similar to: fit.mult.impute() in Hmisc

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'

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

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

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 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 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

Greetings R world, I know some version of the this question has been asked before, but i need to save the output of a loop into a data frame to eventually be written to a postgres data base with dbWriteTable. Some background. I have developed classifications models to help identify problem accounts. The logic is this, if the model classifies the record as including variable X and it turns out

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 +

I have a dataset at a hospital level (as opposed to the patient level) that contains number of patients experiencing events (call this number y), and the number of patients eligible for such events (call this number n). I am trying to model logit(y/n) = XBeta. In SAS this can be done in PROC LOGISTIC or GENMOD with a model statement such as: model y/n = <predictors>;. Can this be done

Hi, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

Hello all, I wish to extract the terms from an rpart object. Specifically, I would like to be able to know what is the response variable (so I could do some manipulation on it). But in general, such a method for rpart will also need to handle a "." case (see fit2) Here are two simple examples: fit1 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) fit1$call fit2 <-

Greetings All, I am looking to write a parametrized Rscript that will accept a variable name(that also is the name of the flat file), transform the data into a data frame and preform various modeling on the structure and save the output and plot of the model. In this example i am using a rpart decision tree. The only problem i am having is integrating the parameter into the internal object name

Hello Everyone, I'm doing a Classification trees with categorical explanatory variables using library rpart and I would like to do a prediction for some data imputs. I don't know where's a function or how can I do it?. Is there someone can help ?? ¿. Here's the code that I'm using. library(rpart) fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) plot(fit)

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-

I am using the rpart package and seem to have trouble with data sets that have columns with no data. I look at the column data in R and all values are NA. When this occurs, I get nothing back from the rpart function. Is there a way to get the rpart package to ignore these columns, without knowing what columns are empty? I have tried the na.action=na.omit and na.action=na.exclude, but neither one

Hi R-helpers. I have the following problem: I would like to apply my function gain(df,X,A) to a list of arguments. df is a data frame X,A are the varibales od data frame. When I do > gain(kyphosis,"Kyphosis",c("Start","Number")) [1] "Start" "Number" I get the following error... Error in unique.default(x) : unique() applies only to vectors I

I am working with a non-parametic smoothing operation using a Generalized Additive Model. It is a bivariate data set. I know how to do the smooth, and out comes a nice smooth curve. Now I want to find the value of the smoothed curve for several values of x (the abscissa). This can be done (please correct me if I am wrong) by using the predict.gam function. You feed the predict.gam function a

Hi, Suppose i have generated an object using the following : fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) And when i print fit, i get the following : n= 81 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 81 17 absent (0.7901235 0.2098765) 2) Start>=8.5 62 6 absent (0.9032258 0.0967742) 4) Start>=14.5 29 0 absent (1.0000000

Dear all, I am reading the cch function source code. But I can not understand the following codes. Please help me. What's the fitter here? fitter <- get(method) out <- fitter(tenter = tenter, texit = texit, cc = cc, id = id, X = X, ntot = nn, robust = robust) [[alternative HTML version deleted]]

I run the following code: library(rpart) data(kyphosis) fit <- rpart(Kyphosis ~ ., data=kyphosis) plot(fit) text(fit, use.n=TRUE) The text labels represent the count of each class at the leaf node. Unfortunately, the numbers are rounded and in scientific notation rather than the exact number of examples sorted by that node in each class. The plot is supposed to look like