similar to: problem with package rsm: running fit.mult.impute with cph

Sorry everybody, temporary dyslexia. Sent from my BlackBerry wireless smartphone

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")

Hello, I would like to know if there is a clever way to avoid the problem illustrated below within the xyplot function. x <- seq(1:10) y <- seq(1:10) pr1 <- xyplot(x ~ y) u <- seq(1:12) v <- seq(1:12) pr2 <- xyplot(u ~ v, col = "red", more = FALSE) prts <- list(pr1, pr2) for(i in prts) print(i, more = TRUE) I realize that one possibility is to

At 14:24 9/19/2004, JohnsoBS@vicksburg.navy.mil, wrote: >Personally, unless one has great need not to, I highly recommend upgrading >to samba3 to start with. The perfomance gains alone I found well worth it. >Plus if you plan to integrate into a network with 2k/XP/2K3, it will greatly >improve compatibility. OK. I tried to install samba 3.0.7,1. Got the same error:

Dear all, I used fit.mult.impute in Dr. Harrell's Design package to fit a cox ph regression model on five imputed datasets, where all missing predictors were filled by multiple imputation using R package Mice. Are there any functions able to do bootstrapping or cross-validation for the aggregated model? I tried function 'validate' and 'calibrate' in Design package, but

I have fitted a proportional odds model, but would like to compare it to a continuation ratio model. However, I am unable to fit the CR model _including_ imputated data. I guess my troubles start with settuping the data for the CR model. Any hint is appreciated! Christian library(Hmisc) library(rms) library(mice) ## simulating data (taken from rms::residuals.lrm) set.seed(1) n <- 400 age

Hello, I would like to use the aregImpute and fit.mult.impute to impute missing values for my dataset and then conduct logistic regression analyses on the data, taking into account that we imputed values. I have no problems imputing the values using aregImpute, but I am getting an error at the fit.mult.impute stage. Here is some sample code (I actually have more observations and variables to

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

Howdy, I am having a problem with a library compiled from some legacy fortran code. I can call the library, it runs as it should, returns a list, and gives a ">" prompt, but then locks up the R session. Functions typed in return nothing. ctrl-c results in a new prompt that is still locked up, and R overwhelms the processor. This happens on Mac, Windows, and Linux exactly the same. I

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

On Aug 1, 2012, at 1:53 PM, Jyotsna Verma <jverma at codeaurora.org> wrote: > > Currently, we rely on switch tables to transform between formats. However, > we would like to have a different mechanism to represent these relationships > instead of switch tables. I am thinking of modeling these relations in > HexagonInstrInfo.td file and use TableGen to generate a table with

Hi, I'm looking for some suggestions on a problem related to the Hexagon backend. Hexagon architecture allows instructions in various formats. For example, we have 3 variations of the add instruction as defined below: ADDrr : r1 = add(r2, r3) --> add 2 32-bit registers ADDrr_p : if(p0) r1 = add(r2, r3) --> predicated version of ADDrr instruction, executed when p0 is true ADDrr_np :

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

Dear r users, I wanted to perform contour figure from response surface model result. I typed this: > model = rsm (Index ~ SO(NV, SKLON, pH), bonita) > summary(model) > par(mfrow = c(2, 3)) > contour (model, ~NV+SKLON+pH) However, I got the error answer as follows: Error in lmobj$data[, nm] : object of type 'environment' is not subsettable Has someone any suggestions to

Hola compañeros de la lista. Los molesto con la siguiente duda. En un diseño central compuesto (CCD) con dos factores (V1 y V2) y una variable de respuesta (R), utilizando valores codificados (-1.4142, -1, 0, 1, 1.4182), al aplicar la orden: rsm.segundo.orden <- rsm(R ~ Bloque + SO(V1, V2), data = DATOS.Codificados) Obtengo el siguiente modelo: R = 103.92 -2.16

Hello R experts I am looking for: - advice - sample commands - pointers in MASS 4E - packages That help to produce 1D and 2D response plots from quadratic RSM linear regression models that have been produced in R. Let me explain a little what I mean. For 1D plots I mean the variable range (either scaled or actual) on the X axis and the Y axis showing the model prediction (preferably with

Hi Everyone, After some more thoughts to the Jacob's suggestion of using multiclasses for Opcode mapping, this is what I have come up with. Please take a look at the design below and let me know if you have any suggestions/questions. I have tried to keep the design target independent so that other targets could benefit from it. 1) The idea is to add 3 new classes into

Hi (sorry I do this question again, but my computer is block and I lost all my e-mail) I will install samba 3.0.10 in solaris 9/10. I would like to know if this version of samba is multi task. Today I use the samba 2.2.2 in solaris 8 (server with 2 processor), bat the "sons process of samba" work only one processor (100%) and the other processor be empty (0%) [the "sons