similar to: weighted regression

I need to do a classic stepwise regression based not on AIC but on F in and F out (or on R2, or R2 adjusted). I have many variables and it will very useful for me to have a fast stepwise algorithm. Does anyone know if this exists for R and where I can find that ? Thank you very much. Pascal Grandeau -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

Dear all, I am trying to implement goodness-of-fit tests for logistic regression models described in : Hosmer, D.W., Lemeshow, S., 2000. Applied logistic regression. New-York, John Wiley & Sons, Inc., 373 p. (pp. 152-154) Namely I would like to reproduce Osius & Rojek's test (Osius, G., Rojek, D., 1992. Normal goodness-of-fit tests for multinomial models with large degrees of

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Full_Name: Pascal GRANDEAU Version: 0.65.0 OS: Windows 95 Submission from: (NULL) (164.138.103.253) I downloaded the new version of R and I see a little bug with Windows 95. If I plot anything and then I call the menu Windows/Tile, this works but when I continue to work after a certain number of input (near 10) the cursor disapear and I cannot continue. This happens also if I change the

I did a regression with ARMA errors using arima0 with ari<-arima0(y,order=c(2,0,2),xreg=reg1,delta=-1) or ari<-arima0(y,order=c(2,0,2),xreg=reg1) where reg1 is the matrix of the regressors and when I see diag(ari$var.coef) I get negative terms. Do you know what this mean ? I try to change transform.pars to 0 or 1 but this crash R on Windows. Is it possible to test the significativity

Hello, Firstly let me explain that the nature of what I want to do is actually beyond my statistical knowledge, having only taken a second year university stats course last year. Therefore I may have missed the statistical essence of what I want to do as well as my lack of ability to do it in R. I am trying to fit some hospital admissions data to a series of weather variables. Due to my lack of

Dear R community, I’m new in R (coming from SAS). If possible, I’d like to know your thoughts regarding the following. I’m working on a regression problem, where my dependent variable is a ratio of two variables (i.e. dep.var=A/B). The variable A takes a value of zero for the vast majority of observations and, of course, so does the dependent variable. I thought I should do a weighted analysis

To everyone, I am currently doing analysis through Splus but am having severe problems. I am trying to fit all subset weighted regression to a multivariate data set and get PRESS statistics, along with Rsquare, Rsquare adjusted and Mallows Cp for all possible models. Splus, will give me these statistics but the leaps procedure is highly sensitive to nearly singular data matrices (which mine is)

Dear UseRs: Recently, I have read an article regarding the association between age and lymph node metastases. http://jco.ascopubs.org/content/27/18/2931.long In statistical analysis, the authors stated "Because a nonlinear relationship between age and lymph node involvement was expected based on existing literature, lymph node involvement was also regressed on age using nonparametric

Dear list, I am trying to set up a propensity-weighted regression using the survey package. Most of my population is sampled with a sampling probability of one (that is, I have the full population). However, for a subset of the data I have only a 50% sample of the full population. In previous work on the data, I analyzed these data using SAS and STATA. In those packages I used a propensity weight

I need to find out the difference between the way R calculates weighted regression and standard regression. I want to plot a 95% confidence interval around an estimte i got from least squares regression. I cant find he documentation for this ive looked in ?stats ?lm ?predict.lm ?weights ?residuals.lm Can anyone shed light? thanks Chris. -- View this message in context:

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

Hi, May I know how to obtain the confidence interval of the survival curve of weighted Cox regression model? I tried coxph, cph, and coxphw, but they did not work. Any help would be much appreciated. Thanks, Jeanne -- View this message in context: http://r.789695.n4.nabble.com/Confidence-interval-of-Survival-Curve-of-Weighted-Cox-Regression-tp3308085p3308085.html Sent from the R help mailing

Dear R-users, I am trying to create a multipanel scatter plot which displays weighted regression lines for each subgroup. I am having diffuculty figuring out how the Trellis multipanel plotting functions work, in general. Does someone know where there are some beginners examples on how to create and use panel.xxx functions in conjunction with the xyplot functions? Back to the matter at hand

Greetings, I would like to use the "boot" function to generate a bootstrap confidence interval for the slope in a SLR that has a zero intercept. My attempt to do this is shown below. Is this the correct implementation of the boot function to solve this problem? In particular, should I be doing anything with the residuals in the "bs" function (e.g., using weighted residuals)?

Hello, is it possible in R to calculate a Weighted averaging partial least squares regression? I'm not firm in statistics and didn't found anything about weighted averaging in combination with PLS in the help archives. Or is it possible to develop a workaround with the pls-package? thanks for help in advance Andreas Plank -- _____________________________________________ Dipl. Biol.

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

Hi, I am currently trying to do dose-response curves using weighted 4-parameter model (4PL). The weighting was based on 1/(expected variance) derived from historical data. I tried both drm() from drc package, and nls(), found very different results derived from drm() vs. nls() using "weights=" argument. d1<-read.table("d1.txt",sep='\t',header=T,row.names=1)

Dear List, Just wondering, is there a Bayesian version of weighted regression available in the literature (to handle survey weights, say)? If yes, could you suggest me a reference? Does MCMCregress handle weights? cheers, Ehsan

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >