similar to: plot(<lm>): new behavior in R-2.2.0 alpha

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

This bug is not quite fixed - the example from my original report now = works using R-2.2.1, but plot(Uniform, 6) does not. The bug is due to if (show[6]) { ymx <- max(cook, na.rm =3D TRUE) * 1.025 g <- hatval/(1 - hatval) # Potential division by zero here # plot(g, cook, xlim =3D c(0, max(g)), ylim =3D c(0, ymx),=20 main =3D main, xlab =3D

I propose the following enhancements and changes to plot.lm(), the most important of which is the addition of a Residuals vs Leverage plot. (1) A residual versus leverage plot has been added, available by specifying which = 5, and not included as one of the default plots. Contours of Cook's distance are included, by default at values of 0.5 and 1.0. The labeled points, if any, are those

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

The following code demonstrates an annoyance with plot.lm(): library(DAAGxtras) x11(width=3.75, height=4) nihills.lm <- lm(log(time) ~ log(dist) + log(climb), data = nihills) plot(nihills.lm, which=5) OR try the following xy <- data.frame(x=c(3,1:5), y=c(-2, 1:5)) plot(lm(y ~ x, data=xy), which=5) The "Cook's distance" text overplots the label for the point with the

I was wondering if someone might be able to tell me what formula R's influence.measures function uses for determining whether the hat value it computes is influential (i.e., the true/false value in the "hat" column of the returned is.inf data frame). The reason I'm asking is that its results disagree with what I've just learned in my statistics class, namely that a point

Hi, I am confused about how to select elements from a list. I'm trying to select all rows of a table 'crossRsorted' such that the mean of a related vector is > 0. The related vector is accessible as a list element l[[i]] where i is the row index. I thought this would work: > crossRsorted[mean(q[[ crossRsorted[,1] ]], na.rm = TRUE) > 0, ] Error in q[[crossRsorted[, 1]]] :

Hello Dear, I have a function, like z=f(x,y), and try a surface plot with this function. But, on the reference of "wireframe" requires data option, so I generated x and y, and computed z with them. But, still I have a problem to draw a surface plot. The code and errors are ################################################## mle_beta0=64.43707; mle_beta1=-24365.16; # generating for

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hi, Figure 13 of Emmanuel Paradis's "R for Beginners" was produced by termplot working on an aov object. The lower right-hand plot is labelled "Cook's distance plot", and I'd really like to produce a similar type of figure, but in a totally different context. (I'm not even sure what this kind of figure is called, perhaps an "impulse plot", where

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters. For

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant

Well I suppose a warning's not going to hurt. Even in a case like the occupationalStatus example where you know some points have been fitted exactly, it might be useful to be reminded that the standardised residuals for these points are then NaN and cannot be displayed. Of course when you don't know in advance that this issue will arise, there is even more reason to give a warning.

Hi Community, I'm trying to reconcile Cook's Distances computed in glm. The following snippet of code shows that the Cook's Distances contours on the plot of Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9)

On Tue, Jan 10, 2017 at 08:13:21PM -0600, Dirk Eddelbuettel wrote: > > On 10 January 2017 at 17:48, frederik at ofb.net wrote: > | Hi R Devel, > | > | I just ran into a corner case with 'strptime'. Recall that the "%OS" > | conversion accepts fractional seconds: > | > | > strptime("17_35_14.01234.mp3","%H_%M_%OS.mp3")$sec > |

Hi R Devel, I just ran into a corner case with 'strptime'. Recall that the "%OS" conversion accepts fractional seconds: > strptime("17_35_14.01234.mp3","%H_%M_%OS.mp3")$sec [1] 14.01234 Unfortunately for my application it seems to be "greedy", in that it tries to parse a decimal point which might belong to the rest of the format: >

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

(1) read.table(), with sep="\t", identifies 13 our of 1400 records, in a file with 1400 records of 3 fields each, as having only 2 fields. This happens under version 2.3.1 for Windows as well as with R 2.3.1 for Mac OS X, and with R-devel under Mac OS X. [R version 2.4.0 Under development (unstable) (2006-07-03 r38478)] (2) Using read.table() with sep="\t", the first 1569

In the following special case, seq fails to give the right answer (which is 0 ) > seq(0,0,1) Error in if (dd < sqrt(.Machine$double.eps)) return(from) : missing value where logical needed For any other equal from and to , it works: > seq(1,1,1) [1] 1 The error occurs in the statement dd <- abs(del)/max(abs(to), abs(from)) of seq.default -- for obvious reasons.