similar to: lme: extract variance estimate

I'm seeking some advice on effectively using the new Matrix library in R1.9.0 for operations with large dense matrices. I'm working on integral operator models (implemented numerically via matrix operations) and except for the way entries are generated, the examples below really are representative of my problem sizes. My main concern is speed of large dense matrix multiplication. In R

Hello, i have a little problem with R and i hope you can help me. I want to use splines to estimate a function but i want to force the interpolation to be monotone. Is this possible with R ? Thank you, Rémi. --------------------------------- [[alternative HTML version deleted]]

Hello, Does anybody know if there is somewhere in R a function to calculate the Lyapunov exponent in a time series? Thanks, Philippe Grosjean .......................................................<??}))><.... ) ) ) ) ) ( ( ( ( ( Prof. Philippe Grosjean \ ___ ) \/ECO\ ( Numerical Ecology of Aquatic Systems /\___/ ) Mons-Hainaut University, Pentagone / ___ /( 8, Av. du

Dear all, Has anyone worked on coding for calculating Lyapunov Exponent for a time series data? or any package is available for computing Lyapunov? Please advice and many thanks in advance. Catherine X Wang

R-users -- I would be interested in tools in R to fit the following gam model: logit(p) = a + f(x1) + f(x2) + f(x1,x2), where f(x1,x2) defines a surface. I have looked into the mgcv library, but it seems only to fit models of the form: logit(p) = a + f(x1) + f(x2) Any ideas? Cheers, Dan =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Dan Powers Associate Professor,

R help list subscribers, I am a new user of R. I am attempting to use R to explore a set of equations specifying the dynamics of a three trophic level food chain. I have put together this code for the function that is to be evaluted by LSODA. My equations Rprime, Cprime, and Pprime are meant to describe the actual equation of the derivative. When I run LSODA, I do not get the output that

I am interested in using the nonlinear forecasting techniques developed by Sugihara et al. In particular the simplex projection and the S-Map (see this website for details and reprints: http://iod.ucsd.edu/simplex/ ). I've looked through CRAN but could not find any package with functions that allow such analysis. Any pointers? Thanks, Manu

Hi, You may try: dat1 <- structure(list(SubID = 1:8, CSE1 = c(6L, 6L, 5L, 5L, 5L, 5L, 3L, 3L), CSE2 = c(5L, 4L, 5L, 4L, 6L, 4L, 6L, 6L), CSE3 = c(6L, 7L, 5L, 3L, 7L, 3L, 6L, 6L), CSE4 = c(2L, 2L, 5L, 4L, 5L, 6L, 3L, 3L), WSE1 = c(6L, 6L, 5L, 4L, 6L, 4L, 6L, 6L), WSE2 = c(2L, 6L, 5L, 4L, 4L, 3L, 5L, 5L), WSE3 = c(2L, 2L, 4L, 5L, 4L, 7L, 2L, 4L), WSE4 = c(4L, 3L, 5L, 2L, 1L, 3L, 1L, 7L)),

hello, I have programmed this function to calculate the Neuman-Keuls test but I have a problem the function return an empty list and I don't know why. summary(fm1) E <- sqrt((summary(fm1)[[1]]["Residuals","Mean Sq"])/length(LR)) lst <- list() lst1 <- list() lst2 <- list() NK <- function (x) { if (length(x) == 2) { Tstudent <- t.test(subset(exple,

Hi, I guess you meant this: dat2<- read.table(text=" patient_id????? t???????? scores 1????????????????????? 0??????????????? 1.6 1????????????????????? 1??????????????? 2.6 1????????????????????? 2???????????????? 2.2 1????????????????????? 3???????????????? 1.8 2????????????????????? 0????????????????? 2.3 2?????????????????????? 2???????????????? 2.5 2?????????????????????

Hi, Try this: final3New<-read.table(file="real_data_cecilia.txt",sep="\t") dim(final3New) #[1] 5369??? 5 #Inside the split within split, dummy==1 for the first row.? For lists that have many rows, I selected the row with dummy==0 (from the rest) using the #condition that the absolute difference between the dimensions of those rows and the first row dimension was minimum

Hi, Using the example data from library(gvlma) library(gvlma) data(CarMileageData) CarMileageNew <- CarMileageData[,c(5,6,3)] ?lst1 <- list() ?y <- c("NumGallons", "NumDaysBetw") ?for(i in seq_along(y)){ ?lst1[[i]] <- gvlma(lm(get(y[i])~MilesLastFill,data=CarMileageNew)) ?lst1} pdf("gvlmaplot.pdf") ?lapply(lst1,plot) dev.off() You could also use

