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

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How to fix dim(X) error in MICE (R)? [on hold]

I am trying to compute multiple imputation using the MICE package in R. I am working with a large scale study which includes 38 countries. For most countries I get reasonable results, but for some I ...
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16 views

How to properly perform multiple imputations when using cross-validation procedures

I am trying to understand the association of an exposure on an outcome. In a dataset of ~600, approximately half the population does not have a measured exposure. We have predicted their exposure ...
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17 views

How to handle data with 2 variables that have same missingness pattern?

I've not had much academic coursework on imputation, and I can't find anything online or in any texts regarding how one could handle missing data where there are two (or more, possibly?) variables ...
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20 views

forecasting imputed data

My data set consists of a 15 year time series of monthly water quality measurements (10 different measurements). The data set has ~30% missingness. I applied multiple imputation using the Amelia II ...
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4 views

Multiple imputation with mi package in R [migrated]

I would like to impute only cells with 99 or 999 values and to omite NA cells because they should not be imputed. I mean missing values are represented with 99 or 999 values. No value should be in NA ...
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1answer
83 views

Applying Rubin's rule for combining multiply imputed datasets

I am hoping to pool the results of a pretty basic set of analysis performed on a multiply imputed data (e.g. multiple regression, ANOVA). Multiple imputation and the analyses have been completed in ...
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19 views

Can quantile regression be used to pool multiply imputed count data?

I am using the mice package in R to impute missing data in small study. The study investigates the effect of a behavioral intervention on the frequency of a particular behavior, i.e., count data that ...
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1answer
24 views

Do I need to adjust the degrees of freedom returned by pool.compare() in MICE?

I am analyzing a multiply imputed dataset produced from the MICE package in R. To assess the overall significance of my linear model, I am using pool.compare() to compare my "full" model to an ...
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1answer
68 views

Multiple Imputations and Survival Analysis

I’m new to using multiple imputations and I would like an opinion on using it with survival analysis in R. I am using MICE on an entire dataset. For one of my independent variables I decided to ...
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10 views

multiple imputation - likelihood base

in nonignorable mechanism, selection model or pattern-mixture model is a multiple imputation method or a likelihood-base method? I am confused, i know in MI, missing data were filled in and then We ...
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35 views

How to generate a longitudinal binary data with missing at random (MAR)?

I want to test the performance of a multiple imputation algorithm for longitudinal binary data. Right now I have applied the algorithm on some real data sets and it turned out promising and then I ...
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13 views

Is there an online method to perform multiple imputation?

I have a dataset with a lot of missing data and I am using multiple imputations (with Amelia in R) before performing analysis on it. This dataset is used to train a classification model and to ...
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18 views

Bootstrapping and classification tables after multiple imputation

I have used the mice code to do my multiple imputation and it gave me gave me an output for my model as well as a new appended dataset using the "long" code. However, I tried to use this new bigger ...
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24 views

Multiple imputation in SAS for longitudinal data

I have a data set from a repeated measurement study comparing two groups with missing data due to lost-to-follow-up (~20%). I know how to apply multiple imputation method for cross-sectional data. ...
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1answer
42 views

Is maximum likelihood a form of data substitution? Or not?

I’m using maximum likelihood with missing data. In this case of missing data, is maximum likelihood a form of data substitution? I’m significantly more familiar with multiple imputation which I ...
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29 views

Pooled results missing from GENLINMIXED multilevel analysis of multiple imputation data

I am running a multilevel analysis with the GENLINMIXED command (Analyze>Mixed Models>Generalized Linear Models) on a set of multiple imputation data. The data file was generated by the Multiple ...
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1answer
51 views

Multiple Imputation methods

Suppose that a variable $Y_j$ has missing values. We can use regression to impute the data using the nonmissing observations: $$Y_j = \beta_0+\beta_{1}Y_{1}+\beta_{2}Y_{2} + \dots + \beta_{(j-1)} ...
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42 views

Formula for pooling variance components across imputations in mixed effects models?

I've had no luck with this question. I see here that someone has asked a very good question about combining confidence intervals. I haven't been able to find anything about even combining the ...
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2answers
155 views

Questions on multiple imputation with MICE for a multigroup-SEM-analysis? (including survey weights)

I am planning to do a multigroup SEM analysis. I gathered survey data and calculated a survey weight. Some of my variables have item nonresponse (mostly around 5% missings). I´ve decided to use ...
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23 views

pooling the results from a repeated measures using multiple imputation

How can I pool the results from a multiply imputed repeated measures analysis? SPSS does not the pooling, as it does for other analysis types. From a technical standpoint, it is not at all clear. ...
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24 views

Multiple Imputation on SPSS number of parameters issue

I have to do several analysis on scores from questionnaires but unfortunately, I have some missing data and I was told that doing multiple imputation to replace them was the best solution. So far I ...
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1answer
30 views

multiple imputation models containing categorical variables

I have been using multiple imputation to estimate missing values in a single continuous exposure variable. One of the variables used in my imputation model is a categorical variable (occupation), with ...
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32 views

Is regression dilution bias avoided by using time-dependent predictors?

Regression dilution bias implies that even random measurement errors may bias the results by pushing the regression slope towards zero. Cross-sectional studies are particularly susceptible to ...
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75 views

Multiple Imputation Using Amelia [duplicate]

I am using Amelia for multiple imputation, and I am satisfied with the imputed results. But I want to restrict the imputed variable to positive values. Is there a way that Amelia can handle it or ...
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23 views

Multiple Imputations: Do all variables need to be included, even if they are complete?

