similar to: question on formula and terms.formula()

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

R-list, 1. Given a formula (f) w variables referencing some data set (dat), is there any easier/faster way than this to get the names (in character form) of the variables on the RHS of '~' ? dat <- data.frame(x1 = x1 <- rnorm(100,0,1), x2 = x2 <- rnorm(100,0,1), y = x1 + x2 + rnorm(100,0,1)) f <- y ~ x1 + x2 mf <- model.frame(f, data=dat) mt <- attr(mf,

This arose when working on an addition to coxph, which has the features that the X matrix never has an intercept column, and we remove strata() terms before computing an X matrix. The surprise: when a terms object is subset the intercept attribute is turned back on. My lines 2 and 3 below were being executed just before a call to model.frame. The simple solution was of course to do them in the

A user sent me an example where coxph fails, and the root of the failure is a case where names(mf) is not equal to the term.labels attribute of the formula -- the latter has an extraneous newline. Here is an example that does not use the survival library. # first create a data set with many long names n <- 30? # number of rows for the dummy data set vname <- vector("character",

To whom it may concern, I happened to run the following R code just to check the layout of the output, but found that the code doesn't work the way I thought it should work. '' > lm(rnorm(100) ~ rnorm(100)) Call: lm(formula = rnorm(100) ~ rnorm(100)) Coefficients: (Intercept) -0.07966 Warning messages: 1: In model.matrix.default(mt, mf, contrasts) : the response appeared

Dear all, Using the tsls package, I noticed that regression lists longer than 64 character are getting truncated. Looking at the original source, tsls.formula <- function(model, instruments, data, subset, weights, na.action, contrasts=NULL){ if (missing(na.action)) na.action <- options()$na.action m <- match.call(expand.dots = FALSE) if (is.matrix(eval(m$data,

Dear all, I would like to suggest changing the pairs.formula command such that a command like pairs(GNP ~ . - Year - GNP.deflator, longley) would behave in a similar fashion as lm(GNP ~ . - Year - GNP.deflator, longley) i.e., make a pairwise scatterplot of GNP and all other variables in the (longley) dataframe except for Year and GNP.deflator. The above command, with the

Full_Name: Gabor Grothendieck Version: R version 1.9.1, 2004-08-03 OS: Windows XP Submission from: (NULL) (207.35.143.52) The following gives an error: > `a(b)` <- 1:4 > `c(d)` <- (1:4)^2 > lm(`a(b)` ~ `c(d)`) Error in model.matrix.default(mt, mf, contrasts) : model frame and formula mismatch in model.matrix() To fix it replace this line in model.matrix.default:

I posted this one as an R bug (https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=14767), but Prof. Ripley says I'm premature, and I should raise the question here. Here's the behavior I assert is a bug: The output from delete.response on a terms object alters the formula by removing the dependent variable. It removes the response from the "variables" attribute and it changes

I am trying to write a function that will run a linear model and plot the regression coeficients with their corresponding means. I am having two problems. I can get the plot with the function below, but I am having trouble labeling the points. function(y,x1,x2,x3,x4){ outlm<-lm(y~x1+x2+x3+x4) imp<-as.data.frame(outlm$coef[-1]) meanvec<-c(mean(x1),mean(x2),mean(x3),mean(x4))

I'm in the very initial stage of expanding the formula processing in my quantile regression function rq() to handle additive nonparametric components, say qss(x), or qss(x,z). I need some advice about strategy for formula processing. My initial foray was to use: terms(formula,specials="qss") and then modify the components of the resulting terms.object. But in changing formula

Dear expeRts, I came across the following error in using model.frame: # make a data.frame jet=data.frame(y=rnorm(10),x1=rnorm(10),x2=rnorm(10),rvar=rnorm(10)) # spec of formula mf1=y~x1+x2 # make the model.frame mf=model.frame(formula=mf1,data=jet,weights=rvar) Which gives the desired output: > mf y x1 x2 (weights) 1 0.8041254 0.1815366 0.4999551 1.4957814 2

Dear List The UKgas data is stored as an object of class 'ts'. I am trying to use "UKgas" in a formula as argument to a function. However, I do not know how to access the 'time series' information in the response (such as start() end() etc.). Here is a boiled down example. ssm <- function(formula, data = list(),subset=NULL) { cl <- match.call() if

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

Dear all, I have encountered some strange things when creating lm objects in R: model depends on the order of the terms specified in a formula. Let us consider the following simple example: > dat <- expand.grid(A = factor(c("a1", "a2")), + B = factor(paste("b", 1:4, sep="")), + rep = factor(1:2)) >

Hi All, I have a formula which has a factor with NAs in it. I wish to keep these in the model matrix, but the NA information is currently lost (the rows are kept but the NA gets converted to 0). Any ideas as to how I can keep NAs in? e.g. junk <-

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,

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

I've got a problem with the sparseby command (reshape library), and I have reached the peak of my R knowledge (it isn't really that high). I have a small data frame of 23 rows and 15 columns, here is a subset, the first four columns are factors and the rest are numeric (only one, line54 is provided). bearID YEAR Season SEX line54 5 1900 8 3 0 16.3923519 11 2270

I'm trying to evaluate a pre-built expression using eval(), e.g. dataset <- data.frame(y = runif(30, 50,100), x = gl(5, 6)) # one like this mf <- expression(model.frame(y~x)) eval(mf, dataset, parent.frame()) # rather than this eval(expression(model.frame(y~x)), dataset, parent.frame()) In the example above there is no problem, the problem comes when I try to do a similar thing