similar to: Cross-Validation for Zero-Inflated Models

Displaying 20 results from an estimated 3000 matches similar to: "Cross-Validation for Zero-Inflated Models"

2009 Jun 24
0
Goodness of fit test / pseudo r^2 measure for Zero Inflated Model
Hi I have been using a Zero-Inflated negative binomial model fitted using the pscl zeroinfl command but I would like to extract a goodness of fit measure are there any suitable pseudo R^2 measures available for this type of analysis to try and assess the amount of variation in the data explained by the model? I have tried with the pR2 command in pscl (for computing various pseudo R2
2009 Mar 22
1
Multiple Comparisons for (multicomp - glht) for glm negative binomial (glm.nb)
Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables
2008 Dec 16
1
Prediction intervals for zero inflated Poisson regression
Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.
2005 Dec 02
1
Zero-inflated neg.bin. model and pscl package
Dear list, I'm currently trying to develop a model to assess clam yield potential in a lagoon. I'm using the zeroinfl function of the pscl package to fit a Zero-inflated negative binomial model, given the high occurrence of zero counts. I don't understand from the sentence in the pscl guide "Zero-inflated count models are a type of two-component mixture model, with a component
2010 Feb 04
1
Zero inflated negat. binomial model
Dear R crew: I think I am in the right mailing list. I have a very simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is clearly overdispersed, with way too many zeroes. I'm interested in looking at the association between these two variables, i.e. how well does chick size predict tape intensity? I fit a zero inflated negat. binomial
2010 Feb 11
1
Zero-inflated Negat. Binom. model
Dear R crew: I am sorry this question has been posted before, but I can't seem to solve this problem yet. I have a simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is a count and clearly overdispersed, with way too many zeroes. I'm interested in looking at the association between these two variables, i.e. how well does chick
2011 May 23
1
Interpreting the results of the zero inflated negative binomial regression
Hi, I am new to R and has been depending mostly on the online tutotials to learn R. I have to deal with zero inflated negative binomial distribution. I am however unable to understand the following example from this link http://www.ats.ucla.edu/stat/r/dae/zinbreg.htm The result gives two blocks. *library(pscl) zinb<-zeroinfl(count ~ child + camper | persons, dist = "negbin", EM =
2012 Nov 30
0
Standard errors for predictions of zero-inflated models
Dear all, I am using the zeroinfl() function from the pscl package to develop a zero-inflated Poisson GLM. I would like to calculate the standard errors of predicted values. I've tried code posted in a previous discussion on this topic (https://stat.ethz.ch/pipermail/r-help/2008-December/182806.html), and I don't understand the results. Before I apply this code, I get the predicted value
2011 Dec 26
2
Zero-inflated Negative Binomial Error
Hello, I am having a problem with the zero-inflated negative binomial (package pscl). I have 6 sites with plant populations, and I am trying to model the number of seeds produced as a function of their size and their site. There are a lot of zero's because many of my plants get eaten before flowering, thereby producing 0 seeds, and that varies by site. Because of that and because the
2012 May 16
1
clusters in zero-inflated negative binomial models
Dear all, I want to build a model in R based on animal collection data, that look like the following Nr Village District Site Survey Species Count 1 AX A F Dry B 0 2 AY A V Wet A 5 3 BX B F Wet B 1 4 BY B V Dry B 0 Each data point shows one collection unit in a certain Village, District, Site, and Survey for a certain Species. 'Count' is the number of animals collected in that
2006 Jul 20
0
Convergence warnings from zeroinfl (package pscl)
Dear R-Helpers, Can anyone please help me to interpret warning messages from zeroinfl (package pscl) while fitting a zero inflated negative binomial model? The console reports convergence and the parameters seam reasonable, but these <<Warning messages: 1: algorithm did not converge in: glm.fit(X, Y, family = poisson()) 2: fitted rates numerically 0 occurred in: glm.fit(X, Y, family =
2009 Jan 22
1
help using zeroinfl()
Hi all, I have been trying to use zeroinfl() with the pscl package with R version 2.1.1. and with the newest versions of the contrib packages compatible with R 2.1.1. I have read the examples, the vignette and all the posts relating to zeroinfl() but I am still confused as to how to structure the model. Here is a small example; the error message is the same for big data sets
2004 Oct 09
0
RE: zero-inflated count models (was polr problem solved)
John Fox wrote <<< >From your description, it seems possible that there are too many zeros for a Poisson or negative-binomial model. Since the focus of your paper is the methodology, you might want to try a zero-inflated Poisson or negative-binomial model. Though I haven't tried them, I'm aware of two sources of R functions for zero-inflated count models -- zeroinfl(), from
2024 Jan 04
1
Obtaining a value of pie in a zero inflated model (fm-zinb2)
Are you referring to the zeroinfl() function in the countreg package? If so, I think predict(fm_zinb2, type = "zero", newdata = some.new.data) will give you pi for each combination of covariate values that you provide in some.new.data where pi is the probability to observe a zero from the point mass component. As to your second question, I'm not sure that's possible, for any
2008 Sep 03
2
ANCOVA/glm missing/ignored interaction combinations
Hi I am using R version 2.7.2. on a windows XP OS and have a question concerning an analysis of covariance with count data I am trying to do, I will give details of a scaled down version of the analysis (as I have more covariates and need to take account of over-dispersion etc etc) but as I am sure it is only a simple problem but I just can't see how to fix it. I have a data set with count
2009 Jul 20
1
randomForest - what is a 'good' pseudo r-squared?
Hi all I have been trying to use the randomForest package to model insect species abundance in different habitats and identify the key variables (landscape/climate etc) in determining abundance, which has all worked fine and I get nice variable importance plots etc. Many thanks to everyone on this help forum who has given tips/advice along the way. But the percentage variance explained /pseudo r
2005 Mar 11
0
Negative binomial regression for count data,
Dear list, I would like to know: 1. After I have used the R code (http://pscl.stanford.edu/zeroinfl.r) to fit a zero-inflated negative binomial model, what criteria I should follow to compare and select the best model (models with different predictors)? 2. How can I compare the model I get from question 1 (zero-inflated negative binomial) to other models like glm family models or a logistic
2010 Mar 03
1
Zero inflated negative binomial
Hi all, I am running the following model: > glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro) where Year has 3 levels, Trt has 2 levels and Micro has 3 levels. However when I run it has a zero inflated negative binomial (as I have lots of zeros) I get the below error message: > Zinb <- zeroinfl(AvGUD ~ Year*Trt*Micro |1, data = AvGUD89, dist = "negbin") Error in optim(fn =
2012 Jul 13
1
Vuong test
Dear All, I am using the function vuong from pscl package to compare 2 non nested models NB1 (negative binomial I ) and Zero-inflated model. NB1 <-  glm(, , family = quasipoisson), it is an object of class: "glm" "lm" zinb <- zeroinfl( dist = "negbin") is an object of class: "zeroinfl"   when applying vuong function I get the following: vuong(NB1,
2009 Jun 24
1
Random Forest Variable Importance Interpretation
Hi I am trying to explore the use of random forests for regression to identify the important environmental/microclimate variables involved in predicting the abundance of a species in different habitats, there are approx 40 variable and between 200 and 500 data points depending on the dataset. I have successfully used the randomForest package to conduct the analysis and looked at the %IncMSE