similar to: AIC for robust regression

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am

Hi all, I am attempting to create a new variable based on values of other variables. The variable is called pharm. It basically takes the numeric code of 1 as yes and 0 to be No from the variable B20_C1 (a question on a survey). However, I would also like to have a level for non-respondents and these are captured in the variable nr.B20C, which is a 1 when there is a non-response on the whole group

In theory ParkAndAnnounce has a lot of usefulness, however, that we've had very little success with application... Our application is similiar to the local Walgreens pharmacy.. Dr. Calls in, selects the "Im a doctor with a prescription option"... call is parked, and announcement overhead is given.. "Doctor holding on ${EXTEN}" exten =>

May I inquire about how to obtain the coefficients from a loess fit of multiple covariates? I have tried terms and coef, but it does not provide me with the coefficients to identify the terms of the entire regression equation. Many thanks Paul -- Paul R. Hutson, Pharm.D. Associate Professor UW School of Pharmacy 777 Highland Avenue Madison WI 53705-2222

Hello R users, I am a fairly new R user, however, one of the problems I am having is the use of applying if-, and-, then-, else- statements in R against datasets & dataframe. Rtips mentions it, but without simple examples I am not quite sure what my syntax should look like. I am particularly interested in syntax that can be used with the transform function. for example if foo[,1] ==

Hi, I'm trying to make a legend with a justified list of numbers, so I thought I would use phantom(0) to align the 3-digit numbers properly with the 4-digit ones: legend(x, y, xjust=1, yjust=1, lty=c(1,2,3,4,5), adj=c(0,0.5), legend=expression(phantom(0)*300*plain(K), phantom(0)*550*plain(K), phantom(0)*830*plain(K),

Hi, I patched my R-1.1.1 sources to add support for a couple more fonts to the postscript device. It works for alphanumeric characters, but I notice that I should be supplying an AFM file with ISOLatin1Encoding (I'm using a standard AFM for now, and get some parsing errors from R when I open the postscript device). Is there a program/script which will re-encode my AFM files from

R version: 0.62.1 (June 15, 1998) I just very naively attempted to grab the 'bicreg' package for Bayesian model selection from the StatLib library, and get it running under R. I've hit a brick wall very quickly, and as an R novice I'm not sure where to go next. Here's what happened: > bicreg(as.matrix(hiv[,c(-17,-18)]),as.matrix(hiv[,18])) Error: invalid formula >

Hello again Max. A correction to my response from yesterday. Things were better than they seemed. I thought it over, checked Arellano's panel book and Driscoll and Kraay (Rev. Econ. Stud. 1998) and finally realized that vcovSCC does what you want: in fact, despite being born primarily for dealing with cross-sectional correlation, 'SCC' standard errors are robust to "both

Hi all: After calculating in R I want to show the answer and some explanations not graphic plots in another new device and then save it as txt.file. However, I couldn't find any package or command to do it yet. I know some commands about generating graphics on different types of display or printing device. ex : windows(), postscript(), pdf() and etc. But these all works for graphic plots,

Hi All R Experts, I met with below mentioned statistics in paper "Stock Index Volatility Forecasting with High Frequency Data" by Eugenie Hol, Siem Jan Koopman http://ideas.repec.org/p/dgr/uvatin/20020068.html I would like to ask that what is "Box-Ljung portmantacau statistic based on N squared autocorrelation" ? Is it same as "Box-Ljung Statistics" of stats

Hi I found on google that there is an implementation of hot-deck imputation in SAS: http://ideas.repec.org/c/boc/bocode/s366901.html Is there anything similar in R? Many Thanks Eleni Rapsomaniki

Hello, Please always cc the list. As for the question, yes, it does. If you want to remove just the ones with exactly 73.1 use the pattern grep("^73\\.1$", etc) Explanation: Beginning of string: ^ End of string: $ Escape special characters: \\ (needed because the period is a special character.) Hope this helps, Rui Barradas On 5/22/2018 12:50 PM, Ahmed Serag wrote: > Thank

Dear Ivo, dear list, (see: Message: 70 Date: Thu, 26 Mar 2009 21:39:19 +0000 From: ivowel at gmail.com Subject: [R] pgmm (Blundell-Bond) sample needed) I think I finally figured out how to replicate your supersimple GMM example with pgmm() so as to get the very same results as Stata. Having no other regressors in the formula initially drove me crazy. This was a case where simpler models are

Hello, I am comparing models made with nlme functions and non-nlme functions, based on Akaike's AIC. The AIC values I get for exactly the same model formulation --for example a linear model with no random effects fit with gls and lm, respectively-- do not fit, although the values of the four model parameters are exactly the same. For example: m1 <- gls(height ~ age, data = Loblolly) m2

Hello list members, Today I noticed on http://wiki.rubyonrails.com/rails/pages/WorkingProfessionallyWithRails that there were a ton of spam links in the source code that I couldn''t see on the page itself. Upon careful examination I found the links to be hidden: I am posting the content here for reference and to show patterns of spam: <u

Hello, I'm learning about robust M-estimators right now and had settled on the "Huber Proposal 2" as implemented in MASS, but further reading made clear, that at least 2 further weighting functions (Hampel, Tukey bisquare) exist. In a post from B.D. Ripley going back to 1999 I found the following quote: >> 2) Would huber() give me results that are similar (i.e., close

Hi all, This was posted originally on r-sig-mixed-models, but I thought I would post here as well as it might be of more general interest. With a colleague, I have been trying to implement the Conditional AIC described by Vaida and Blanchard 2005 Biometrika, "Conditional Akaike information for mixed-effects models". This quantity is derived in a way analogous to the AIC, but is

R Users, I was just wondering if anyone has written a program (or if there is a package) out there that calculates the different derivations of AIC (e.g. AIC, AICc, QAIC, etc.) along with AIC differences (delta's), model likelihoods, Akaike weights and evidence ratio's (from Burnham and Anderson 2002). Just in a "for instance" if someone had the -2LL, sample sizes,

Dear R users (r-help@r-project.org), The definition of AIC (Akaike information criterion) for normal error models has just been changed. Please refer to the paper below on this matter. Eq.(22) is the new definition. The essential part is RSS(n+q+1)/(n-q-3); it is close to GCV. The paper is temporarily available at the "Papers In Press" place. Kunio Takezawa(2012): A Revision of