similar to: standardized residuals (rstandard & plot.lm) (PR#8468)

Full_Name: Heather Turner Version: 2.2.0 OS: Windows XP Submission from: (NULL) (137.205.240.44) Standardized residuals as calculated by rstandard.lm, rstandard.glm and plot.lm are Inf/NaN rather than zero when the un-standardized residuals are zero. This causes plot.lm to break when calculating 'ylim' for any of the plots of standardized residuals. Example:

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson",

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I

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, the application of the function rstandard() in the base package to a glm object does not produce residuals standardized to have variance one: the reason is that the deviance residuals are divided by the dispersion estimate and not by the square root of the estimate for the dispersion. Should the function not be changed to produce residuals with a variance about 1? R 1.8.1 on

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.

Hola Ángel: Yo creo que tendrías que establecer el sistema de coordenadas de referencia de tu objeto raster antes de salvarlo como GTiff. Algo así: crs(r1) <-"+proj=utm +zone=14 +datum=WGS84" Saludos, Marcelino. El 20/02/2020 a las 1:42, Angel Cervantes escribió: > Hola a todos, quisiera pedirles su ayuda. Estoy tratando de crear un raster a partir de una tabla de datos

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I've been looking fairly carefully at the Q-Q plots produced by plot.lm() and am having difficulty understanding why plot.lm() is doing what it's doing, specifically scaling the standardized residuals by the prior weights. Can anyone explain this to me ... ? Multiplying by the weights seems to give the wrong plot, at least for binomial

This code will read binary file and display it as a map. may problem is that this code is using a continuous colour scheme, even though I have discrete data (which is a classification scheme). How can I map numbers to colours with raster? Please require(raster) conne <- file("C:\\lai.bin", "rb") sd<- readBin(conne, integer(), size=1, n=360*720, signed=F)

Hi all!   My question is how to colour pixels by z value in persp plot in raster package. Here is an example:     x <- seq(-1.95, 1.95, length = 30) y <- seq(-1.95, 1.95, length = 35) z <- outer(x, y, function(a,b) a*b^2) r1 <- raster(nrows=35, ncols=30, xmn=0, xmx=30, ymn = 0, ymx = 35) r1[] <- c(z) persp(r1)   There already exist some function to produce persp plot for anothe

Hola grupo, estoy siguiendo una gu?a de la librer?a rLandsat que me la he descargado de: devtools::install_github("socialcopsdev/rLandsat") Y tras hacer los siguiente (obviamente tengo me he registrado previamente en la api correspondiente): product_id = c("LC08_L1TP_145049_20180301_20180308_01_T1", "LC08_L1TP_145049_20170330_20170414_01_T1",

Hi all, Two questions: 1. Is there a way to evaluate models from lmer() with a poisson distribution? I get the following error message: library(lme4) lmer(tot.fruit~infl.treat+def.treat+(1|initial.size),family=poisson)->model plot(fitted(model),resid(model)) Error: 'resid' is not implemented yet Are there any other options? 2. Why doesn't the function estimable() in gmodels

I'm trying to write some functions extending influence measures to multivariate linear models and also allow subsets of size m>=1 to be considered for deletion diagnostics. I'd like these to work roughly parallel to those functions for the univariate lm where only single case deletion (m=1) diagnostics are considered. Corresponding to stats::hatvalues.lm, the S3 method for class

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,

Hi, I have the following code from Splus that I'd like to migrate to R. So far, the only problem is the arima.filt function. This function allows me to filter an existing time-series through a previously estimated arima model, and obtain the residuals for further use. Here's the Splus code: # x is the estimation time series, new.infl is a timeseries that contains new information # a.mle

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat :

Hi all, I'm trying to apply loess regression to my data and then use the fitted model to get the *standardized/studentized residuals. I understood that for linear regression (lm) there are functions to do that:* * * fit1 = lm(y~x) stdres.fit1 = rstandard(fit1) studres.fit1 = rstudent(fit1) I was wondering if there is an equally simple way to get the standardized/studentized residuals for a

In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

Hi, I found that using different contrasts (e.g. contr.helmert vs. contr.treatment) will generate different fitted probabilities from multinomial logistic regression using multinom(); while the fitted probabilities from binary logistic regression seem to be the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables