search for: markseeto

I'm trying to create a data frame in which some of the column names contain a dash "-". A simple example: d <- data.frame(x = c(0, 1)) d <- data.frame(d, y = c(0,1)) names(d)[2] <- "a.-5" d x a.-5 1 0 0 2 1 1 d <- data.frame(d, y = c(0,1)) d x a..5 y 1 0 0 0 2 1 1 1 names(d)[2] <- "a.-5" d x a.-5 y 1 0 0 0 2 1 1 1 Why

Hello, I'm fairly new to R and I can't work out how to produce a double inequality like (LaTeX) $0 \leq x \leq 1$ in the legend of a graph. If I try > legend(50, 0.1, legend = c(expression(0 <= x <= 1), c(2 <= x <= 3)), pch = c(1,1), col = c(2, 3)) then I get an error message "unexpected '<=' in ...". I've checked the help files for plotmath and

How can I extract the adjusted R^2 value from an ols object (using rms package)? library(rms) x <- rnorm(10) y <- x + rnorm(10) ols1 <- ols(y ~ x) Typing "ols1" displays adjusted R^2 among other things, but how can I assign it to a variable? I tried str(ols1) but couldn't see where to go from there. Thanks, Mark Seeto

Sorry for the non-R question, but how do I reply through the Nabble interface and have my reply emailed to the person I'm replying to (in case they don't use Nabble), with cc to the mailing list? If I choose the option to reply to the person by email, I don't see an option to cc to the mailing list. If I reply to the list, there's an option to email the post to someone, but I

Dear R-help, In the rms package, I have fitted an ols model with a variable represented as a restricted cubic spline, with the knot locations specified as a previously defined vector. When I save the model object and open it in another workspace which does not contain the vector of knot locations, I get an error message if I try to predict with that model. This also happens if only one workspace

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

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) #***

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y

Dear R help, I'm having some trouble with model formulas for the ols function in the rms package. I want to have two variables represented as restricted cubic splines, and also include an interaction as a product of linear terms, but I get an error message. library(rms) d <- data.frame(x1 = rnorm(50), x2 = rnorm(50), y = rnorm(50)) ols(y ~ rcs(x1,3) + rcs(x2,3) + x1*x2, data=d) Error in

This isn't a question about R, but I'm hoping someone will be willing to help. I've been looking at calibration plots in multiple regression (plotting observed response Y on the vertical axis versus predicted response [Y hat] on the horizontal axis). According to Frank Harrell's "Regression Modeling Strategies" book (pp. 61-63), when making such a plot on new data

Dear R-help, I recently upgraded from R 2.13.1 to R 2.14.2 and now get warning messages when plotting after using x11(). Example: > plot(rnorm(10)) ### no warnings > x11(); plot(rnorm(10)) Warning messages: 1: In axis(side = side, at = at, labels = labels, ...) : Font family not found in Windows font database 2: In axis(side = side, at = at, labels = labels, ...) : Font family not

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be