similar to: splitting up optional args

On SVN revision 41087: ?args has this example line: args(c) # -> NULL (c is a 'primitive' function) The comment seems out of date, as args(c) does in fact have a non-NULL return value: args(c) # function (..., recursive = FALSE) # NULL While at it, I was wondering, why formals(c) still returns NULL, in contrast. Regards Thomas Friedrichsmeier -------------- next

Dear R Help folks -- I have been trying to put together a list of the steps or stages of R function evaluation, with particular focus on those that have "standard" or "nonstandard" forms. This is both for my own edification and also because I am thinking of joining the world of R bloggers and have been trying to put together some draft posting that might be useful. I seem to

I am trying to reset the default arguments in the rnbinom function with the following example code: params <- c("size" = 1, "mu" = 1) formals(rnbinom)[names(params)] <- params rnbinom(n = 10) It returns the following: Error in rnbinom(n = 10) : argument "prob" is missing, with no default If I set the defaults with this code: params <- c("size"

? Sat, 17 Feb 2024 11:15:43 -0700 "Reed A. Cartwright" <racartwright at gmail.com> ?????: > I'm wrapping a function in R and I want to record all the arguments > passed to it, including default values and missing values. This is hard if not impossible to implement for the general case because the default arguments are evaluated in the environment of the function as it

Tested in R 2.8.1 Windows > ff <- formals(function(x)1) > ff1 <- as.list(function(x)1)[1] # ff1 acts the same as ff in the examples below, but is a list rather than a pairlist > dput( ff , control=c("warnIncomplete")) list(x = ) This string is not parsable, but dput does not give a warning as specified. > dput( ff ,

Dear all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

Hi, I am a little bit surprised by the following output of 'formals'. Is this the intended behavior? > f <- function(a=1,b=-1) { a+b } > class(formals(f)$a) [1] "numeric" > class(formals(f)$b) [1] "call" Josef -- ----------------------------------------------------------------------------- Josef Leydold | WU (Vienna University of Economics and

Hi, I am not a statistics expert, so I have this question. A linear model gives me the following summary: Call: lm(formula = N ~ N_alt) Residuals: Min 1Q Median 3Q Max -110.30 -35.80 -22.77 38.07 122.76 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.5177 229.0764 0.059 0.9535 N_alt 0.2832 0.1501 1.886 0.0739

Hi all, What's the official name for the value that represents a missing argument? e.g. formals(plot)$x str(formals(plot)$x) deparse(formals(plot)$x) is.symbol(formals(plot)$x) What's the correct way to create an object like this? (for example if you are manipulating the formals of a function to add an argument with no default value, as in http://stackoverflow.com/questions/3892580/).

Dear Group: I want to create a function having a ... argument and to have the default arguments evaluated, as thus: g <- function(a, b, ...) a+b formals(g) <- alist(a=,b=2+3,...=) g function (a, b = 2 + 3, ...) a + b But I want the default argument for b to be evaluated as 5. How can this be done? Note: My real need is for a more complex expression to be evaluated for the default

Full_Name: Fabian Scheipl Version: 2.8.0 OS: Windows, Linux Submission from: (NULL) (138.246.7.150) It seems to me that the documentation for body<- is wrong. The help file for body(fun, envir = environment(fun)) <- value says that: value can be an expression or a list of R expressions. This produces errors however: ################################ > f <- function(x){} >

Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1

Dear R-devel list, I realized that removing a predictor in lm through the "-"'s operator in formula() does not affect the complete cases that are considered. A minimal example is: summary(lm(Wind ~ ., data = airquality)) # 42 observations deleted due to missingness summary(lm(Wind ~ . - Ozone, data = airquality)) # still 42 observations deleted due to missingness, even if only 7

I want to make a multi-dimensional array. To be specific I want to make the following array results<-array(0,dim=c(2,2,64,7)) This is the code I have created but it gives no result due to the error "subscript out of bound". x<-rep(7,7) # Missingness in intervention y<-rep(7,7) # Missingness in control

Dear all, I would like to model the relationship between y and x. y is binary variable, and x is a count variable which may be possion-distribution. I think it is better to divide x into intervals and change it to a factor before calling glm(y~x,data=dat,family=binomail). I try to use rpart. As y is binary, I use "class" method and get the following result. >

There is a problem when importing an spss-file containing explicitly declared missing values in R using the read.spss function from the foreign package. I'm not sure these problems are the same in every version of spss, I am using the latest version 16.0.2. I included http://www.nabble.com/file/p18776776/missingdata.sav missingdata.sav and

Duncan, Thank you for the clarification on how delayedAssign works. Should R-level interfaces to promise objects ever become available, I expect they would at time come in handy. On the subject of substitute and delayedAssign, I do have a follow-up question for the list. I'm trying to convert a named list of expression objects into an environment of promise objects. After conversion, each

Here is a suggestion for improving match.arg() Comments welcome. Best Jens Oehlschl?gel # up to now match.arg() works as t1 <- function( param = c("default", "alternative1", "alternative2") ){ param <- match.arg(param) param } # and args(t1) # > function (param = c("default", "alternative1", "alternative2")) #

Hi, Thanks for the note. How would explain the following snippet taken from `formals` doc page (the code comment is also from that doc) ? ## formals returns NULL for primitive functions. Use it in combination with ## args for this case. is.primitive(`+`) formals(`+`) formals(args(`+`)) Le sam. 6 oct. 2018 ? 13:42, Rui Barradas <ruipbarradas at sapo.pt> a ?crit :

symbols(iris[,1],iris[,2],rectangles=as.matrix(iris[,3:4]),inches=0.3, ylim=c(1,5)) Error in plot.default(NA, NA, type = "n", ylim = ylim, xlim = xlim, xlab = xlab, : formal argument "ylim" matched by multiple actual arguments Since plot is called inside symbols, with specified xlim and ylim, any specification through ,... hurts this call. I am pretty sure that you