search for: hatvalu

...easures 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 "lm" objects, > hatvalues <-function (model, ...) UseMethod("hatvalues") > hatvalues.lm <- function (model, infl = lm.influence(model, do.coef = FALSE), ...) { hat <- infl$hat names(hat) <- names(infl$wt.res)...

Hello: I am trying to understand the method 'hatvalues(...)', which returns something similar to the diagonals of the plain vanilla hat matrix [X(X'X)^(-1)X'], but not quite.  A Fortran programmer I am not, but tracing through the code it looks like perhaps some sort of correction based on the notion of 'leave-one-out' variance i...

I am struiggling a bit with this function 'hatvalues'. I would like a little more undrestanding than taking the black-box and using the values. I looked at the Fortran source and it is quite opaque to me. So I am asking for some help in understanding the theory. First, I take the simplest case of a single variant. For this I turn o John Fox...

Hi all, In using the identify command, I get the following message > plot(hatvalues(scireg3)) > abline(h=.0154,lty=2) # plots a reference line at (k + 1)/n > identify(1:1165, hatvalues(scireg3),row.names(sciach)) Error in xy.coords(x, y) : 'x' and 'y' lengths differ which doesn't allow me to see the observation number when I scroll over with the...

...mula for Cook's distance can be found for example here: http://en.wikipedia.org/wiki/Cook%27s_distance I tried to apply it in R: > y <- (1:400)^2 > x <- 1:100 > lm(y~x) -> linmod # just for the sake of a simple example > linmod$residuals[1]^2/(2*mean(linmod$residuals^2))*(hatvalues(linmod)[1]/(1-hatvalues(linmod)[1])^2) 1 0.02503195 > cooks.distance(linmod)[1] 1 0.02490679 Why differ the two results? Thanks a lot if somebody have some instructions for me. Best wishes: Kolos

I understand that the hat matrix is a function of the predictor variable alone. So, in the following example why do the values on the diagonal of the hat matrix change when I go from an unweighted fit to a weighted fit? Is the function hatvalues giving me something other than what I think it is? library(ISwR) data(thuesen) attach(thuesen) fit <- lm(short.velocity ~ blood.glucose) summary(fit) hatvalues(fit) W <- 1/blood.glucose fit.w <- lm(short.velocity ~ blood.glucose,weights=W) summary(fit.w) hatvalues(fit....

Let me try to be more specific. The x y coordinates are different because of NAs in the dataset. In this analysis, a set of hat values (a measure of influence in regression) is given for each observation. On the basis of the regression that was run to get these hat values, the sample size was 1164 (one removed due to NA). The length of the data set is 1165. If I remove the NA from the

...Macintosh. I recently upgraded from version 1.6.0 and found that the "car" library now has a problem: ---Begin transcript--- >library(car) Attaching package 'car': The following object(s) are masked from package:base : dfbeta dfbeta.lm dfbetas dfbetas.lm hatvalues hatvalues.lm influence influence.glm influence.lm rstudent rstudent.glm rstudent.lm >x <- 1:10 >y <- 2*x+3+2*rnorm(10) >model<-lm(y~x) >qq.plot(model) Bus error ---End transcript--- No plot appears. After the bus error, R no longer operates normally, and I have to kill t...

...h_i=1 for all i, hence one just adds 1/2 to each count, which is equivalent to the Jeffrey's prior, but I'd also like to get bias corrected estimates for other log linear models. It appears that I need to iterate using GLM, with the weights option and h_i, which I can get from the function hatvalues. For logistic regression, this can be performed by splitting up each observation into response and nonresponse, and using weights as described in Heinze, G. and Schemper, M. (2002), but I'm unsure of how to implement the analogue for log-linear models. A procedure using IWLS is described by F...

Hi there, I hope I can get advice regarding the calculation of leverage values or studentized residual values of a non-linear regression model. It seems like rstudent() does not work on a nls object. Many thanks in advance! Best regards, Xingli

....1=betareg(Y~X1+X2,data=dump) summary(f2.1) I get : Call: betareg(formula = Y ~ X1 + X2, data = dump) Standardized weighted residuals 2: Error in quantile.default(x$residuals) : missing values and NaN's not allowed if 'na.rm' is FALSE In addition: Warning message: In sqrt(v * (1 - hatvalues(object))) : NaNs produced Does anybody know how to fix this? Oyvind -- View this message in context: http://r.789695.n4.nabble.com/Error-using-betareg-tp3621955p3621955.html Sent from the R help mailing list archive at Nabble.com.

