search for: mult

...here is that even the intercepts are the same. Lastly, these coefficients are the same as using clogit before adding the constant, but the intercept are different. I have attached my code for all this, maybe I messed it somewhere. Vumani ########################## xvar<-rnorm(100,0,1) data.mult<-rbinom(100,size=3,prob=(exp(0.5+0.4*xvar)/(1+exp(0.5+0.4*xvar)))) library(Nnet) mult.fit<-multinom(data.mult~xvar) coef(mult.fit) library(survival) choice<-c(ifelse(data.mult==0,1,0),ifelse(data.mult==1,1,0),ifelse(data.mult==2,1,0),ifelse(data.mult==3,1,0)) temp<-list(time=2-choice,...

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 I get a warning me...

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 this (if mult = 10): max my...

...1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 1, 2 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 2, 2 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 3, 2 [,1] [,2] [1,] 1 1 [2,] 1 1 This is the code I have written. t = c( 1 , 2, 2 ) r<-as.vector(table(t)) mult<-vector("list",length(unique(t))) for(i in 1:length(unique(t))){ for(k in 1:r[i]){ mult[[i]]<-array(1,dim=c(2,2,n[[i]][k],k)) } } mult The results are correct for the array [[1]]. However for the array [[2]] I get the following result: [[2]] , , 1, 1 [,1] [,2] [1,] 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 is no...

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 (by 30) to a long chain of "add" and "mult" instructions, then indvars follows loop-unroll and triggers S...

...nment, 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: 1. Give the function mult.fig an explicit local environment on definition: > environment(mult.fig) <- new.env() 2. Instead of using the superassignment operator, assign the restore list to the local environment of multi.fig (from within multi.fig): assign("old.par", ......, envir = environment(mult.fig)...

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? Tha...

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' argument in Design's fit.mult.impute, but it is not clear whether this would work, especially as fit.mult.impute seems only to work with t...

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 defaul...

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 + info.parents + info.teacher + info.sch.nurs...

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 actual...

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 5. # defaultLinear makes sense for our data. fmp <- fit.mult.impute(Y ~ X1 + X2 ... [for th...

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 funct...

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 enterData(int firstMatrix[][10], int secondMatrix[][10], int rowFirst, int columnFirst, int rowSecond, int columnSecond); void multiply...

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, b...

...ed to be able to spill all legal register classes. > > > Instead of scavenging for registers during pseudo-expansion, I would like > to make it possible to create new virtual registers during spilling. The > plan is to give TII::storeRegToStackSlot() permission to: > > - Insert multiple instructions at the provided iterator, and > > - Create new virtual registers, possibly from different register classes. > > I think that functionality would solve your problems, right? > > Yes, it sounds like it will solve the problem. Using the following example where live...

..., d, raw = T)' was fitted with type "nmatrix.1" but type "nmatrix.10" was supplied Does anyone have any ideas? Thanks, Harold ############################################################################ polyModelSelection <- function(x,y,maxd,add.dim=F) { dim.mult <- 0 if (add.dim) { dim.mult = 1 } bestD <- 1 bestError <- 1 loss <- 1 lm.models <- vector("list",maxd) for (d in 1:maxd) { lm.mod <- lm(y ~ 0 + poly(x,d,raw=T)) lm.models[[d]] <- lm.mod loss[d] <- modelEr...

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 $vreg_acc0) > > if the register can create new virtual registers $vreg_gpr4 and $vreg_gpr5, I think spilling can be avoided: > > > MULT $vreg_acc0,...

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, at 05:57 PM, Milan Bouchet-Valat wrote: > Hi! > > Some time ago, I replaced calls to library() with calls to > requireNamespace() in my p...