search for: mikewlcheung

...-1 variances as known values in lme(). Thanks in advance! Best, Mike -- --------------------------------------------------------------------- Mike W.L. Cheung Phone: (852) 2857-8621 Department of Psychology Fax: (852) 2858-3518 The University of Hong Kong E-mail: mikewlcheung at hku.hk HONG KONG Website: http://web.hku.hk/~mikewlch

Dear all, According to CHANGES IN R 3.0.0: o diag() as used to generate a diagonal matrix has been re-written in C for speed and less memory usage. It now forces the result to be numeric in the case diag(x) since it is said to have 'zero off-diagonal entries'. diag(x) does not work for character vector in R 3.0.0 any more. For example, v <- c("a",

Dear List, I have some discrete data and want to calculate the percentiles and the percentile ranks for each of the unique scores. I can calculate the percentiles with quantile(). I know that "ecdf" can be used to calculate the empirical cumulative distribution. However, I don't know how to exact the cumulative probabilities for each unique element. The requirement is similar

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels