similar to: formulas with non-syntactic names and an Error() term

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

SImplify your call to lm using the "." argument instead of manipulating formulas. > strt <- lm(y1 ~ ., data = dat) and you do not need to explicitly specify the "1+" on the rhs for lm, so > frm2<-as.formula(paste(trg," ~ ", paste(xvars,collapse = "+"))) works fine, too. Anyway, doing this gives (but see end of output)" bst <-

Failed? What was the error message? Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Aug 22, 2017 at 8:17 AM, Stephen O'hagan <SOhagan at manchester.ac.uk> wrote: > I'm trying to use boot.stepAIC for

The error is "the model fit failed in 50 bootstrap samples Error: non-character argument" Cheers, SOH. On 22/08/2017 17:52, Bert Gunter wrote: > Failed? What was the error message? > > Cheers, > > Bert > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### Is this intended behavior for the read.fwf(na.strings="-999")? I anticipated that

Hello R Gurus, I am perplexed by the different results I obtained when I ran code like this: set.seed(100) test1<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200) predict(test1, newdata=cbind(NewBinaryY, NewXs), type="response") and this code: set.seed(100) test2<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200, xtest=NewXs, ytest=NewBinarY) The

This feels like inconsistent behavior. latticeParseFormula works the way I anticipated for factor, but not for ordered. I want the behavior I see with tmp2, but not with tmp. My next step is to use the right.name to isolate the tmp2[,c("a","b")] columns. tmp <- data.frame(y=(1:12)+.1, a=factor(rep(1:3,4)), b=ordered(rep(1:4,

OK, here's the problem. Continuing with your example: strt1 <- lm(y1 ~1, dat) strt2 <- lm(frm1,dat) > strt1 Call: lm(formula = y1 ~ 1, data = dat) Coefficients: (Intercept) 41.73 > strt2 Call: lm(formula = frm1, data = dat) Coefficients: (Intercept) 41.73 Note that the formula objects of the lm object are different: strt2 does not evaluate the formula. So

I'm trying to use boot.stepAIC for feature selection; I need to be able to specify the name of the dependent variable programmatically, but this appear to fail: In R-Studio with MS R Open 3.4: library(bootStepAIC) #Fake data n<-200 x1 <- runif(n, -3, 3) x2 <- runif(n, -3, 3) x3 <- runif(n, -3, 3) x4 <- runif(n, -3, 3) x5 <- runif(n, -3, 3) x6 <- runif(n, -3, 3) x7

Consider: x <- list(`a b` = 1) x$a<tab> (i.e., press the 'tab' key after typing 'x$a') The auto-complete mechanism will fill the buffer like so: x$a b This is not particularly helpful because this is now a syntax error. It seems to me there's a simple fix -- in utils:::specialCompletions(), we can wrap the result of utils:::specialOpCompletionsHelper() with

Great suggestion! I've started a patch: https://bugs.r-project.org/show_bug.cgi?id=18479 On Wed, Mar 1, 2023 at 1:56 AM Ivan Krylov <krylov.r00t at gmail.com> wrote: > > ? Wed, 1 Mar 2023 01:36:02 -0800 > Michael Chirico via R-devel <r-devel at r-project.org> ?????: > > > +comps[non_syntactic] <- paste0("`", comps[non_syntactic], "`") >

There turn out to be a few more things to fix. One problem is easy to solve: vapply() needs a third argument specifying the type of the return value. (Can we have unit tests for tab completion?) The other problem is harder: `comps` defaults to an empty string, and you can't have a symbol consisting of an empty string, because this value is internally reserved for missing function arguments.

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

For the vcdExtra package, I'm exploring methods to generate and fit collections of glm models, and handling lists of such model objects, of class "glmlist". The simplest example is fitting all k-way models, from k=0 (null model) to the model with the highest-order interaction. I'm having trouble writing a function, Kway (below) to do what is done in the example below >

Dear R user, I am new with split-plot designs and I have problems with multiple comparisons. This data correspond to an split-plot experiment with two replications (bloque).(Hoshmand, 2006 pp 138). Briefly, the whole-plot factor is Nitrogen concentration ("nitrogeno") and the subplot factor is the variety of corn ("hibrido"). The aim is to determine if major differences

Dear List, I am having trouble with some R code I have written to perform Redundancy Analysis (RDA) on a matrix of species abundance data (Y) and a matrix of environmental data (X). RDA is a constrained form of PCA and can be thought of as a PCA of the fitted values of a regression of each variable in Y on all variables in X. For info, the first use of RDA is in: Rao, C.R, 1964. The use and

## the following is a dream: add some sugar syntax to allow for contracts with teeth (in and out checking) > is.positive <- function(x) (all(x>0)) > exponentiate <- function( x ::is.data.frame , exponent ::is.numeric is.positive) :: is.vector is.numeric { x$base :: is.positive ## error also if base does not exist in x; may need some special IQ x$base^exponent }

It seems that if you build the formula as a character string, and postpone the "as.formula" into the lm call, it works. instead of frm1 <- as.formula(paste(trg,"~1")) use frm1a <- paste(trg,"~1") and then strt <- lm(as.formula(frm1a),dat) regards, Heinz Stephen O'hagan wrote/hat geschrieben on/am 23.08.2017 12:07: > Until I get a fix that works, a

> tmp <- data.frame(y=rnorm(12), a=factor(rep(letters[1:4],3))) > tmp y a 1 -0.60866099 a 2 0.55500538 b 3 0.12231693 c 4 -0.24613790 d 5 -0.09253593 a 6 -1.54652581 b 7 0.17204210 c 8 -1.22778942 d 9 1.22151194 a 10 -0.43982577 b 11 -1.25444287 c 12 -0.97251060 d > tmp.aov <- aov(y ~ a, data=tmp) > summary(tmp.aov) Df Sum Sq Mean Sq F value