search for: nonintegers

Hello all, I have a fairly simple data manipulation question. Say I have a dataframe like this: dat <- as.data.frame(runif(7, 3, 5)) dat$cat <- factor(c("1","4","13","1","4","13","13A")) dat runif(7, 3, 5) cat 1 3.880020 1 2 4.062800 4 3 4.828950 13 4 4.761850 1 5 4.716962 4 6

Hello Why this works: ncota <- 1 nslope <- 29 resul <- matrix(rep(0,ncota*nslope*4),ncota*nslope,4) But this doesn't? ncota <- 1 sini <- 0.1; sfin <- 1.5; spaso <- 0.05; nslope <- 1+((sfin-sini)/spaso) resul <- matrix(rep(0,ncota*nslope*4),ncota*nslope,4) I guess the problem is that the division gives a noninteger number. How can I get the second one work? I

I see that "case weights" can be optioned in multinom(). I wanted to make sure I understand what weights= is expecting. My weights (not really mine but I'm stuck with them) are noninteger, are not scaled to sum to the sample size, and larger weights are intended to increase influence. The description of various types of weights is a perennial confusion for me; sorry. STS

A question about the inner workings of glm() and dpois(): Suppose I call glm(y ~ x, family=poisson, weights = w) where y contains NON-INTEGER (but still nonnegative) values. (a) Does glm() still correctly maximise the weighted Poisson loglikelihood ? (i.e. the function given by the same formal expression as the weighted loglikelihood of independent Poisson variables Y_i except that the

I was looking for an R-package that would reduce matrices to row-echelon form, but Google was not my friend; any leads? If not, I wonder if the problem could be expressed in terms of constraint satisfaction...

Hi, I have count data that have been meddled with enough to make them non integers. Using glm(poisson) returns a "non integer" error but glm(quasipoisson) does not. Just wondering if anyone knows if I am violating the assumptions of a quasipoisson error structure by using these non-integer response data? Thanks! I'd welcome your thoughts and/or references... Mark

>>>>> "PD" == Peter Dalgaard <p.dalgaard@biostat.ku.dk> >>>>> on 24 Nov 2004 18:32:15 +0100 writes: PD> tlumley@u.washington.edu writes: >> > > dhyper() does not allow non-integer values for input >> parameters m and n. >> > >> > this is in contrast to the other functions in the

I have a question about stepAIC and extractAIC and why they can produce different answers. Here's a stepAIC result (slightly edited - I removed the warning about noninteger #successes): stepAIC(glm(formula = (Morbid_70_79/Present_70_79) ~ 1 + Cohort + Cohort2, family = binomial, data = ghs_70_79, subset = ghs_70_full),direction = c("backward")) Start: AIC=3151.41

...s-Jordan elimination). I modified it a bit: rref <- function(A, tol=sqrt(.Machine$double.eps),verbose=FALSE, fractions=FALSE){ ## A: coefficient matrix ## tol: tolerance for checking for 0 pivot ## verbose: if TRUE, print intermediate steps ## fractions: try to express nonintegers as rational numbers ## Written by John Fox if (fractions) { mass <- require(MASS) if (!mass) stop("fractions=TRUE needs MASS package") } if ((!is.matrix(A)) || (!is.numeric(A))) stop("argument must be a numeric matrix") n <- nrow(A) m <- ncol(A)...

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. ---1936847065-1111238301-1101309010=:193006 Content-Type: TEXT/PLAIN; charset=iso-8859-1; format=flowed Content-Transfer-Encoding: QUOTED-PRINTABLE On Wed, 24 Nov 2004 Erik.Jorgensen@agrsci.dk wrote: > > dhyper() does not allow non-integer

Hallo Everybody Consider the following vector a=1:10 b=20:30 c=40:50 x=c(a,b,c) I need a function that can tell me that there are three set of continuos sequences and that the first is from 1:10, the second from 20:30 and the third from 40:50. In other words: a,b, and c. regards Christiaan [[alternative HTML version deleted]]

R core team - It took far, far longer than I anticipated, but I have finally finished the next release of the survival code. Primary changes 1. The source has been migrated to Rforge. This will now be the primary source. I've used SCCS -> rcs -> cvs and now svn. Further changes will be someone else's problem. I expect that maintaince will now begin to migrate to a larger

In species distribution modeling where one uses a large sample of background points to capture background variation in presence\pseudo-absence or use\available models (0\1 response) it is frequently recommended that one weight the data so the sum of the absence weights is equal to the sum of presence weights so that the model isn?t swamped by an overwhelming and arbitrary number of background

I don't normally have to go anywhere near this stuff , but it seems to me that this should be a straight-forward process in R. For the purposes of this enquiry I thought I would use something I can work out on my own. So I have my matrix and the right hand results from that matrix tdata <- matrix(c(0,1,0,-1,-1,2,0,0,-5,-6,0,0,3,-5,-6,1,-1,-1,0,0),byrow = T,ncol = 5) sumtd <-