search for: makepredictcall

Dear All, I'm interested in creating a function similar to ns() from package splines that can be passed in a model formula. The idea is to produce "safe" predictions from a model using this function. As I have seen, to do this I need to use makepredictcall(). Consider the following toy example: myns <- function (x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = range(x), extraArg = 0) { ns.x <- if (is.null(knots)) { ns(x, df = df, intercept = intercept, Boundary.knots = Boundary.knots) } e...

An issue came up with the rms package today that makepredictcall would solve, and I was going to suggest it to the author. But looking in the help documents I couldn't find any reference to it. There is a manual page, but it does not give much aid in creating code for a new transformation function. Did I miss something? If not, I'd be willing to dr...

...: > See the developer site, e.g. > http://developer.r-project.org/model-fitting-functions.txt . > > That is where specialized info is (and this is specialized). > > On 06/03/2014 18:19, Therneau, Terry M., Ph.D. wrote: >> >An issue came up with the rms package today that makepredictcall would >> >solve, and I was going to suggest it to the author. But looking in the >> >help documents I couldn't find any reference to it. There is a manual >> >page, but it does not give much aid in creating code for a new >> >transformation function. Did I...

Hi all, Very surprising (to me!) and mystifying result from predict.glm(): the predictions vary depending on whether or not I use ns() or splines::ns(). Reprex follows: library(splines) set.seed(12345) dat <- data.frame(claim = rbinom(1000, 1, 0.5)) mns <- c(3.4, 3.6) sds <- c(0.24, 0.35) dat$wind <- exp(rnorm(nrow(dat), mean = mns[dat$claim + 1], sd = sds[dat$claim + 1])) dat <-

....2270250736796, 35.4093171222502, 43.038645381669), Boundary.knots = c(12.9423820390783, 108.071583734075), intercept = FALSE)) > attr(m2$terms, "predvars") list(claim, splines::ns(wind, df = 5)) This appears to be due to a bug in the splines package. There, the function splines:::makepredictcall.ns looks like this: makepredictcall.ns <- function(var, call) { if(as.character(call)[1L] != "ns") return(call) at <- attributes(var)[c("knots", "Boundary.knots", "intercept")] xxx <- call[1L:2L] xxx[names(at)] <- at xxx...

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

I recently got a request to add head() and tail() methods for Surv objects, which is quite reasonable, but not unlike other requests for logLik,? vcov, extractAIC, ...?? What they all have in common is that are methods added since creation of the survival package, and that I didn't know they existed. To try and get ahead of the curve, is there a way to list names of all of the default

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