similar to: Trouble figuring messages from rcspline.plot

http://r.789695.n4.nabble.com/file/n3058505/file.csv file.csv Hi, Rusers I have a problem in making a rcspline.plot with a Hmisc package. My data is in the upload attachment. My programme as follows: library(Hmisc) A<-read.csv("file.csv",header=TRUE) attach(A)

Dear Dr ; I am a PhD student at Epidemiology department of National University of Singapore. I used R command (rcspline.plot) for plotting restricted cubic spline ??? the model is based on Cox. I managed to get a plot without adjustment for other covariates, but I have a problem regarding to adjusting the confounders. I applied below command to generate the matrix for

Dear all, As I am new to the R community - although eager to advance- I would like to pose a question to the community. I have an SPSS file which I have imported it in R (with the read.spss command) which conists of scale (continuous) variable "adiponectin" and the corresponding categorical value "death" (0=No, 1=Yes). In all there are 60 observations (among which

Hi, I'm trying to get the 'xts' library from CRAN packaged for Fedora 12, and one of its dependencies is 'Hmisc'. I have created a spec file for that using R2spec, but building it fails with something in building the manual pages: Warning: ./man/escapeRegex.Rd:22: unknown macro '\backslash' [similar warnings snipped] Warning: newline within quoted string at

Hello, All: I am unable to manually replicate predict.lm, specifically comparing se.fit with (fit[,3]-fit[,2]): I think their ratio should be 2*qnorm((1-level)/2), and that's not what I'm getting. Consider the following slight modification of the first example in help('predict.lm'): set.seed(1) x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <-

Hi all, I have a vector of proportions (post_op_prw) such that >summary(amb$post_op_prw) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 0.0000 0.0000 0.0000 0.3985 0.9134 0.9962 1.0000 > summary(cut2(amb$post_op_prw,0.0001)) [0.0000,0.0001) [0.0001,0.9962] NA's 1904 1672 1

Dear all, I wanted to plot the mean and standard error on the regression equation (instead of individual data points) in the following code, but I could not find the right code in the help files. Could someone please show how to do this. Thank you very much. temp <- c(16,16,16,16,16, 20,20,20,20,20, 24,24,24,24,24, 28,28,28,28,28, 32,32,32,32,32) dev1hr <- c(36.2, 34, 32.2, 36.4, 36,

Hi, I've lost my mind on it... I have to scatterplot two vectors, grouped by a third variable, with two different dimensions according to whether each cell line in the plot is sensitive or resistant to a given drug, and with a different color for each of 9 tissues of origin. Here's what I've done:

Dear list, I'm currently analyzing some count data using a hurdle model. I've used the rcspline.eval function in the Hmisc-library to contruct the spline terms for the regression model, and what I want in the end is the ability to compute coefficients and confidence intervals for different changes in the smooth function as well as plotting the smooth function along with the

1. When you don't know (or are not sure) what an object is, str() is your friend. 2. My guess is that `lidnames' is a character vector with names. 3. If genes.txt has only only column, you might as well use: mygenes <- scan("genes.txt", what="") which reads the data into mygenes as a character vector. Then your command should work. [read.table()

Hi I have measured the UV absorbance (abs) of 10 solutions of a substance at known concentrations (conc) and have used a linear model to plot a calibration graph with confidence limits. I now want to predict the concentration of solutions with UV absorbance results given in the new.abs data.frame, however predict.lm only appears to work for new "conc" variables not new "abs"

Hi folks, Using R Version 1.6.2 (2003-01-10) on SuSE Linux 7.2, I just installed Hmisc_1.5-3.tar.gz and Design_1.1-5.tar.gz These were taken from http://hesweb1.med.virginia.edu/biostat/s/library/r Checked the dependencies: Hmisc: grid, lattice, mva, acepack -- all already installed Design: Hmisc, survival -- survival already installed, so installed Hmisc first All seems to go

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

Please forgive me if you already received this. I had an e-mail sending glitch this morning. http://cran.r-project.org/src/contrib/checkSummary.html reported an error in Design.trans.Rd * checking Rd files ... ERROR Rd files with syntax errors: /var/mnt/hda3/R.check/r-devel/PKGS/Design/man/Design.trans.Rd: unterminated section 'alias' The .Rd file is attached. It begins

Windows 98 R : Copyright 2001, The R Development Core Team Version 1.2.1 (2001-01-15) Dear friends. How comes this works and produce a single prediction: x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <- data.frame(x = seq(-3, 3, 0.5)) predict(lm(y ~ x), new, se.fit = TRUE) pred.w.plim <- predict(lm(y ~ x), new, interval="confidence") new1 <- data.frame(x=3)

Hi, I am using the rcs() function in the Design library to model non-linearity that is not well characterized by an otherwise mechanistic function. I am able to make the model 'available' to others through the excellent nomogram() function and the set of tables that it can create. However, I would like to present the model in an 'expanded' format-- probably what rcspline.restate()

Hi I am having some problems running a covariate analysis with my colleage using R with the NonMem program we are using for a graduate school project. R and NonMem run fine without adding in the covariates, but the program is giving us a problem when the covariate analysis is added. We think the problem is with the R code to run the covariate data analysis. We have the control stream, R code

I would like to impute missing data in a set of correlated variables (columns of a matrix). It looks like transcan() from Hmisc is roughly what I want. It says, "transcan automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables." And, "By default, transcan imputes NAs with "best

A significant update to the Hmisc package is now available on CRAN for all platforms. Hmisc source is now on github at https://github.com/harrelfe/Hmisc and the full change log may be found at https://github.com/harrelfe/Hmisc/commits/master The most important updates are additions of new graphics functions for summarizing and displaying data with an aim of replacing tables. There is also

Dear R-users, I'm currently having trouble with the implementation of a groupedData object in the lme() function. Executing the following function > applyScalingSimp <- function(input.population) > { > ## GA is a time value > varInOrder <- c("GA","weight","grouping","sex") > modelVar <-