similar to: partial correlation significance

Hi, after reading the archives I found some methods... adopted and modified one of them to the following. I think it is correct after checking and comparing the results with other software... but if possible someone could have a look and spot any mistakes I would be grateful. Thanks pcor3 <- function (x, test = T, p = 0.05, alternative="two.sided") { nvar <- ncol(x) ndata

Hello all, here's a collection of answers I got on my question concerning partial correlation coefficients: Some people gave a simple formula for the three-variable-case, as did Dave Lucy: pcor <- function(v1, v2, v3) { c12 <- cor(v1, v2) c23 <- cor(v2, v3) c13 <- cor(v1, v3) partial <- (c12-(c13*c23))/(sqrt(1-(c13^2)) * sqrt(1-(c23^2)))

I'm a great fan of read.table(), but this time the data had a lot of cruft. So I used readLines() and editted the char vector to eventually get something like this: " 23.4 1.5 4.2" " 19.1 2.2 4.1" and so on. To get that into a 3 col numeric table, I first just used: writeLines(data,"tempfile")

I am ploting gridded time series data. I would like the actual lat and lon value appear on the graph-if possible inside the graph as numbers. If there is also more elegant ways to plot the graphs I will appreciate more suggestions. ################################# library(ggplot2) library(lattice) month <- c("Jan", "Feb", "Mar", "Apr", "May",

Hi Mackay and anybody (a) Is it possible to select randomly (let say five grids) and plot? (b) Is it possible to plot five nearest grid in one figure? The original question and improved codes: #I am ploting gridded time series data. I would like the actual lat #and lon value appear on the graph-if possible inside the graph as #numbers. If there is also more elegant ways to plot the graphs I

Dear All, I am trying to solve the optimization problem below, but I am always getting the following error: Error in optim(rep(20, nvar), f, gr, method = "L-BFGS-B", lower = rep(0, : L-BFGS-B needs finite values of 'fn' Any ideas? Thanks in advance, Paul ----------------------------------------------- k <- 10000 b <- 0.3 f <- function(x) { n <- length(x)

R CMD check tells me * checking R code for possible problems ... NOTE agexact.fit.rds: ... may be used in an incorrect context: ?optim(init, agfitfn, ...)? Warning: <anonymous>: ... may be used in an incorrect context: ?optim(init, agfitfn, ...)? Can anyone tell me what this message means? My searches haven't turned up anything useful. This is with R 2.7 and 2.9. The message

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

Hi! I just started to use R recently, and would like to ask a help about automating the job. I need to use "kmeans" function with my own 300 data files, and wonder if it's possible to do it automatically. For example, > library (mva) > mydata <- read.table ("data1") > cl <- kmeans(mydata, 5, 20) and I just need to save "cl" info (i.e. the center

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

Hi, I'm currently using the R package e1071 to train naive bayes classifiers and came across a bug: When the posterior probabilities of all classes are small, the result from the predict.naiveBayes function become NaNs. This is an issue with the treatment of the log-transformed probabilities inside the predict.naiveBayes function. Here is an example to demonstrate the problem (you might need

I am making my first attempts at using some Fortran code with R, and so far it's going OK. To print from my Fortran programs, it seems I need subroutines dblepr, realpr and intpr. From the excellent "Writing R Extensions" document: "Three subroutines are provided to ease the output of information from FORTRAN code. subroutine dblepr(label, nchar, data, ndata)

Dear R community members and R experts I am stuck at a point and I tried with my colleagues and did not get it out. Sorry, I need your help. Here my data (just created to show the example): # generating a dataset just to show how my dataset look like, here I have x variables # x1 .........to X1000 plus ind and y ind <- c(1:100) y <- rnorm(100, 10,2) set.seed(201) P <-

Hallo All, I would like to apply a function to all permutations of variables in a dataframe (except the first). What is the best way to achieve this? I produce the permutations using: nvar <- ncol(dat) - 1 perms <- as.matrix( expand.grid(rep( list(1:0) , nvar ))[ , nvar:1] ) Thanks in advance Serguei Test-dataframe, comma-delimited: code,wav,w,area,gdp,def,pop,coast,milspend,agr

I am now getting the occasional complaint about survival routines that are not able to handle big data.?? I looked in the manuals to try and update my understanding of max vector size, max matrix, max data set, etc; but it is either not there or I missed it (the latter more likely).?? Is it still .Machine$integer.max for everything??? Will that change??? Found where? I am going to need to go

hi all i know that one should try and limit the amount of looping in R programs. i have supplied some code below. i am interested in seeing how the code cold be rewritten if we dont use the loops. a brief overview of what is done in the code. ============================================== ============================================== ============================================== 1. the input

Dear R-users, I have 1 remark and 1 question on the inclusion of interactions in the gam function from the gam package. I need to fit quantitative predictors in interactions with factors. You can see an example of what I need in fig 9.13 p265 from Hastie and Tibshirani book (1990). It's clearly stated that in ?gam "Interactions with nonparametric smooth terms are not fully

Assume a C program invoked by .Call, that returns a list. Near the top of the program we allocate space for all the list elements. (It is my habit to use "xyz2" for the name of the R object and "xyz" for the pointer to its contents.) PROTECT(means2 = allocVector(REALSXP, nvar)); means = REAL(means2); PROTECT(u2 = allocVector(REALSXP, nvar)); u =

The number of recent questions from umn.edu makes me wonder if there's homework involved.... Simpler for your example is to use get and subset. dat <- structure(..... as found below var.to.test <- names(dat)[4:6] #variables of interest nvar <- length(var.to.test) chisq <- double(nvar) for (i in 1:nvar) { tfit <- survdiff(Surv(time, completion==2) ~

Hello Dear R community,  This is my problem.  I have a data set (dataframe) called "mydat". It consist of 3 numerical variable.  They are Centrecode, FSUSN and Round. I want to create unique ID by combining these 3 variables. Follwing commands gives me what I need. mydat1 <- paste(mydat$Centrecode, mydat$FSUSN,mydat$Round,sep="") newds <- data.frame(mydat1)    For a