similar to: correlation in lme4

Dear list, I do not know if this is a bug. Let's suppose mat is a matrix derived from this code: > x<-rnorm(10) > y<-rnorm(10) > names(x)<-LETTERS[1:10] > names(y)<-LETTERS[1:10] > mat<-cbind(x,y) Now in R 2.0.1 I have: > names(dist(mat)) "A" "B" "C" "D" "E" "F" "G" "H"

Dear all, there are R packages able to simulate or estimate bilinear model for time series? I know it is an open problem, but do exist something for very simplified bilinear models? Alternatively, what kinfd of non linear time series models are performed in R? If R is not able, could someone suggest me for some commercial softwares to deal with bilinear models? i'm afraid of a negative

hello. My name is kerlim arturo. i was search into internet about R apache y PhP and i found your mail into foro. i want create a stadistic's software on line with language R,apache y php . i want know, what is the steps for developer this stadistic's software? where can i get information about language R, apache y php; and examples between language R and php ('source'). can I

Hi everybody, I have the following problem. I have a vector containing character elements, such as: list = c("aa","bb","cc","dd","ee") I want to create an index which identifies the elements that are different from, e.g. "aa" and "bb". When I do the following: jj = list!="aa" & list!="bb" > jj

I came across this one: http://www.nysun.com/article/32787 which says that the violent death rate in Iraq (which presumably includes violent deaths from the war) is lower than the violent death rate in major American cities. Does anyone have any insights from statistics on how to interpret this?

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 --

Hello I have this problem. I have a large matrix of this sort: > prova [,1] [,2] [,3] [,4] [1,] 3 3 3 3 [2,] 3 3 3 1 [3,] 1 3 3 3 [4,] 1 1 1 3 [5,] 3 1 1 3 [6,] 3 1 1 3 [7,] 1 3 1 3 [8,] 1 3 3 3 What I want to do is to count the number of sequences of ones and stack the results in a

dear all, When I use all.vars(), I am interest in extracting only the variable names.. Here a simple example all.vars(as.formula(y~poly(x,k)+z)) returns [1] "y" "x" "k" "z" and I would like to obtain "y" "x" "z" Where is the trick? many thanks vito -- ==================================== Vito M.R. Muggeo Dip.to Sc

Hello, I have the following problem. I am running simulations on possible states of a set of agents (1=employed, 0=unemployed). I store these simulated time series in a matrix like the following, where rows indicates time periods, columns the number of agents (4 agents and 8 periods in this case): Atr=[ 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 1 0

--- On Mon, 3/12/12, aleksandr shfets <a_shfets at mail.ru> wrote: > From: aleksandr shfets <a_shfets at mail.ru> > Subject: Fwd: Re[2]: [R] B-spline/smooth.basis derivative matrices > To: "Vassily Shvets" <shv736 at yahoo.com> > Received: Monday, March 12, 2012, 5:15 PM > > > > -------- ???????????? ????????? > -------- > ?? ????:

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)

dear all, I would like to get the lme call without fitting the relevant model. library(nlme) data(Orthodont) fm1 <- lme(distance ~ age, random=list(Subject=~age),data = Orthodont) To get fm1$call without fitting the model I use call(): my.cc<-call("lme.formula", fixed= distance ~ age, random = list(Subject = ~age)) However the two calls are not the same (apart from the data

dear all, Is the following intentional? Am I missing anything in documentation? d<-data.frame(y=rnorm(10,5,.5),exp=rnorm(10), age=rnorm(10)) formula(lm(exp(y)~exp+age, data=d)) #--> exp(y) ~ exp + age formula(lm(exp(y)~., data=d)) #--> exp(y) ~ age variable 'exp' (maybe indicating "experience") is not included in the model. The same happens with 'log' (and

I am trying to download R.1.3 for Windows , and I have troubles with some of the .zip files. I am working with a laptop operating on Windows 98 and use Netscape 4.5 as a browser. I have downloaded without problems the following files: rw1030b1.zip rw1030ch.zip rw1030d1.zip rw1030d2.zip rw1030sp.zip rw1030w.zip but when I try to download rw1030b2.zip rw1030h.zip rw1030l.zip the

dear all, I do not if it is a nonsense question.. Is it possible in the R session to get the name of the current .Rdata file that I ran? I mean: suppose I double click the file myfile.Rdata. ls() returns the names of the objects in the current workspace (that is saved in myfile.Rdata). In the current R session, I would like to obtain "myfile.Rdata". Is it possible? Thanks in

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

Hello, I'm currently using the segmented package of M.R. Muggeo to fit a two-slope segmented regression. I would like to constrain a null-left-slope, but I cannot make it. I followed the explanations of the package (http://dssm.unipa.it/vmuggeo/segmentedRnews.pdf) to write the following code : fit.glm <- glm(y~x) fit.seg <- segmented(fit.glm, seg.Z=~x,psi=0.3) fit.glm

Functions are first class objects, so some kind of collision is bound to happen if you do this... so don't. -- Sent from my phone. Please excuse my brevity. On January 30, 2018 3:11:56 AM PST, "Vito M. R. Muggeo" <vito.muggeo at unipa.it> wrote: >dear all, >Is the following intentional? Am I missing anything in documentation? >

dear all, It appears that glmnet(), when "selecting" the covariates entering the model, skips from K covariates, say, to K+2 or K+3. Thus 2 or 3 variables are "added" at the same time and it is not possible to obtain a ranking of the covariates according to their importance in the model. On the other hand lars() "adds" the covariates one at a time. My question

Hi, I downloaded both gee and geepack, and I am trying to understand the differences between the two libraries. I used the same data and estimated the same model, with a correlation structure autoregressive of order 1. Surprisingly for me, I found very different results. Coefficients are slightly different in value but sometimes opposite in sign. Moreover, the estimate of rho (correlation