search for: covratio

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the computations can be optimized, as they are in stats::influence() and related methods. I'm interested...

Hello everyone., I am looking for a covariance function this not the first time I have this type of problem (to find which function does something). I try in google with "R cran covariance function" but usually this ends with different results that do not help me that much. Could you please try to advice me how to search for what function implements the functionality you want.

...eturns elements sigma and coefficients that are only based on the first variable (first column of the residual matrix wt.res) and give wrong results for other variables. This will influence functions dfbeta.lm (coefficients), dfbetas.lm (coefficients, sigma), dffits (sigma), rstudent.lm (sigma) and covratio (sigma). lm.influence finds these elements in compiled code and this is harder to fix. MASS (the book & the package) avoid using compiled code in their (univariate) studentized residuals, and instead use a clever short-cut. In addition to these, there are a couple of other cases which seem to...

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

...lowing up on a question I posted 8/10/2010, but my newsreader has lost this thread. > Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general > classes of influence measures for multivariate > regression models, including analogs of Cook's D, Andrews & Pregibon > COVRATIO, etc. As in univariate > response models, these are based on leverage and residuals based on > omitting one (or more) observations at > a time and refitting, although, in the univariate case, the > computations can be optimized, as they are in > stats::influence() and related meth...

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my

...column by column as documented, so character or logical matrices generate factor columns. o Formulae containing interactions with the response were handled incorrectly by model.matrix, giving wrong answers or crashes. o anova(lm(.)) with 0 weights now works. o plot.lm(), dffits(), covratio() now do the right thing for weighted lm regression. o formatC(as.integer(c(1,0,NA))) now works. PR#394, thanks to Jens O.-A. o format.char(.) is much faster for long vector argument [Jens O.-A.] o \enumerate now enumerates in text conversion of .Rd files. o glm() no longer fai...

...column by column as documented, so character or logical matrices generate factor columns. o Formulae containing interactions with the response were handled incorrectly by model.matrix, giving wrong answers or crashes. o anova(lm(.)) with 0 weights now works. o plot.lm(), dffits(), covratio() now do the right thing for weighted lm regression. o formatC(as.integer(c(1,0,NA))) now works. PR#394, thanks to Jens O.-A. o format.char(.) is much faster for long vector argument [Jens O.-A.] o \enumerate now enumerates in text conversion of .Rd files. o glm() no longer fai...