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

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SPSS, Multiple Imputation, analysis weight

I was wondering about the possibility to handle sampling weights and multiple imputation when I found the so called "analytic weight" button in the MI setup in SPSS. Now I am wondering: what is the ...
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18 views

Multiple imputation with high missing rate in covariate

I know that this question has been asked quite a lot - but I did want to see what people's opinions currently were on how applicable Multiple Imputation (MI) is to perform on a dataset with a high ...
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What happens if none of the predictors exeeds the threshold set by mincor in mice?

My imputation command includes a restriction regarding the minimum correlation: mincor=.3. In case none of the predictors correlates .3 or more with the target variable (the variable which contains ...
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34 views

multiple imputation and propensity scores

I have a dataset with 1300 observations and 30 variables. One of the variables has 10% missing data, another has 5% and a third has 3%. Seeing Propensity score matching after multiple imputation I ...
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56 views

how to check missing data is missing at random or not?

I have a survey data, in which there are some missing data (not answered questions). I threw away those where the whole page(s) questions were missed, but there are still some with unanswered ...
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20 views

How do I interpret the mice worm plot used for diagnostic?

I am using the mice package to impute missing data for a logistic model (relative to credit risk). In my case missing data are present only on covariates and not in the dependent variable (composed by ...
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54 views

Sensitivity Analysis for Missing Not at Random (MNAR) data

I currently have a dataset which contains variables with different degrees of missingingness. One of the key variables for my analysis has about 12% of the values Missing Not at Random (MNAR). From ...
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36 views

How to combine/pool binomial proportion confidence intervals after multiple imputation?

After I multiply imputed my dataset m times I wanted to calculate a binomial proportion confidence interval. I did that formerly using the Hmisc::binconf() function ...
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28 views

Generalization of degrees of freedom for t distribution for coefficients after multiple imputation

Donald Rubin has shown that regression coefficient estimates have fatter tails after multiple imputation and has provided a formula for the degrees of freedom to use as a t-distribution approximation ...
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31 views

What imputation methods can be used for missing not at random covariate values in a survival analysis?

I'm new to survival analysis and trying to understand how to use it properly. My dataset is a time series dataset where most dependent variable values are available, 2 dependent variable values are ...
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How to best plot and chart imputed data?

Multiple imputation is not yet a widespread technique, so there are not many guidelines yet on how to conduct and present reasearch using multiply imputed datasets. One aspect is graphics. How would ...
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15 views

Imputing skewed variable?

I have a data set with missing values in the IVs. I intend to use MI and in particular PMM for the numerical variables. One of them is very skewed and has many 0s so I can't log tranform it. My ...
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43 views

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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22 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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16 views

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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48 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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25 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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13 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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24 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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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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28 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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81 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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25 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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69 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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23 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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19 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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46 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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36 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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58 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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11 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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46 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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40 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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18 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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21 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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73 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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42 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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79 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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65 views

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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83 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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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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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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87 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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124 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 negative!)....
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52 views

compare different Imputation method by RMSE

My original dataset : ...
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20 views

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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74 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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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 ...