Multiple imputation refers to a set of stochastic imputation routines aimed at preserving the multivariate features of the data

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Multiple imputation simulation problems, bad imputations or bad simulation?

I'm relatively new to simulations, R, and multiple imputation. I am writing a simulation to better learn multiple imputation on my own. Here is my simulation of MCAR: ...
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14 views

Which Imputation method to use in MI

I'm making a predictive model. I'm thinking of using MI but not sure which imputation method to use. Is there some metrics or graphs one can compute on the data to see which method is best for which ...
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How can convergence (in distribution) be assessed in the context of multiple imputation by chained equations?

The MICE algorithm starts by randomly imputing the missing values in a dataset, and then proceeds to predict the missing values in each variable by modeling the relationship between the non-missing ...
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35 views

Cross-validation in combination with Multiple Imputation

I am working on a project where I want to cross-validate a Machine Learning algorithm (not logistic regression) on multiply imputed data. My question is, how can I use the training data to multiply ...
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15 views

When is it ok to MI Data with MNAR predictor without further instructions

I have a data set with predictors that are mostly MAR(supposedly), however I do also have one that is likely to be MNAR in the sense that the missing of that predictor depends on an unobserved ...
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12 views

Incorporate Prior Knowledge in mice similar to Amelia

I want to extend this question: Suppose we were to add another variable to the dataset in the linked question x4=c(0,0,0,0,1,1,1,0,1,1,NA,NA, 0,1,NA,0,1,NA,0,1) ...
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23 views

Multiple imputation in different software packages

I'm a grad student and my mentor asked me to write up some multiple regressions that had already been run. The model has around a dozen predictor variables (some dichotomous, some continuous) and 1 ...
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14 views

dividing a multiply imputed dataset into derivation and validation cohorts

R/statistics noob. Mac OSX 10.11, RStudio 0.99.842. I'm developing a clinical prediction tool as part of my PhD. I have missing data (23k cases, 24 variables, 70% of variables have at least one ...
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24 views

Unequal timepoints longitudinal data with missing values

I have a longitudinal data with unequal time points with missing values. I am looking for methods to impute the missing data. I looked at R packages NORM and AMELIA II and SAS procedures PROC MI. All ...
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44 views

How to improve running time for R MICE data imputation

My question in short: are there methods to improve on the running time of R MICE (data imputation)? I'm working with a data set (30 variables, 1.3 million rows) which contains (quite randomly) ...
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19 views

Multiple imputation: How many subjects per variable are needed?

I want to perform a multiple imputation via the mice package in R. My dataset looks as follows: 250 observations 60 variables variable type: numeric approximately 7-10% missings per variable ...
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41 views

How to use aregImpute “group” argument?

Can someone provide an example of using the "group" argument with aregImpute()? I see that group=NULL is the default, but my data include a few factor variables with levels with <5 observations. My ...
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22 views

Data dropout during the longitudinal experiment

I perform a longitudinal experiment and I don t know how to deal with the data dropouts. I use STATISTICA Statsoft but I`m not a professional neither in statistics nor in programming. So the question ...
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16 views

Multiple Imputation on date categories

I am doing multiple imputation using SPSS 22 and I have some issues that I'm not able to sort. I am working with a database of about 300,000 cases, of which approximately 10% of cases have missing ...
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29 views

How to choose which imputation to use to replace missing values

I am a psychologist (i.e. not a statistician or mathematician) and wish to replace missing values in my dataset. I have followed the steps here and they seem straightforward. But I really don't know ...
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33 views

Multiple data imputation to use in model

I have a followup question for multiple data imputation. So I been reading a bit about this method and I am still confused. So lets say I have m=5 datasets from multiple imputation. I extract the five ...
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40 views

Multiple imputation before or after creating variables?

This question seemed simple, but I cannot find the answer in books. I know that the assumptions of multiple imputation require that only the variables are imputed that will be used in the analysis. ...
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8 views

Multiple Imputations in Mplus: Asymptomatic Covariance

I ran multiple imputation on a model that created 30 imputations that I then used to analyze a multilevel model that included a three-way cross-level interaction. In order to interpret the interaction ...
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39 views

Multiple Imputation how to get one dataset out m=50?

So I am new to R and new to MI as well. Reading through "Flexible Imputation of Missing Data" and slowly becoming acquainted. I was going through a sample run of my data, worked through most of the ...
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30 views

Monte Carlo Simulations: Can I Use Real Data as Universe?

In Monte Carlo simulations, it is a commonly used procedure to generate synthetic data based on a large survey (e.g. a microcensus) first. These synthetic data is then used as universe/population for ...
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15 views

Multiple imputation and factor analyses

I have conducted a survey to collect my thesis data. The data naturally contains some missing values. I want to use multiple imputation but as I want to do a factor analysis this seems a little ...
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20 views

Multiple imputation in SPSS with PMM does not result in imputation of observed values

I'm running a multiple imputation in SPSS version 23 and have chosen PMM (Predicitive Mean Matching) as method, which should result in imputed values matching an observed value but it does not as ...
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59 views

how to compute a simple t-test on multiply-imputed survey data

This is a methods question and not a programming question but I have included R code that replicates what I am after up until the point of the problem. Say that I have a multiply-imputed survey data ...
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30 views

how to remove outliers prior to multiple imputation

A colleague came to me with the following problem. She has a complex, multivariate data set, in which respondents completed a number of measures with anywhere from 6 to 30 Likert type items for each ...
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47 views

What statistical models / approaches can I use to estimate missing hourly values?