Hi, May be this helps: #Creating some dummy data.? set.seed(24) lst1<-lapply(1:8,function(x) ts(sample(1:25,20,replace=TRUE))) set.seed(49) lst2<-lapply(1:8,function(x) ts(sample(1:45,20,replace=TRUE))) ??Find_Max_CCF() #No vignettes or demos or help files found with alias or concept or #title matching ?Find_Max_CCF? using regular expression matching. Found a function with the same

Hi, May be this helps: dat1<- read.csv("dat7.csv",header=TRUE,stringsAsFactors=FALSE,sep="\t") dat.bru<- dat1[!is.na(dat1$evnmt_brutal),] fun2<- function(dat){?? ????? lst1<- split(dat,dat$patient_id) ??? lst2<- lapply(lst1,function(x) x[cumsum(x$evnmt_brutal==0)>0,]) ??? lst3<- lapply(lst2,function(x) x[!(all(x$evnmt_brutal==1)|all(x$evnmt_brutal==0)),])

As a newer R practicioner, it seems I stump myself weekly (at least) with issues that have spinning my wheels.  Here is yet another... I'm trying to turn a list of numeric vectors (of uneual length) inot a dataframe.  Each vector held in the list represents a row, and there are some rows of unequal length.  I would like NAs as placeholders for "missing" data in the shorter vectors. 

HI, Using c11<- 0.01 c12<- 0.01 c1<- 0.10 c2<- 0.10 One possible problem is that: dim(res5) #[1] 513? 20 res6<-aggregate(.~m1+n1+m+n,data=res5[,c(1:6,9:12,21:24)] ,max) #Error in `[.data.frame`(res5, , c(1:6, 9:12, 21:24)) : ?# undefined columns selected A.K. ________________________________ From: Joanna Zhang <zjoanna2013 at gmail.com> To: arun <smartpink111 at

Hi, Use ?split() #dat1 is the dataset: lst1<- split(dat1,dat1$BaseProd) lst1 #$`2231` ?# BaseProd? CF OSA #1???? 2231 0.5 0.7 #2???? 2231 0.8 0.6 #3???? 2231 0.4 0.8 # #$`2232` ?# BaseProd CF OSA #4???? 2232? 1?? 2 #5???? 2232? 3?? 1 # #$`2233` ?# BaseProd? CF OSA #6???? 2233 0.9 0.5 #7???? 2233 0.7 0.5 #8???? 2233 4.0 5.0 #9???? 2233 5.0 7.0 lst1[[1]] #? BaseProd? CF OSA #1???? 2231 0.5 0.7

Hi, Try this: dat1<- read.csv("dat7.csv",header=TRUE,stringsAsFactors=FALSE,sep="\t") dat.bru<- dat1[!is.na(dat1$evnmt_brutal),] fun1<- function(dat){??? ? ??? lst1<- split(dat,dat$patient_id) ??? lst2<- lapply(lst1,function(x) x[cumsum(x$evnmt_brutal==0)>0,]) ??? lst3<- lapply(lst2,function(x) x[!(all(x$evnmt_brutal==1)|all(x$evnmt_brutal==0)),]) ???

Hi, May be you can try this: dat1New<-? dat1[!(duplicated(dat1$gene)|duplicated(dat1$gene,fromLast=TRUE)),] dat2<-dat1[duplicated(dat1$gene)|duplicated(dat1$gene,fromLast=TRUE),] ?lst1<-split(dat2,dat2$gene) dat3<-unsplit(lapply(lst1,function(x) {x1<- sum(apply(x[,6:32],2,function(y) y[1]>=y[2]));x2<- sum(apply(x[,6:32],2, function(y) y[1]<=y[2])); if(x1>x2) x[1,] else

HI, You can do this: dat1<- read.csv("dat7.csv",header=TRUE,stringsAsFactors=FALSE,sep="\t") dat.bru<- dat1[!is.na(dat1$evnmt_brutal),] fun2<- function(dat){? ????? lst1<- split(dat,dat$patient_id) ??? lst2<- lapply(lst1,function(x) x[cumsum(x$evnmt_brutal==0)>0,]) ??? lst3<- lapply(lst2,function(x) x[!(all(x$evnmt_brutal==1)|all(x$evnmt_brutal==0)),])