similar to: Partial R2 values calculated using different packages

Building a package using R.2.8.0 on Windows XP gives problems when compiling C code: Trying to run 'rcmd build' on R.2.8.0 on Windows XP gives ... ---------- Making package gRbase ------------ adding build stamp to DESCRIPTION making DLL ... gcc -std=gnu99 -Ic:/Programs/R/current/include ------------------------------------ gcc.exe: no input files make[3]: *** [setops.o] Error 1

Dear all, My gRbase package imports functionality from the bioconductor packages graph, Rgraphviz and RBGL. To make installation of gRbase easy, I would like to have these bioconductor packages installed in connection with installation of gRbase, but to do so the user must use setRepositories() to make sure that R also installs packages from bioconductor. Having to call setRepositories causes

I am running FileMaker 6.0 via Wine on Ubuntu 6.10. I have a file/ table/whatever it is called Case.fp5 and another table called Actions.fp5. Case is a one to many relationship to Actions, related via the CaseID. Date Closed is calculated in Case: If Last(Actions::Action Type) = "Close" then Date Closed = Last(Actions::Date Created) (something like that since I can't actually see

I need to force a coxph() function in R to use a pre-calculated set of beta coefficients of a gene signature consisting of xx genes and the gene expression is also provided of those xx genes. If I try to use "coxph()" function in R using just the gene expression data alone, the beta coefficients and coxph$linear.predictors will change and I need to use the pre-calcuated linear predictor

Dear all, I run R CMD build --compact-vignettes="both" gRbase and/or R CMD build --compact-vignettes="gs+qpdf" gRbase and in the log from r-devel (on winbuilder) I get * checking sizes of PDF files under 'inst/doc' ... WARNING 'gs+qpdf' made some significant size reductions: compacted 'gRbase-arrays.pdf' from 421Kb to 115Kb consider

Dear all, I run R CMD build --compact-vignettes="both" gRbase and/or R CMD build --compact-vignettes="gs+qpdf" gRbase and in the log from r-devel (on winbuilder) I get * checking sizes of PDF files under 'inst/doc' ... WARNING 'gs+qpdf' made some significant size reductions: compacted 'gRbase-arrays.pdf' from 421Kb to 115Kb consider

?did this inside R; not ideal but worked. ________________________________ From: R-devel <r-devel-bounces at r-project.org> on behalf of S?ren H?jsgaard <sorenh at math.aau.dk> Sent: 08 July 2018 15:46 To: r-devel at r-project.org Subject: Re: [Rd] [R] consider running tools::compactPDF(gs_quality = "ebook") Dear all, I run R CMD build --compact-vignettes="both"

Dear all, I run R CMD build --compact-vignettes="both" gRbase and/or R CMD build --compact-vignettes="gs+qpdf" gRbase and in the log from r-devel (on winbuilder) I get * checking sizes of PDF files under 'inst/doc' ... WARNING 'gs+qpdf' made some significant size reductions: compacted 'gRbase-arrays.pdf' from 421Kb to 115Kb consider

In linear regression, regression weights of x1 on Y given x2 and x3 should be mathematically identical to the semipartial correlations between x1 and Y, given x2 and x3. However, I do not obtain identical results, so apparently I'm doing something wrong in R. Data preparation: data<-read.csv("file.csv", head=T) data <- subset(data, select=c(age,s1,s2,s3,s5,s10,WSAS01)) data

Hello Everyone, Trying to learn a little bit about data mining. I'm working on a text mining project that will attempt to predict whether cancer patients got a particular type of genetic testing. A subsequent stage then will be aimed at predicting what the results of that testing were. ? I've used the tm package to prepare my data and am planning to use rattle to do the actual data

Hi, in Design package, a plot of survival probability vs. a covariate can be generated by survplot() on a cph object using the folliowing code: n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('male','female'), n, TRUE)) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <-

I have been working on fitting Cox model for prediction by using rms package. I want to measure model's calibartion and discrimination. Discrimination was measured by using validate() in rms, Dxy can be transferred to Harrell's c index. But in this way, I cannot get 95%CI of c index. How can I do this in R? And by the way, what value should be in c index to present the model's well?

Hello, I am using Dr. Harrell's rms package to make a nomogram. I was able to make a beautiful one. However, I want to change 5-year survival probability to 5-year failure probability. I couldn?t get hazard rate from Hazard(f1) because I used cph for the model. Here is my code: library(rms) f1 <- cph(Surv(retime,dfs) ~ age+her2+t_stage+n_stage+er+grade+cytcyt+Cyt_PCDK2 , data=data11,

I am sure that I do not have the answer to your question - but I would like to add to your views regarding the S4-issue. I've been involved in the gRbase package for graphical modelling in R. The first version was implemented in S4 but now we have made a roll-back to using S3 methods. While, I can see that S3 is not really comprehensive enough for 'proper' object oriented

Dear R users: In Prof. Harrell's library rms, calibrate.rms plot the Bias-corrected Probability and Apparent Probability. The latter one can be retrieved from class calibrate.default. But how to retrieve the former one. BW *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

Hello, I'm working on a Cox Proportional Hazards model for a cancer data set that has missing values for the categorical variable "Grade" in less than 10% of the observations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using

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,

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Dear all, I'm writing my manuscript to publish after analysis my final data with ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of my data (2 categirical factors with 2 levels: Quantity & Quality; 2 dependent var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here Irid.area) are significantly influenced by the covariate (standard length, SL), I

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,