search for: estiamtes

Dear all, I have some questions for count data. First, I want to calculate the expectation value and variance from truncated data when I assume that the truncated data follow Poisson or negative binomial distribution. For example, N freq 1 30 2 35 3 26 4 8 5 0 6 2 7 0 The mean of N is 2.2. If the data follows Poisson distribution, the estimated lambda is 1.9. Is there any package to

Hi, I would like to know if anyone knows how to compute a variance of the non-linear combination of the coefficient estimates. Say, I get a model of y~c+ax+bz (1) where x and z are the independent variables, c is the constant estimate, and a and b are the coefficient estimates. Then, I want to know the variance of b*c/a (2). How am I going to get it? In Stata, I can just use

Hey, I am estiamting a partially linear model y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets. Has anyone heard of methods to test if the betas are significant or to address model fit? Thanks for any thoughts or comments. Elizabeth Lawson __________________________________________________ [[alternative HTML version deleted]]

I am using arima to develop a time series regression model, I am using arima b/c I have autocorrelated errors. Several of my independent variables are categorical and I have coded them as factors . When I run ARIMA I don't get any warning or error message, but I do not seem to get estimates for all the levels of the factor. Can/how does ARIMA handle factors in xreg? here is some example

Hello ; I am doing a regression of count data (number of award and there are some covariates) I have estiamted the parameters of negative binomial distribuion (lambda is a function of covaraites, GLM model) by glm.nb function and training dataset. Now I want to predict the number of award (for example y=0, y=1, y=2,) or testing dataset. I dont know how to calculate this numbers? I would be very

Hi, I am trying to estiamte parameters for gamma distribution using mle for below data using fitdist & fitdistr functions which are from "fitdistrplus" & "MASS"packages . I am getting errors for both functions. Can someone please let me know how to overcome this issue?? data y1<- c(256656, 76376, 6467673, 46446, 3400, 3100, 5760, 4562, 8000, 512, 4545, 4562,

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the estimate values at each iteration. Deepankar

Hi, I have a problem with file transfers between a windows systems and unix systems. I have one win32 desktop (intel e6400 2Gb Ram), one win32 laptop (p-m 2Ghz). Also one linux laptop (p-m 1.4GHz) and one opensolaris desktop (intel e4400 1GB Ram). The two laptops have built-in 100Mbit ethernet and desktops have 1Gbit ethernet on the motherboard. Both desktops use a Marvell Yukon. The file

...of problems: 1. Varying the initial estimates of the ARMA parameters results in different fitted models and, in particular, the values of minus log-likelihood. How can I choose a "best" model? Some fitted models return NaN for some of the se's and/or correlations of the parameter estiamtes so presumably these can be ruled out. 2. I am not sure how I specify that I want random effects in the model and nor the nature of these (eg random intercept or random intercept and slope, etc). Any help would be greatly appreciated. Peter -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

I am currently doing a research paper that requires the estimation of a thereshold model. I have gone through the reference manual for the pacjage "tsdyn" and i am failing to see how the the threshold models provided by R can estimate a model in which the other control variables exist or are included in the estiamtion of the model. Any help provided will be greatly appreciated Andrew

...of looping and would ideally like to write it so that it avoid the use of "for" loop ... however I'm having some trouble. Very briefly, the basic idea is to implement a binary partitioning algorithm to determine the optimal cutpoint based on deviance measures obtained from likelihood estiamtes. This is in the geo-spatial context so I'm actually using the geoRglm package to obtain this likelihood fit. I initially use the "variog" function to help specify the initial parameter values passed to "likfit" to ensure convergence. Although not the most elegant solution...

...Statistics : original bias std. error t1* 1.391884 0.2708119 16.32276 > bob -----Original Message----- From: umeno [mailto:umeno@students.uiuc.edu] Sent: Thursday, October 30, 2003 1:19 PM To: R-Help <r-help Subject: [R] Variance of a non-linear combination of the coefficient estiamtes Hi, I would like to know if anyone knows how to compute a variance of the non-linear combination of the coefficient estimates. Say, I get a model of y~c+ax+bz (1) where x and z are the independent variables, c is the constant estimate, and a and b are the coefficient estimates. Then, I want...

Hello, Is it possible to estimate prediction interval using GAM? I looked through ?gam, ?predict.gam etc and the mgcv.pdf Simon Wood. I found it can calculate confidence interval but not clear if I can get it to calculate prediction interval. I read "Inference for GAMs is difficult and somewhat contentious." in Kuhnert and Venable An Introduction to R, and wondering why and if that

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of

Hello all, Would somebody be kind enough to show me how to do a KS test in R for a lognormal distribution with ESTIMATED parameters. The R function ks.test()says "the parameters specified must be prespecified and not estimated from the data" Is there a way to correct this when one uses estimated data? Regards, Kwabena. -------------------------------------------- Kwabena Adusei-Poku

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

I'm attempting to calculate a regression in R that I normally use Prism for, because the formula isn't pretty by any means. Prism presents the formula (which is in the Prism equation library as Heterologous competition with depletion, if anyone is curious) in these segments: KdCPM = KdnM*SpAct*Vol*1000 R=NS+1 S=(1+10^(X-LogKi))*KdCPM+Hot a=-1*R b=R*S+NS*Hot+BMax c = -1*Hot*(S*MS+BMax) Y