similar to: Model formula for ols function (rms package)

Hello, I have a couple of questions about the ols function in Frank Harrell's rms package. Is there any way to specify variables by their column number in the data frame rather than by the variable name? For example, library(rms) x1 <- rnorm(100, 0, 1) x2 <- rnorm(100, 0, 1) x3 <- rnorm(100, 0, 1) y <- x2 + x3 + rnorm(100, 0, 5) d <- data.frame(x1, x2, x3, y) rm(x1, x2, x3,

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

Using platform x86_64-pc-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu status major 2 minor 13.0 year 2011 month 04

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

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I have installed Hmisc and Design. When I use ols, I get the following error message: Error in if (!length(fname) || !any(fname == zname)) { : missing value where TRUE/FALSE needed The model that I am running is: > ecools <- ols(eco$exp ~ eco$age + eco$own + eco$inc + inc2, x=TRUE) I have tried several other combinations of arguments that take TRUE/ FALSE values, but no luck.

Dear All, I'm trying to use the ols function in the Design library (version 2.1.1) of R to estimate parameters of a linear model, and then use the contrast function in the same library to test various contrasts. As a simple example, suppose I have three factors: feature (3 levels), group (2 levels), and patient (3 levels). Patient is coded as a non-unique identifier and is

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

Hi, Running the following example from ?Predict() throws an error I have never seen before: set.seed(1) x1 <- runif(300) x2 <- runif(300) ddist <- datadist(x1,x2); options(datadist='ddist') y <- exp(x1+ x2 - 1 + rnorm(300)) f <- ols(log(y) ~ pol(x1,2) + x2) p1 <- Predict(f, x1=., conf.type='mean') Error in paste(nmc[i], "=", if (is.numeric(x))

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 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, I think I am beginning to understand what is involved in the so-called "Type-I, II, ..." ANOVAS (thanks to all the replies I got for yesterday's post). I have a question that will help me (and others?) understand it better (or remove a misunderstanding): I know that ANOVA is really a special case of regression where the predictor variable is categorical. I know that there

Hi everyone, I'm having some difficulty getting "simple effects" for the ols() function in the rms package. The example below illustrates my difficulty -- I'll be grateful for any help. #make up some data exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),

Hi all, I'm reposting this with a more appropriate subject. Do I need to define limits as the error message seems to suggest? If so, how? The error message, my code, the output and the first few lines of my data are all below. Thank you! "Error in Getlim(at, allow.null = TRUE, need.all = TRUE) : variable dmodel.df does not have limits defined in fit or with datadist" My code:

Hello, I have a question about the ?plot.predict? function in Frank Harrell's rms package. Do you know how to superpose in the same graph the prediction curve of ols and raw data points? Put most simply, I would like to combine these two graphs: > fit_linear <- ols (y4 ~ rcs(x2,c(5,10,15,20,60,80,90)), x=TRUE, y=TRUE) > p <- Predict(fit_linear,x2,conf.int=FALSE) > plot (p,

Hi, I read data from a file. I'm trying to understand how to use Design.rcs by using simple test data first. I use 1000 integer values (1,...,1000) for x (the predictor) with some noise (x+.02*x) and I set the response variable y=x. Then, I try rcs and ols as follows: m = ( sqrt(y1) ~ ( rcs(x1,3) ) ); #I tried without sqrt also f = ols(m, data=data_train.df); print(f); [I plot original

Dear R users, I tried to fit a cox proportional hazard model to get estimation of stratified survival probability. my R code is as follows: cph(Surv(time.sur, status.sur)~ strat(colon[,13])+colon[,18] +colon[,20]+colon[,9], surv=TRUE) Error in if (!length(fname) || !any(fname == zname)) { : missing value where TRUE/FALSE needed Here colon[,13] is the one that I want to stratify and the

Consider the code: x <- seq(0,1,0.2) y <- seq(0,1,0.01) cbind(match(y,x),y) which, surprisingly, doesn't show a match at 0.6! (It gives correct matches at 0, 0.2, 0.4, 0.8 and 1, though) In addition, x[4]==y[61] yields FALSE. (but x[5]==y[81], the one for 0.8, yields TRUE) Is this a consequence of machine error or something else? Could this be overcome? (It works correctly when

Dear all, this might be not only an R-question but also a statistical. When I do a logistic regression analysis (species distribution modeling) with function lrm (Design package) I get the follwoing error message: > tadl1<-lrm(triad~fd+dista+fd2+dista2+fd:dista+dista:geo2, x=T, y=T) Error in if (!length(fname) || !any(fname == zname)) { : missing value where TRUE/FALSE needed The

hi all - i have a function that needs to call glm() with a weights argument that includes a variable whose name comes from the caller. so instead of: fit <- glm(formula, poisson(), data, weights = 1-z, ...), i do something like this: fit <- do.call("glm", list(formula=formula, family=poisson(), data=data, weights = call("-", 1, as.name(zname)), ...))