similar to: partial correlation function

Hi, among the many (5) methods that I found in the list to do partial correlation in the following two that I had a look I am getting different t-values. Does anyone have any clues on why is that? The source code is below. Thanks. pcor3 <- function (x, test = T, p = 0.05) { nvar <- ncol(x) ndata <- nrow(x) conc <- solve(cor(x)) resid.sd <- 1/sqrt(diag(conc)) pcc <-

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)))

Hello, on a simple linear model the values produced from the fitted(model) function are difference from manually calculating on calc. Will anyone have a clue... or any insights on how fitted function calculates the values? Thank you. -- -- Yianni [[alternative HTML version deleted]]

The normality of the residuals is important in the inference procedures for the classical linear regression model, and normality is very important in correlation analysis (second moment)... Washington S. Silva > Thank you all for your replies.... they have been more useful... well > in my case I have chosen to do some parametric tests (more precisely > correlation and linear regressions

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 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

1) At the end of main, there's attempt to free cur_part->record, which rarely comes from malloc. Only valid case is if gpt handover was performed and chaining was not successful (cur_part->record is overwritten with gpt specifc handover record). Freeing the handover area has been adjusted. 2) If our current iterator is ebr, parent wouldn't be freed at the end of main. Added generic

Signed-off-by: Christoph Hellwig <hch@lst.de> Index: linux-2.6/fs/ocfs2/cluster/tcp.c =================================================================== --- linux-2.6.orig/fs/ocfs2/cluster/tcp.c 2007-05-06 13:51:17.000000000 +0200 +++ linux-2.6/fs/ocfs2/cluster/tcp.c 2007-05-17 15:00:14.000000000 +0200 @@ -261,14 +261,12 @@ out: static void o2net_complete_nodes_nsw(struct o2net_node

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")

Dear Thomas & Dear List, the fitting function `coxph.fit' called by `coxph' may fail to estimate the regression coefficients when some values of the design matrix are very large. For example library(survival) ### load example data load(url("http://www.imbe.med.uni-erlangen.de/~hothorn/coxph_fit.Rda")) method <- "efron" ### copied from `coxph.fit' coxfit

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

Hello, SAS' point and click interface has the option of produce a scatterplot with a superimposed confidence ellipse for the correlation coefficient. Since I generally like R so much better, I would like to reproduce this in R. I've been playing with the ellipse package. In order to have the points and the ellipse on the same graph I've done the following. (Load ellipse

A number of users have spotted a terminal problem with the 'debug' package under R2.4.0, along the lines of > mtrace(x) > x() Error in attr(value, "row.names") <- rlabs : row names must be 'character' or 'integer', not 'double' This arose from a bug in 'rbind.data.frame' in R2.4.0 itself. The bug is fixed in R2.4.0 patched, so the

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",

When attempting to merge 3 data frames, one of which has fewer columns than the others, rbind.data.frame correctly refuses to perform the bind. However, the error message given is a bit obscure due to a logical bug in the match.names() internal function to rbind.data.frame. Illustration: ## Three data frames with same column variable names: > foo <- data.frame(v1 = c('a',

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

Greetings, I am running a factor analysis model in rjags and have received the following error Error parsing model file: syntax error on line 5 near "" Line 5 is model { Here is my syntax. Any insight would be greatly appreciated. require(rjags) modelstring = " model { for (i in 1 : nData) { for (j in 1 : nIndicators) { y[i,j] ~ dnorm(mu[i,j],psi[j]) ephat[i,j] <-

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)

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