search for: getxlevels

...] "A" "B" ?C" - - - - snip- - - - - Why the discrepancy? It?s true that the level-set (i.e., TRUE, FALSE) for a logical ?factor? is known, but examining the $levels component is a simple way to detect variables treated as factors in the model. For example, I?d argue that .getXlevels() returns misleading information: - - - - snip- - - - - > .getXlevels(terms(mod), model.frame(mod)) $c [1] "a" "b" "c" $f [1] "A" "B" ?C" - - - - snip- - - - - An alternative for detecting ?factors? is to examine the 'contrasts'...

..."setosa"), data = iris) Coefficients: (Intercept) Sepal.Width I(Species == "setosa")TRUE 3.5571 0.9418 -1.7797 ------------ snip -------------- I believe that the culprit is .getXlevels(), which makes provision for factor and character predictors but not for logical predictors: ------------ snip -------------- > .getXlevels function (Terms, m) { xvars <- vapply(attr(Terms, "variables"), deparse2, "")[-1L] if ((yvar <- attr(Terms, &q...

...m.wfit(x, y, w, offset = offset, singular.ok = singular.ok, ...) } class(z) <- c(if (mlm) "mlm", "lm") z$na.action <- attr(mf, "na.action") z$offset <- offset z$contrasts <- attr(x, "contrasts") z$xlevels <- .getXlevels(mt, mf) z$call <- cl z$terms <- mt if (model) z$model <- mf if (ret.x) z$x <- x if (ret.y) z$y <- y if (!qr) z$qr <- NULL z } John David Sorkin M.D., Ph.D. Professor of Medicine, University of Maryland School of Medici...

...ok = singular.ok, > ...) > } > class(z) <- c(if (mlm) "mlm", "lm") > z$na.action <- attr(mf, "na.action") > z$offset <- offset > z$contrasts <- attr(x, "contrasts") > z$xlevels <- .getXlevels(mt, mf) > z$call <- cl > z$terms <- mt > if (model) > z$model <- mf > if (ret.x) > z$x <- x > if (ret.y) > z$y <- y > if (!qr) > z$qr <- NULL > z > } > > > > J...

...ok = singular.ok, > ...) > } > class(z) <- c(if (mlm) "mlm", "lm") > z$na.action <- attr(mf, "na.action") > z$offset <- offset > z$contrasts <- attr(x, "contrasts") > z$xlevels <- .getXlevels(mt, mf) > z$call <- cl > z$terms <- mt > if (model) > z$model <- mf > if (ret.x) > z$x <- x > if (ret.y) > z$y <- y > if (!qr) > z$qr <- NULL > z > } > > > > John...

Interesting discussion. A few things occurred to me. Apologies to Iris Simmons: I mixed up his answer with Bert's question. Bert raises questions about promises, and I think they are related to John Sorkin's question. A big difference between R and most other languages is that function arguments are computed lazily. match.call & substitute tell us what expressions will be evaluated

I would like to figure out the equations for calculating "AIC" in both "step() function" and "AIC () function". They are different. Then I just type "step" in the R console, and found the "AIC" used in "step() function" is "extractAIC". I went to the R help, and found: "The criterion used is AIC = - 2*log L + k *

...paths on all interfaces (they used to on some and not others). o The find() argument is now named 'numeric' and not 'numeric.': the latter was needed to avoid warnings about name clashes many years ago, but partial matching was used. o stats:::.getXlevels() confines attention to factors since some users expected R to treat unclass(<a factor>) as a numeric vector. o grep(), strsplit() and friends now warn if incompatible sets of options are used, instead of silently using the documented priority. o...

...paths on all interfaces (they used to on some and not others). o The find() argument is now named 'numeric' and not 'numeric.': the latter was needed to avoid warnings about name clashes many years ago, but partial matching was used. o stats:::.getXlevels() confines attention to factors since some users expected R to treat unclass(<a factor>) as a numeric vector. o grep(), strsplit() and friends now warn if incompatible sets of options are used, instead of silently using the documented priority. o...