similar to: Marginal effects of ZINB models

How does one properly get find_all to grab the data from the DB table ordered by a specific column? For example: @anglers = Angler.find_all This code gets all the angler records and they are sorted in order of the id field which is of course set as a primary key in the DB (mysql). I''d really like to pull the data from the DB sorted in the order of another field, for example

Dear all.. I have a respon variable y. Predictor variable are x1, x2, x3, x4, x5 (1) What is the syntax to get paramater estimation of ZINB Model by Newton Raphson (not BFGS) (2) What syntax to plot probability of observed & predicted of ZINB Thx. Regards Krist. [[alternative HTML version deleted]]

Sorry bit of a Newbie question, and I promise I have searched the forum already, but I'm getting a bit desperate! I have over-dispersed, zero inflated data, with variance greater than the mean, suggesting Zero-Inflated Negative Binomial - which I attempted in R with the pscl package suggested on http://www.ats.ucla.edu/stat/R/dae/zinbreg.htm However my data is non-integer with some pesky

I want to generate 1,000 random samples of sample size=1,000 from ZINB. I know there is a rnegbin() to generate random samples from NB, and I know I can use the following process: do i=1 to 1000 n=0 do i=1 to 1000 if runi(1)>0.1 then x(i) = 0; else x(i)=rnegbin(); n=n+1; if n>1000 then stop; end; output; end; Anybody can help me out with the R code? Thanks very much ahead of time.

Hello, I am working with zero inflated models for a current project and I am getting wildly different results from R's zeroinfl(y ~ x, dist="negbin") command and Stata's zinb command. Does anyone know why this may be? I find it odd considering that zeroinfl(y ~ x, dist="poisson") gives identical to output to Stata's zip function. Thanks, --david [[alternative

Dear Mr.Zeileis & all. (1)     Thx for your reply. Yes, I am talk about the function zeroinfl() from the package "pscl". I want to use Newton Raphson to get parameter             estimation ZINB, so I try this: ----------------------------------------------------------------------------------------------------------------------------------         > zinb <- zeroinfl(y

Dear users, Does anyone have any idea how to generate standardised beta coefficients for a ZINB model in R to compare which explanatory variables are having the greatest impact on the dependent variable? Thanks, Jeremy [[alternative HTML version deleted]]

Hi, I am Olga Viedma. I am running a Zero-inflated negative binomial (ZINB) multi-level model using MCMCglmm package. I have a doubt. Can I use the "Liab" outputs as fitted data, instead of the predicted values from "predict"? The liab outputs fit very well with the observed data, whereas the predicted values are so bad. Thanks in advance, Olga Viedma D . Olga

Buenas tardes, Estoy estimando un modelo binomial negativo de ceros inflados (ZINB) utilizando el comando zeroinfl() del paquete pscl. Al ejecutarlo me da el siguiente aviso: Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred ¿Sabéis que significa y si puedo usar el modelo aún con ese aviso? ¿Los coeficientes son fiables? Muchas gracias, Miriam

Buenas tardes, ¿Cómo interpretarías el intercepto que da R en un modelo de ceros inflados? Por un lado en la parte de conteo tengo un intercepto de -4.2 y en la parte de ceros de 102, ambos salen significativos (***). ¿Qué me dirían? Gracias

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

Muchas gracias por la respuesta. He mirado y los coeficientes no son altos pero sí tengo una gran cantidad de ceros en la variable dependiente (más del 90%). Sin embargo, al incluir otro tipo de variables independientes no me da ese aviso, dejando la misma variable dependiente. ¿Cómo podría utilizar stan/rstan de forma sencilla para diagnosticar el modelo? Muchas gracias El Lun, 9 de Abril de

¿Quieres decir que para un nivel de una variable categorica todas las observaciones de la variable respuesta sean ceros? Gracias El Lun, 9 de Abril de 2018, 19:59, Carlos J. Gil Bellosta escribió: > ¿Podría ser que para algún nivel de alguna variable independiente > categórica solo hubiese ceros? En ese caso, casi seguro, aparecería ese > tipo de warning. > > El lun., 9 abr. 2018 a

Hola de nuevo Carlos, he probado a quitar esa variable categórica y me sigue dando el aviso... El Lun, 9 de Abril de 2018, 20:17, Carlos J. Gil Bellosta escribió: > Si, creo que el motivo del warning puede ser ese. Es hipotético, pero > plausible. Sobre todo cuando tienes más de un 90% de ceros. > > El coeficiente de ese nivel para el modelo de la mixtura (ceros vs > binomial >

I am creating an application to run a fishing tournament (see http:// www.ruby-forum.com/topic/51209 for a little background) Now I am running into an issue trying to calculate a leader board. each participate can enter multiple fish but only the largest fish per a given species counts towards the overall score. My entries table looks like this:

Hi Pengxuan, I just saw that the newly added AOSP buildbot allready builds LLVM very nicely. However, it is still red due to the following assertion: FAILED: out/target/product/angler/obj_arm/STATIC_LIBRARIES/libskia_static_intermediates/src/core/SkOpts.o /bin/bash -c "PWD=/proc/self/cwd ulimit -t 180; llvm.inst/bin/clang++ -I external/libjpeg-turbo -I external/libpng -I

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

Hello all, I have a problem estimating Random Effects model using censReg function. small part of code: UpC <- censReg(Power ~ Windspeed, left = -Inf, right = 2000,data=PData_In,method="BHHH",nGHQ = 4) Error in maxNRCompute(fn = logLikAttr, fnOrig = fn, gradOrig = grad, hessOrig = hess, : NA in the initial gradient ...then I tried to set starting values myself and here is

Dear Sirs   First herewith I'll introduce myself. My name is Kristiono, I want ask you to help me how to get  ZINB (Zero Inflated Negative Binomial) regression modeling step by step. Anyway, I get some trouble to get step by step about 1.    How to get the log likelihood function of ZINB (step by step) 2.    How to get first derivative, second derifative to get MLE by Newton Raphson (step by

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at