search for: infmat

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

I am using rw0641 with Windows 98. To list just the influential repetitiones that result from "influence.measures", I am using the input result <- lm(y~x) and the code from the example in the help for "influence.measures" INFLM <- function(result){ inflm <- influence.measures(result) which(apply(inflm$is.inf,1,any)) } It works fine up to now with the

...- theta shape<- theta0[1] rate<- theta0[2] S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1) I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2) theta<- theta0 + solve(I) %*% S if(max(abs(theta-theta0)) < 1e-08) break } list(estimates=theta, infmat=I) } However, this appears: Error: object "gamma.mles" not found I tried looking in the packages for gamma.mles, but I couldn't find it anywhere. Can someone tell me where can I load it? Thanks -- View this message in context: http://www.nabble.com/Gamma-MLE-tf4122769.html#a117248...

...03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > influence.measures(glm.D93)$infmat[, 8] 1 2 3 4 5 6 0.288294276 0.309131723 0.011614584 0.030963844 0.304525117 0.444410274 7 8 9 0.459190432 0.002802907 0.377028535 > cooks.distance(glm.D93) 1 2 3 4...

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