search for: multing

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

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

Dear R-users, I am working with R version 2.10.1. Say I have is a simple function like this: > my.fun <- function(x, mult) mult*sum(x) Now, I want to apply this function along with some other (say 'max') to a simple data.frame, like: > dat <- data.frame(x = 1:4, grp = c("a","a","b","b")) Ideally, the result would look something like

Dear all, I want to obtain the following result [[1]] , , 1, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 2, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 ................ , , 9, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 10, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 [[2]] , , 1, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 2, 1 [,1] [,2] [1,] 1

Hi! Some time ago, I replaced calls to library() with calls to requireNamespace() in my package logmult, in order to follow the new CRAN policies. But I just noticed it broke jackknife/bootstrap using several workers via package parallel. The reason is that I'm running model replicates on the workers, and the formula includes non-standard terms like Mult() which are provided by gnm. If gnm

Hi, I'm working on a slow-compile problem caused by SCEV (PR28830), and I need your suggestions on how to fix it. The loop below causes ScalarEvolution::getMulExpr to hang. int get(unsigned n) { unsigned i, j, mult = 1; for (i = 0; i < 1; i++) { for (j = 0; j < 30; j++) { mult *= n++; } } return mult; } the inner loop is completed unrolled

Martin, I have an objection in principle to anything that has the side-effect of clobbering something in the global environment, even something as innocuous looking as "old.par". I certainly object to putting something like that into a public library, however clever and useful the code might be. It just is not safe. As a quick way round this I suggest the following (R only) solution:

I used the multcomp package sometime back for doing multiple comparisons. I see that it has been updated and the methods like simint are no longer supported. When I run the program it prompts to me to use glht. How do I get the lower and upper conf int and the pValues using glht? Does anyone have an example? Thanks ../Murli [[alternative HTML version deleted]]

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'

Hi, I am attempting to set the confidence limits on a ls means plot as follows: mult<-glht(lm(effectModel, data=statdata, na.action = na.omit), linfct=mcp(mainEffect="Means")) meanPlot <- sub(".html", "meanplot.jpg", htmlFile) jpeg(meanPlot) plot(mult, main=NA, xlab=unlist(strsplit(Args[4],"~"))[1]) This produces 95% CIs by default but I would

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

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 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 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

Hi there, I have a problem using the function call tracing tools that is designed in llvm tools set. My aim is to record every function call that a program makes when it run. However, for whatever reason, a simple matrix multiply c program that I wrote cannot record all the function calls that happened when the program run. Here is the program: matrix.c #include <stdio.h> void

The enclosed zip has some test files for both LLVM and Clang, to go along with the last mult-alt patch I submitted to the list. You'll note that some of the code is commented out for various problems not directly related to the mult-alt stuff. Though I worked on some additional versions for platforms not included here in the LLVM tests, they have various problems with the lowering, but which

On Mon, Mar 25, 2013 at 2:07 PM, Jakob Stoklund Olesen <stoklund at 2pi.dk>wrote: > > On Mar 25, 2013, at 1:41 PM, Akira Hatanaka <ahatanak at gmail.com> wrote: > > > Hi Jakob, > > > > I believe Hal is trying to enable register scavenger to find two (or > more) registers that can be used as temporaries. > > > > One problem I see with this

Hi all, I recently wrote some code to store a number of polynomial models in a list and return this list. The model is returned fine, but when I make a subsequent call to predict(), I have an error. The code for polyModel selection is listed at the end of the email. Here is an example of the error: > poly.fit <- polyModelSelection(x,y,10,F) > for (d in 1:4) { + lm.model <-

On Mar 25, 2013, at 2:51 PM, Akira Hatanaka <ahatanak at gmail.com> wrote: > Yes, it sounds like it will solve the problem. > > Using the following example where live ranges of accumulators $vreg_acc0 and $vreg_acc1 conflict, > > MULT $vreg_acc0, $vreg_gpr0, $vreg_gpr1 > MULT $vreg_acc1, $vreg_gpr2, $vreg_gpr3 > > (consumer of $vreg_acc1) > (consumer of

Hi Milan, I expect I may be able to do something about the way the terms are evaluated, to ensure the evaluation is done in the gnm namespace (while still ensuring the variables can be found!). In the meantime, I think the following will work: Mult <- gnm::Mult f <- Freq ~ Eye + Hair + Mult(Eye, Hair) gnm::gnm(f, family=poisson, data=dat) Hope that helps, Heather On Wed, Apr 29, 2015,