similar to: How to generating diagnal blocks ?

dear all, This is a probably a silly question. If I type > grep("x",c("a.x" ,"b.x","a.xx"),value=TRUE) [1] "a.x" "b.x" "a.xx" Instead, I would like to obtain only "a.x" "b.x" How is it possible to get this result with grep()? many thanks for your attention, best, vito --

Dear all, It appears that MASS::polr() and Design::lrm() return the same point estimates but different st.errs when fitting proportional odds models, grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1) score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595, 557,557,584,599,517,649,584,463,591,488,563,553,549) library(MASS) library(Design)

With version 8 of acrobat reader, it is now possible to have 3D in PDf documents. Does it exist already an R package who manage to produce 3D plots which can be saved as interactive 3D graphs in a PDF file? Best Regards Bruno Cavestro ------------------------------------------------------ Leggi GRATIS le tue mail con il telefonino i-mode? di Wind http://i-mode.wind.it/

Hi all, Could anybody recommend some easy-to-understand and example based notes/tutorials on how to use cubic splines to do filtering on multi-dimension data? I am confused by the 1-dimensional case, and more confused by multi-dimensional case. I found all the books suddenly become very abstract when it comes to this subject. They don't provide examples in R or Splus at all. Specifically,

I have estimated a multiple nonparametric regression using the loess command in R. I have also estimated an additive version of the model using the gam function. Is there a way of using the output of these two models to test the restrictions imposed by the additive model?

Dear all, Is there *any* reason explaining what I describe below? I have the following line myfun(x) If I type them directly in R (or copy/past), it works.. However if I type in R 2.6.1 > source("code.R") ##code.R includes the above line Error in inherits(x, "data.frame") : object "d" not found namely myfun() does not work correctly. In particular the

dear all, Could anyone explain me the behaviour of median() within by()? (I am running R.2.7.0) thanks, vito > H<-cbind(rep(0:1,l=20),matrix(rnorm(20*2),20,2)) > by(H[,-1],H[,1],mean) INDICES: 0 V1 V2 -0.2101069 0.2954377 --------------------------------------------------------------------------------------------------------------------- INDICES: 1 V1

Dear all, I am dealing with the following (apparently simple problem): For some reasons I am interested in passing variables from a dataframe to a specific environment, and in fitting a standard glm: dati<-data.frame(y=rnorm(10),x1=runif(10),x2=runif(10)) KK<-new.env() for(i in 1:ncol(dati)) assign(names(dati[i]),dati[[i]],envir=KK) #Now the following two lines work correctly:

I'm using the monoreg function (with weights) from the fdrtool package. How can I calculate the R square for this type of regression? Thanks for your help, Thierry -- View this message in context: http://www.nabble.com/R-square-for-Monotone-regression-tp15580803p15580803.html Sent from the R help mailing list archive at Nabble.com.

Hello R users I ran more than 100 logistic regression analyses. Some of the analyses gave me this kind warning below. ########################################################### Warning messages: 1: algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ... 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y,

Hi all, I'm having sometrouble with managing the seach path, in a function , I need to attach some data set at the begining and detach them at the end, say, myfunction<- function() { attach(mylist); ............. detach(mylist) } , the problem is, since I am still debugging this code, sometimes it got error and ended before reaching the end, thus the data is left in the

Is it possible to use the '\ell' (i.e. the log likelihood) in plots? I've been browsing the plotmath documentation unsucesfully. Cheers, Mario dos Reis mdosrei at nimr.mrc.ac.uk +44 (0)20 8816 2300 Division of Mathematical Biology National Institute for Medical Research The Ridgeway Mill Hill London, NW7 1AA, UK

One of the assumptions, on which the (General) Linear Modelling is based is that the response variable is measured with some uncertainties (or weighted), but the explanatory variables are fixed. Is it possible to extend the model by assigning the weights to the explanatory variables as well? Is there a package for doing such a model fit? Thanks

Hello, I'm trying to find an algorithm to estimate a switching regression model based on the 1990 Economics Letters paper by Ohtani/Kakimoto/Abe or the earlier version from 1985 (Ohtani/Katayama, Economic Studies Quarterly; assuming as a transition path a polynomial of order 1). I found an idea for using nls here: http://www.biostat.wustl.edu/archives/html/s-news/2000-04/msg00223.html.

Dear All, I have a matrix say, X ( 100 X 40,000) and a vector say, y (100 X 1) . I want to perform linear regression. I have scaled X matrix by using scale () to get mean zero and s.d 1 . But still I get very high values of regression coefficients. If I scale X matrix, then the regression coefficients will bahave as a correlation coefficient and they should not be more than 1.

Dear All, I'm trying to do the estimation in a changepoint regression problem via R, but never found any suitable function which might help me to do this. Could someone give me a hand?on this matter? Thank you.

Dear R Users, I have a CO2 time series. I want to fit this series seasonal cycle and trend with fourth harmonic function, and then compute residuals. I am doing something like: file<-read.csv("co2data.csv") names(file) attach(file) fit<-lm(co2~1+time+I(time^2)+sin(2*pi*time)+cos(2*pi*time)+sin(4*pi*time)+cos(4*pi*time)+

Hello all, I have 3 time series (tt) that I've fitted segmented regression models to, with 3 breakpoints that are common to all, using code below (requires segmented package). However I wish to specifiy a zero coefficient, a priori, for the last segment of the KW series (green) only. Is this possible to do with segmented? If not, could someone point in a direction? The final goal is to

Dear all, I have the following (rather) strange problem.. For some reasons, I finally work with a variable whose name includes an R function, "a.log(z)", say. And that is a problem when I call it in a formula, for instance: > myname<-"a.log(z)" > dd<-data.frame("a.log(z)"=1:10,y=rnorm(10)) > o<-lm(y~1,data=dd) >

Most of the data sets I'm dealing with exhibit a time trend. We would like to get rid of the time trend. The plot shows in some cases a monotonic increase of the dependent variable with time. This is the easiest case. In some other cases the plot shows a time trend where the dependent variable changes slope 4-5 times along the observations measurement period. I've attempted a segmented