Martin - Thanks, but although hatvalues() is useful for calculating PRESS, I can't find anything directly relevant to my question in the influence help pages. After some burrowing in the literature I'm doubting there is an answer out there (PRESS R^2 is always presented in a fairly ad hoc way). This is a new topic, as you say,...

>>>>> Murray Efford <murray.efford at otago.ac.nz> >>>>> on Fri, 8 Apr 2016 18:45:33 +0000 writes: > Thanks for these perfectly consistent replies - I didn't > understand the purpose of m = sum(w * f/sum(w)) and saw it > merely as a weighted average of the fitted values. My > ultimate concern is how to compute an appropriate

> On Apr 10, 2016, at 3:11 AM, Murray Efford <murray.efford at otago.ac.nz> wrote: > > Martin - > Thanks, but although hatvalues() is useful for calculating PRESS, I can't find anything directly relevant to my question in the influence help pages. After some burrowing in the literature I'm doubting there is an answer out there (PRESS R^2 is always presented in a fairly ad hoc way). > This is a new topic, as you...

...uot;lm") [1] add1.lm* alias.lm* anova.lm case.names.lm* [5] confint.lm* cooks.distance.lm* deviance.lm* dfbeta.lm* [9] dfbetas.lm* drop1.lm* dummy.coef.lm* effects.lm* [13] extractAIC.lm* family.lm* formula.lm* hatvalues.lm [17] influence.lm* kappa.lm labels.lm* logLik.lm* [21] model.frame.lm model.matrix.lm nobs.lm* plot.lm [25] predict.lm print.lm proj.lm* qr.lm* [29] residuals.lm rstandard.lm rstudent.lm simulate.lm* [33] su...

...nd on some platforms not detecting hat = 1 correctly. The last sentence identifies NaN being reported for sigma, as I find with my data. But my data do not have hat = 1, but the hat diagonals are large. The troublesome Observation 3 does not have the largest hat value in the data though: > hatvalues(dep.glm2) 1 2 3 4 5 6 7 8 0.1689061 0.1064651 0.9098542 0.9030814 0.3799079 0.6382790 0.9327408 0.9607654 And besides, I am using the most recent version of R (see below). BTW, the NaNs appear in the previous version of R also....

> On Apr 10, 2016, at 9:38 AM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Apr 10, 2016, at 3:11 AM, Murray Efford <murray.efford at otago.ac.nz> wrote: >> >> Martin - >> Thanks, but although hatvalues() is useful for calculating PRESS, I can't find anything directly relevant to my question in the influence help pages. After some burrowing in the literature I'm doubting there is an answer out there (PRESS R^2 is always presented in a fairly ad hoc way). >> This is a new topic, as...

Hello, I have 4 questions in R and I am looking for 4 answers! the questions are the below: 1) is there a function in R for the Least Significant Difference method for multiple comparisons? 2) Is there a function in R that gives me the studentized deleted residuals and the leverage points? 3) is there a way that I could create a link in a program of mine, so as to select an option when my program

Dear R-help, Does anybody know where can I find R/Splus code for computing PREdiction Sum of Squares (PRESS) in a linear regression model? I have a large regression model (~100 predictors, ~30,000 cases) and good prediction is the main goal. I remember there is a faster way to compute it (no need to repeatedly fit models), but if there is existing code...... Thanks. Mai Z

...lies. However, for the inverse Gaussian, the choice seems to me less clear cut. So I just wanted to compare two different options. I have used the dispersion parameter to compute the standardized deviance residuals: summary(model.gamma)$deviance.resid /(summary(model.gamma)$dispersion * (1 - hatvalues(model.gamma)))^0.5 I noticed differences with Genstat which outputs these stand. dev. residuals directly, and they are explained by the automatic use of deviance instead of Pearson. Kind regards, Robin Smit -----Original Message----- From: Prof Brian Ripley [mailto:ripley@stats....