similar to: missing values and survival analysis

I am a new user of R for Windows, enthusiast about the many functions of the Design and Hmisc libraries. I combined the results of a Cox regression model after multiple imputation (of missing values in some covariates). Now I got my vector of coefficients (and of standard errors). My question is: How could I use directly that vector to run programs such as 'nomogram', 'calibrate',

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

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 all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

Dear R-help, I am trying to impute missing data for the first time using R. The norm package seems to work for me, but the missing values that it returns seem odd at times -- for example it returns negative values for a variable that should only be positive. Does this matter in data analysis, and/or is there a way to limit the imputed values to be within the minimum and maximum of the actual

Hi, I'm trying to estimate a (parametric) competing risks model in the context of duration (or survival, if you wish) analysis. That is, instead of studying the transition of subjects to "death", I wish to study the transitions to multiple *destinations* (which is different from studying multiple *durations*, or recurrent events). I am more interested in the hazard function rather

Hi all, I'm currently working with a dataset that has quite a few missing values and after some investigation I figured that multiple imputation is probably the best solution to handle the missing data in my case. I found several references to functions in S-Plus that perform multiple imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions? I searched the archives but was not

  I have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods. I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed

Hi, I have a survey dataset of about 20000 observations where for 2 factor variables I have about 200 missing values each. I want to impute these using 10 possibly explanatory variables which are a mixture of integers and factors. Since I was quite intrigued by the concept of rrp I wanted to use it but it takes ages and terminates with an error. First time it stopped complaining about too little

>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked. But when I use the functions like aregImpute the package is displaying coundn't find the function Where as in help.search it is giving that use of the function >

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

hi, i'm trying to learn how to use aregImpute by doing the examples provided with the package, and after installing Hmisc.1.6-1.zip (for Windows), and running the very first example on R 1.7.1, i get an error message warning me about "mace" (see below) and acepack. i found the acepack package, but its filename ends in tar.gz and i'm finding it difficult to open (because its

Hello All, Does anyone know where I can find information about how to do a power analysis for Cox regression with a time-varying covariate using R or some other readily available software? I've done some searching online but haven't found anything. Thanks, Paul

I tried your code, but it's giving the following error.. Error in match.fun(FUN) : argument "FUN" is missing, with no default

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, >

I am looking for a book that discusses the theory of multiple imputation (and other methods of dealing with missing data) and, just as importantly, how to implement these methods in R or S-Plus. Ideally, the book would have a structure similar to Faraway (Regression), Pinheiro&Bates (Mixed Effects) and Wood (GAMs) and would be very modern (i.e. published within the last couple of years).

Dear All, I'm having just a little terminology problem, relating the language used in the Hosmer and Lemeshow text on Applied Survival Analysis to that of the help that comes with the survival package. I am trying to back out the values for the baseline hazard, h_o(t_i), for each event time or observation time. Now survfit(fit)$surv gives me the value of the survival function, S(t_i|X_i,B),

Is there a good way in R to impute values which exist, but are less than the detection level for an assay? Thanks, Jonathan Williams OPTIMA Radcliffe Infirmary Woodstock Road OXFORD OX2 6HE Tel +1865 (2)24356

Dear all; I am trying to use HMISC imputation function to perform multiple imputations on my data and I keep on getting errors for the code given in the help files. When using "aregImpute" the error is; >f <- aregImpute(~y + x1 + x2 + x3, n.impute=100) Loading required package: acepack Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),

Hi, I am a long time SPSS user but new to R, so please bear with me if my questions seem to be too basic for you guys. I am trying to figure out how to analyze survey data using logistic regression with multiple imputation. I have a survey data of about 200,000 cases and I am trying to predict the odds ratio of a dependent variable using 6 categorical independent variables (dummy-coded).