similar to: Creating model frame from R formula inside compiled code

In R 2.12.0 I get > d <- data.frame(x=1:10, y=log(1:10), f3=LETTERS[rep(1:3,c(3,3,4))]) > m <- model.frame(y~.^2, data=d) > formula(m) y ~ x + f3 In S+ formula(m) gives formula given to model.frame(), but in R you have to do the following get that formula: > formula(attr(m, "terms")) y ~ (x + f3)^2 Would it break anything to add to the top of

I don't have a copy of SV4 (or SV3, where model.frame was introduced), but S+ 8.3 (based on SV4) puts the class "model.frame" on model.frame()'s return value but has no methods (in the default packages) for class "model.frame". Perhaps that is why R omitted the class. However, S+ 8.3's (and problably S's) formula.data.frame did look for a "terms"

Dear R-users, I keep getting an ERROR saying " Error in model.matrix.default(formula, mframe) : model frame and formula mismatch in model.matrix() " when i fit poLCA with more than 63 variables. Below are the details. I am trying to do a Latent Class Analysis using poLCA. My data set contains binary scores of, for instance, 200 students on 100 items. These numbers could even be more

When formula() is applied to the output of model.frame() it ignores the formula in the model.frame's 'terms' attribute: > d <- data.frame(A=log(1:6), B=LETTERS[rep(1:2,c(2,4))], C=1/(1:6), D=rep(letters[25:26],c(4,2)), Y=1:6) > m0 <- model.frame(data=d, Y ~ A:B) > formula(m0) Y ~ A + B > `attributes<-`(terms(m0), value=NULL) Y ~ A:B This is in part

Suppose I have the following function myFun <- function(formula, data){ f <- formula(formula) dat <- model.frame(f, data) dat } Applying it with this sample data yields a new dataframe: qqq <- data.frame(grade = c(3, NA, 3,4,5,5,4,3), score = rnorm(8), idVar = c(1:8)) dat <- myFun(score ~ grade, qqq) However, what I would like is for the resulting dataframe (dat) to include

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Hello all, I came across a behavior of R with environments that I'm not sure what is causing it. It involves changing variables that are found through using model.frame on a formula inside a function. I wonder if it's a "bug" or a "feature". And in either case, how it might be managed. Here is a simple example: # let's say we have an x and y variables: temp_x

I have a function that, among other things, runs a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~

R version: 1.7.1 OS: Red Hat Linux 7.2 Hi all, The formula object in model.frame() is not retrieved properly when model.frame() is called from within a function and the "subset" argument is supplied. foo <- function(formula,data,subset=NULL) { cat("\n*****Does formula[-3] == ~y ?**** TRUE *****\n") print(formula[-3] == ~y) cat("\n*****Result of model.frame()

Hello everyone, I am trying to fit the following model All X. variables are continuous, while the conditions are categoricals. model <- lm(X2

Full_Name: Sebastian Meyer Version: 2.10.0 OS: x86_64-pc-linux-gnu Submission from: (NULL) (77.135.124.96) When using model.frame (model.frame.default) on an intercept-only model and with na.action=na.fail SPECIFIED, i.e. data(iris) model.frame( ~ 1, data = iris, na.action = na.fail) one gets the following error: Error in complete.cases(object) : negative length vectors are not

I have a function in R that takes another function as argument: f <- function(g, ...) { #g is expected to be a function } I want to see if there is a way to implement "f" in C and calling it from R using ".C" interface. I know that I can use function pointers for my C implementation, but I imagine it's going to be nearly impossible to pass a function from R to C. Are

Dear Martin, Since no one else has picked up on this, I?ll take a crack at it: The proposal is to define the S3 class of model-frame objects as c(?model.frame?, ?data.frame?) (not the formal class of these objects, even though this feature was coincidentally introduced in S4). That?s unlikely to do harm, since model frames would still ?inherit? data.frame methods. It's possible that some

I've encounted a problem using expand.model.frame. To illustrate, consider the function > test <- function(model){ + expand.model.frame(model, "income") + } > The data frame Prestige (from the car library) has several variables, including prestige, income and education. I've attached this data frame and fit the following model >

Dear List I am looking for the recommended way to create a formula inside a function with an empty environment. I tried several versions (see below), and one of them seemed to work, but I dont understand why there is a difference between .GlobalEnv and the environment inside a function. I would be greatful for any reference or explanation or advice. Thanks Thomas #+BEGIN_SRC R :results output

Dear R-Helpers Given a function like foo <- function(data,var1,var2,var3) { f <- formula(paste(var1,'~',paste(var2,var3,sep='+'),sep='')) linmod <- lm(f) return(linmod) } By typing foo(mydata,'a','b','c') I get the result of the linear model a~b+c. How can I rewrite the function so that the formula can be updated inside the function,

I am trying to parallelize part of a C function that is called from R (via .C) using OpenMP's "parallel for" pragma. I get mixed results: some runs finish with no problem, but some lead to R crashing after issuing a long error message involving memory violations. I found this post, which describes how a .Call() function can be made to avoid crashing R by raising the stack limit:

Hi, How do I use a function inside a regression formula? When I tried to use min() function inside a regression formula, I ran into errors. Here are the commands to reproduce the error: mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv")) attach(mydata) names(mydata) mylm2 = lm(admit~gre+I(max(gpa,rank))) *Error in model.frame.default(formula = admit ~

Hello, How does one specify a formula to lm inside a function (with variable names not known in advance) and have the formula appear explicitly in the output? For example, f <- function(d) { in.model <- sample(c(0,1), ncol(d)-1, replace=T) current.model <- lm(paste(names(d)[1], "~", paste(names(d[2:ncol(d)])[which(in.model == 1)], collapse= "+")), data=d) #***

The general formula is y ~ a + b + c + ... There is this approach: formula <- reformulate(independent_vars, response = "y") model <- lm(formula, data = mydata) summary(model) It does not generate a string object, but the formula is still a string even if it is of class formula. Also, in this approach you only get + and if you want interactions or such you will need to code them