My dataset consists of hourly values by weekday across several sites, where the sites vary by spatial location and by other common characteristics, such as type, or 'cafe,' 'restaurant,' and 'bar.' ...
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57 views

How to pool results from post hoc lsmeans analysis across multiple imputations with MICE

I have five imputed datasets created with MICE in R, and am running run some post hoc analyses using the lsmeans package. ...
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Variable importance in regression with large number of missing values

I have a dataset with multiple (approximately 20) categorical and ordinal predictors and a numerical outcome and I am trying to understand which and how each of these predictors affect the outcome ...
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76 views

Framework of multiple imputation

I read this paper about ("Multiple Imputation For Missing Data: What Is It And How Can I Use It?")(http://www.csos.jhu.edu/contact/staff/jwayman_pub/wayman_multimp_aera2003.pdf) Does any one have ...
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15 views

Imputation of predictors missing data for logistic modelling

I never used imputation of missing data and I would like to understand the effect of imputation in a specific scenario. Lets suppose that I have a dataset whit some predictors variable and one binary ...
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17 views

Lower Denomination Imputation

Before I start, I will point out that I am very new to imputing data and so any advice would be greatly appreciated. Apologies if there is an obvious answer that I am overlooking. I have a data set ...
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73 views

Change mean imputation in MICE package

MICE Steps The chained equation process can be broken down into six general steps: Step 1: A simple imputation, such as imputing the mean, is performed for every missing value in the dataset. These ...
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15 views

Overall estimate of multiple imputation (MI) vs. estimate of individual MI model

I generate the 95%CI for each of the MI model and the combined MI model. My question is how likely is it for the combined MI model to be non-significant, while most of or all of the individual MI ...
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84 views

Multiple Imputation and setting constraints

I'm trying to work my way through multiple imputation. If I don't define constraints, I get some negative numbers which don't make sense (as my data is based on reaction times, so can't be ...
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46 views

compare different Imputation method by RMSE

My original dataset : ...
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Imputation of a (weird) multivariate outcome

I am working with a dataset in which the outcome of interest is a vector of dates of particular events: (date_1,date_2,date_3,...,date_n). Some of these outcome vectors are completely missing, but I ...
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51 views

Using multiple imputation followed by repeated measures

I have missing data that I have done multiple imputation with. I want to then use the means or 'pooled' data from the five imputations to do a repeated measures ANOVA. It seems I can't do this in ...
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53 views

Using entropy to imputing missing value based on grey relational analysis and clustering

This algorithm contain three techniques : 1-fuzzy c-mean clustering 2-Grey relational theory 3-Entropy multiple imputation The frame work of this algorithm is as follows : My questions are ...
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26 views

Multiple imputation with firm R - options for including firm level fixed effects

I have a rather large panel data (ca. 600 000 obs in ten years and 75 000 firms) that has some missing observations that need to be imputed. I have thus far managed to impute the missing values with ...
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180 views

How to know which imputation is best for impute my dataset from Multiple imputation by using mice

I used mice package to impute the missing value as follows: ...
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24 views

Multiple imputation for dichotomous outcome: impute outcome or outcome components?

I'm using logistic regression to evaluate a potential association between exercise levels and whether or not a person develops dementia. I'm using multiple imputation to help fill in (MAR) missing ...
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17 views

Multiple imputation: manual calculation of new values

I need to impute some data but I find myself in the impossibility of using the model I will eventually use for my analysis as a model of imputation. I have reasons to believe that the var to be ...
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170 views

Impute missing data before or after feature selection?

Will the results of the feature selection be biased if I perform the feature selection before imputing missing data? I have a large data set of 20000 samples and 130 variables. The data sets ...
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64 views

Model multiple imputation with interaction terms

According to the documentation of the mice package, if we want to impute data when we're interested in interaction terms we need to use passive imputation. Which is ...
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18 views

Multiple imputation of lungitudal, time-unstructured data in SPSS

I have a longitudinal data set of home measurements of some disease-related physiological parameters that have been sampled throughout a period of 16 months. Of course there is some degree of missing ...
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28 views

why are residuals not independent of one another in a linear regression?

I am wondering why residuals not independent of one another in a linear regression
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87 views

Analysis of imputed datasets in Stata 14

I am relatively new to multiple imputation (and statistical analysis in general), so I apologize if my question seems naïve to more experienced users. I am dealing with a somewhat large dataset ...
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30 views

Pooled MI Variance

I am leaning about multiple imputations (MI), and I struggling to understand the pooled (or total) MI variance equation. From Rubin’s rules, the expression is: $$T=\hat{U} + \pmb{(1+1/m)} B$$. ...
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49 views

Multiple imputation in SPSS: Excessive iterations to converge?

I am doing multiple imputation in SPSS 23 to deal with missing data before running hierarchical regressions. I ran Ender's diagnostic macro which calculates Gelman and Rubin's PSR (potential scale ...
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29 views

Permuting the formula argument to Hmisc:aregImpute - how to evaluate?

I just ran across David Norris' comment about aregImpute and formula order in this post: Permuting the formula argument to Hmisc:aregImpute My question: how can you tell that the solution is fairly ...
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23 views

Response imputation with Amelia within model selection loop

I'm looking into the R package Amelia for multiple imputation of missing (response) values and I'm wondering how to integrate it within my cross-validation loop. Should I impute my whole dataset ...