similar to: Obtaining fitted model information

Dear List, After obtaining a second-hand PC and because XP costs too much, I installed Xandros 3.0 (based on Debian) but pretty easy to use if migrating from WinXP. Does anyone know how to install R on this OS? Thank you for your time, Tom Volscho ************************************ Thomas W. Volscho Graduate Student Dept. of Sociology U-2068 University of Connecticut Storrs, CT 06269

Dear List, I have some projects where I use enormous datasets. For instance, the 5% PUMS microdata from the Census Bureau. After deleting cases I may have a dataset with 7 million+ rows and 50+ columns. Will R handle a datafile of this size? If so, how? Thank you in advance, Tom Volscho ************************************ Thomas W. Volscho Graduate Student Dept. of Sociology U-2068

Dear List, I have acquired a new desktop and wanted to put a free OS on it. I am trying Fedora Core 1, but not sure what the best Linux OS is for using R 2.0.1? Thank you in advance for your input, Tom Volscho ************************************ Thomas W. Volscho Graduate Student Dept. of Sociology U-2068 University of Connecticut Storrs, CT 06269 Phone: (860) 486-3882

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

In trying to streamline various optimization functions, I would like to have a scratch pad of working data that is shared across a number of functions. These can be called from different levels within some wrapper functions for maximum likelihood and other such computations. I'm sure there are other applications that could benefit from this. Below are two approaches. One uses the <<-

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

Dear R Users on a self-written function for calculating maximum likelihood probability (plz check function code at the bottom of this message), one value, wden, suddenly jump to zero. detail info as following: w[11]=2.14 lnw =2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182 0.178 0.179... w[11]=2.14 lnw=2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182

Hi All, I would like to compute the simple finite-difference approximation to the gradient of a scalar function of a large number of variables (on the order of 1000). Although a one-time computation using the following function grad() is fast and simple enough, the overhead for repeated evaluation of gradient in iterative schemes is quite significant. I was wondering whether there are

Hi all, I would like to know how the BIC criterion is calculated for models estimated using gls( ) function. I read in Pinheiro & Bates (2000) p84 that BIC = -2logL + npar*log(N) (for the ML method), or BIC = -2logLR + npar*log(N-p) (for the REML method) but when I use any of these formulae I don't obtain the result given by R. Thanks in advance for any help. Eve CORDA Office national

Dear list, I'm migrating a project from Matlab to R, and I'm facing a relatively complicated problem for nls. My objective function is below: >> objFun <- function(yEx,xEx,tEx,gamma,theta,kappa){ yTh <- pdfDY(xEx,tEx,gamma,theta,kappa) sum(log(yEx/yTh)^2) } The equation is yTh=P(xEx,tEx) + noise. I collect my data in: >> data <-

Hey, R users I do not know how to describe my question. I am a new user for R and write the following?code for a dynamic labor economics?model and use OPTIM to get optimizations and parameter values. the following code does not work due to the?equation: ?? wden[,i]<-dnorm((1-regw[,i])/w[5])/w[5] where w[5]?is one of the parameters (together with vector a, b and other elements in vector

Dear List, I have a dataset that provides sampling weights (National Survey of Family Growth 2002). I want to produce a cross-tabulation and use the provided sampling weights to obtain representative population estimates. (I believe they are simply frequency weights but codebook is uninformative). I can reproduce results (using this data) that were reported in a recent journal article, if I

Having worked with optim() and related programs for years, it surprised me that I haven't noticed this before, but optim() is inconsistent in how it deals with bounds constraints specified at infinity. Here's an example: # optim-glitch-Ex.R x0<-c(1,2,3,4) fnt <- function(x, fscale=10){ yy <- length(x):1 val <- sum((yy*x)^2)*fscale } grt <- function(x, fscale=10){ nn

Previously, I've posted queries about this, and thanks to postings and messages in response have recently had some success, to the extent that there is now a package called nlmrt on the R-forge project https://r-forge.r-project.org/R/?group_id=395 for solving nonlinear least squares problems that include small or zero residual problems via a Marquardt method using a call that mirrors the nls()

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

I am fitting logistic regression models, by defining my own link function, and would like to get AICc values. Using the glm command gives a value for AIC, but I haven't been able to get R to convert that to AICc. Is there a code that has already been written for this? Right now I am just putting the AIC values into an excel spreadsheet and calculating AICc, likelihood, and AIC

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have

Hi, I'm fitting poisson regression models to counts of birds in 1x1 km squares using several environmental variables as predictors. I do this in a stepwise way, using the stepAIC function. However the resulting models appear to be overparametrized, since too much variables were included. I would like to know if there is the possibility of fitting models by steps but using the AICc

I have a case-control study that I'm analysing using the conditional logistic regression function clogit from the survival package. I would like to calculate the AICc of the models I fit using clogit. I have a variety of scripts that can calculate AICc for models with a logLik method, but clogit does not appear to use this method. Is there a way I can calculate AICc from clogit in R? Many