similar to: Arbitrary number of covariates in a formula

Hello, I would like to create a group variable that is based on the values of three variables: For example, > dat <- data.frame(A=c(1,1,1,1,1,2,2,2,2,2), B=c(1,1,1,5,5,5,9,9,9,9), C=c(1,1,1,1,1,2,2,7,7,7)) > dat A B C 1 1 1 1 2 1 1 1 3 1 1 1 4 1 5 1 5 1 5 1 6 2 5 2 7 2 9 2 8 2 9 7 9 2 9 7 10 2 9 7 All rows that that are

Hi all! I have a list of matrices and I want to multiply the ith element of the list with the ith element of a another vector. That is, > LL <- list(A=diag(3),B=diag(3),C=diag(3)) > vec <- 1:3 > for(i in 1:3) + { + LL[[i]] <- LL[[i]]*vec[i] + } > LL $A [,1] [,2] [,3] [1,] 1 0 0 [2,] 0 1 0 [3,] 0 0 1 $B [,1] [,2] [,3] [1,] 2 0

Hello! Is there a possibility in R to save data in pdf-format? I do not want to save a plot but some lines of simple text. Regards, Franco Mendolia

Hello! I want to create a messagebox whenever the user wants to destroy the window (e.g. <Alt-F4> or the 'x' in the right top corner) and ask if a modified file should be saved or not. If 'cancel' is chosen then nothing should happen and the windows still should be existing. This doesn't work. When I press cancel the window will be destroyed although. I also

Hi all, I would like to parallelize some R code and would like to use the 'foreach' package with a foreach loop. However, whenever I call a function from an enabled package outside of MASS, I get an error message that a number of the functions aren't recognized (even though the functions should be defined). For example: library(foreach) library(doSMP) library(survival) # Create the

Dear R-help, I am using R-3.3.2 on Windows 10. I teach on a course which has 4 computer practical sessions related to the development and validation of clinical prediction models. These are currently written for Stata and I am in the process of writing them for use in R too (as I far prefer R to Stata!) I notice that predictions made from a Cox model in Stata are based on un-centred variables,

Dear All, This is not strictly R related (though I would implement the solution in R; besides, being this list so helpful for these kinds of stats questions...). I got a "strange" request from a colleage. He has a bunch (approx. 25000) subjects that belong to one of 12 possible classes. In addition, there are 8 covariates (factors) that can take as values either "absence"

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Dear R users, I am trying to make this (3 by 10) matrix A --A---------------------------------------------------- 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.5 0.5 0 0 0 0 0 0 0 ------------------------------------------------------- from "mass.func" --mass.func------------------------------------------- > mass.func $`00` prop 5 1 $`10`

Hi, Using package gclus in R, I have created some graphs that show the trends within subgroups of data and correlations among 9 variables (v1-v9). Being interested for more details on these data I have produced also the var-covar matrices. Question: From a pair of two subsets of data (with 9 variables each, I have two var-covar matrices for each subgroup, that differ for a treatment on one

Hello! I want to use the Icens package for analyzing interval-censored data. This code from the manual gives me what I want. library(Icens) data(cosmesis) csub1 <- subset(cosmesis, subset=Trt==0, select=c(L,R)) e1 <- VEM(csub1) plot(e1) However, I would like to change the color of the shading from green to something less green, say gray. Any ideas how I could do that? I looked at par, but

Hello, I finally run my measurement model in sem - successfully. Now, I am trying to print out the path diagram that is based on the results - but for some reason it's not working. Below is my script - but the problem is probably in my very last line: # ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31 library(sem) # Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation matrix

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

Dear expeRts, I have a data.frame with certain covariate combinations ('group' and 'year') and corresponding values: set.seed(1) x <- data.frame(group = c(rep("A", 4), rep("B", 3)), year = c(2001, 2003, 2004, 2005, 2003, 2004, 2005), value = rexp(7)) My goal is essentially to

> Date: Mon, 20 Feb 2006 16:43:55 -0600 > From: Aldi Kraja <aldi at wustl.edu> > > Hi, > Using package gclus in R, I have created some graphs that show the > trends within subgroups of data and correlations among 9 variables (v1-v9). > Being interested for more details on these data I have produced also the > var-covar matrices. > Question: From a pair of two

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

Hi all, I cant' wrap my head around an error from the coxph function (package survival). Here's an example: library(survival) n = 100; set.seed(1); time = rexp(n); event = sample(c(0,1), n, replace = TRUE) covar = data.frame(z = rnorm(n)); model = coxph(Surv(time, event)~ . , data = covar) R gives the following error: > model = coxph(Surv(time, event)~ . , data = covar) Error in

Dear R users. I'm trying to fit a generalised linear spatial mode using the geoRglm package. To do so, I'm preparing my data (geodata) as follow: geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* covar.col=16*) where covar.col is a factor variable (years in this case 90-91-92-93)). Then I run the model as follow: / model.5 = list(cov.pars=c(1,1),

In the first line, use the dist function, found in library mva, to get the distance between each pair of rows. From this calculate an incidence matrix for which element i,j is true if row i in dat equals row j in dat (and false elsewhere). In the second line, for each row calculate the indices of the matching rows and take the minimum of those as the key. incid <-