *Intro text:* Modelplotr - Build

Hi there! We would like to share the news that our package *modelplotr *has recently been added to CRAN with your audience. Here's a some text for the introduction: --------------------- *Title:* *modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models * *Visual:* https://modelplot.github.io/img/modelplotr_CRAN-topviz-1.gif *Intro text:* Modelplotr - Build

Dear all, I have been using the function gls in the package nlme in R to fit some spatial regressions (as described in Dormann et al.). However, I have been struggling trying to find a way to calculate a measure of effect size from these models, so I wanted to know if any of you had an idea on how to do this. More precisely, I am producing a multiple model with an exponential correlation

I think you've badly misinterpreted the purpose of the R listserv with this request: https://www.stat.math.ethz.ch/mailman/listinfo/r-help says "The `main' R mailing list, for announcements about the development of R and the availability of new code, questions and answers about problems and solutions using R, enhancements and patches to the source code and documentation of R,

Hi all, I'm working on a multinomial (or "polytomous") 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

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

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

Dear R-users, I used lm() to fit a standard linear regression model to a given data set, which led to a coefficient of determination (R^2) of about 0.96. After checking the residuals I realized that they follow an autoregressive process (AR) of order 1 (and therefore contradicting the i.i.d. assumption of the regression model). I then used gls() [library nlme] to fit a linear

Dear list, Inspecting residuals of my linear models, I detected spatial autocorrelation. In order to take this into account, I decided to use the GLS method with the correlation = corGaus ( ~ X + Y). Then, I can sort my GLS models based on their AIC. But ... how to know the proportion of the variance explained by the best one (it can be best of the worst models) ? R-squared value has not the

Hello. I am running 9 poisson regressions with 5 predictors each, using glm with family=gaussian. Gaussian distribution fits better than linear regression on fit indices, and also for theoretical reasons (e.g. the dependent variables are counts, and the distribution is highly positively skewed). I want to determine pseudo R^2 now. However, using the pR2() of the pscl package offers drastically

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