Let's say I have some variables with complete data, and some incomplete. In SPSS's MI add-on you can select some or all of the variables as the first step in the MI process (except subject ID). Does ...
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55 views

Multiple Imputation: If “Pooled” isn't “averaged” then what is it?

I used MI for a couple variables, and just want to be sure I know what SPSS did when it pooled test statistics. I've read that "pooled" is not "averaged"...so what is the calculation being done when ...
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77 views

Multiple imputation of conditional variables

I need to impute the missing values of a dataset of medical data in which several variables only make sense if another variable has a specific value. In the questionnaire the data come from they were ...
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32 views

Fit statistics and imputed data

I have a general question about pooling fit statistics when using multiple imputations. I am trying to pool the AIC, BIC, G-squared, or log likelihood across 10 imputed data sets. Is it possible to ...
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55 views

Multiply imputing data, but using just one of the imputed data sets

All, I have a question about what's practical when it comes to presenting results of multiply imputed data. I'm well-versed on the difference among MCAR/MAR/MNAR and approaches to imputing the data ...
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2answers
80 views

Something wrong with as.mids from mice package in R?

I am running some imputations using the mice package in R. During this process I need to use the as.mids function. However, it seems that as.mids change the values of my subsequent analysis - but I ...
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192 views

Smaller standard errors *after* multiple imputation?

I have 1771 observations, with 30% missing data for x1 (Yes:No), and no other missing values from 26 other variables (mix of continuous and factor). I am using quantile regression in R, with and ...
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1answer
56 views

multiple imputation of longitudinal, time-unstructured data

I have a longitudinal dataset of radiation exposures of an occupational cohort. A percentage of the exposure values are missing and I would like to multiply impute the missing values (it is one option ...
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134 views

Combining p-values from multiple imputed data sets

I have created a set of imputed data sets using 'mice'. The data is longitudinal in nature, with both a longitudinal outcome and survival outcome of interest, and time-varying predictors. I conducted ...
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36 views

Combine imputation and weighting in R

I'm dealing with stratified cluster sampling data. I would like to do multiple imputation first and then use weights in R. I looked at the survey-package for weighting and the mi- and mice-package for ...
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91 views

Data pre-processing: Multiple imputation

Q1: When using multiple imputation, a missing value is replaced by multiple estimated values at a time, thus creating multiple samples of the original one. If this is the case, should we run the ...
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61 views

Multiple Imputation and Matrix Completion

It is quite common that data sets will contain missing values in them. Suppose we want to try to fill in the missing values. For this we have techniques such as single/multiple imputation and matrix ...
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117 views

Multiple Imputation - Help Needed

These multiple imputation results relate to data I have previously described and shown here - Skewed Distributions for Logistic Regression Three variables I am using have missing data. Their names, ...
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2answers
70 views

Imputing using CFA for use in a Cox regression

I am using CFA (confirmatory factor analysis) to create a measurement model of social capital that is to be used in a Cox regression. Because of missing data I first impute the incomplete data by MICE ...
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109 views

Multiple imputation in SPSS: merge datasets?

I have two sets of data, measured from the same persons. The variables in the two sets are very different. There are some extreme values (occurring at random) in both sets I would like to handle using ...
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20 views

Multiple Imputation for Spatial Models

I'm trying to estimate various spatial models (SAR, SDM, SEM) but have missing data throughout my variables. The mice package in R gives a straightforward solution when none of the variables with a ...
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61 views

Non parametric test after multiple imputation

In advance, I'm not a statistician and I'm a SPSS user (although I also use STATA fairly well and R rarely). I'm having some trouble in making some nonparametric comparisons after Multiple ...
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86 views

R MICE imputation failing

I am really baffled about why my imputation is failing in R's Mice 2.22 package. I am attempting a very simple operation with the following data frame: ...
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40 views

Taking only a single data set from a multiple imputation?

If I use a package like R's mice to do multiple imputation, then only select the first of the resulting imputations and use that as a single imputation -- ignoring ...
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72 views

Linear Model or Time Series Model or ANOVA?

I have some data: column 1 = dates (daily data from say 1st Jan 2014 to 1 July 2014) column 2 = person (about 10 different people) column 3 = sales made (daily data from say 1st Jan 2014 to 1 ...
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64 views

Multiple imputation vs single imputation

We have missing data which we want to impute in order to provide an imputed value to some business users. However, we will not be providing any other information other than the point estimate. In ...
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54 views

Imputation of a binary variable by Bayesian logistic regression

In the book "Flexible Imputation of Missing Data" by Van Buuren, the following algorithm is presented I think I understand the algorithm as given, but I would like to know what is the "more ...
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192 views

Multiple Imputation and using Rubin's Rules on non-linear combination of estimates

When using multiple imputation, Rubin's rules can be used to pool the estimates. From my understanding though, the pooling should only be done on parameters that are asymptotically normal. What I ...
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626 views

Why is this multiple imputation low quality?

Consider the following R code: ...
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2answers
670 views

Is it possible to manually calculate standard deviation for a multiply-imputed survey variable based on the standard error (SE)?

I am analyzing a multiply-imputed complex sample survey data using Stata. For normally distributed numerical variables I want to report the mean and standard deviation. However, the Stata command for ...
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290 views

lmer with multiply imputed data

How can I get pooled random effects for lmer after multiple imputation? I am using mice to multiple impute a dataframe. And lme4 for a mixed model with random intercept and random slope. Pooling lmer ...