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 - 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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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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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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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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9 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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28 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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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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49 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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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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37 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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57 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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R - basic multiple imputation with the mi package

Consider the following R code: ...
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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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20 views

Degrees of freedom for a t-test using multiple imputation

I ran a paired samples t-test using multiple imputation in SPSS, and I am unsure as to how to proceed in reporting the degrees of freedom from the pooled data. The original data had 43 df and now it ...
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72 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 ...
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1answer
189 views

Multiple imputation introduces negative values; dataset still valid?

After some detective work in my data sources, I realized the reasons for my previously reported 98% of missing data ratio. After implementing some data collection code fixes, the current missing data ...
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86 views

Error using mice() package in R for handling missing data [closed]

I am doing regression with a data with Y as target variable and 16 feature variables. I had two date feature variables which where as factor. I converted them to date format as shown below: ...
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Are pooled results from multiple imputation equivalent to a posterior mean?

I am fairly new to multiple imputation and trying to be sure I understand the approach. Say I have a data set with missing values, so I create 5 imputed data sets using multiple imputation by ...
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1answer
53 views

Is multiple imputation recommended to handle attrition?

I have PRE-POST data from a psychotherapy group intervention, including measures on anxiety, depression, QoL, coping, self-esteem, as well as demographic variables. I´m performing a t-test to examine ...
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18 views

Imputing values that depend on each other, such as percentiles or proportions

I have a data set with school level measures including test score percentiles. These percentiles are central to my analysis. For example, one measure is the test score of the 25th and 75th percentile ...
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69 views

Data imputation for meta analysis using mice package in R

I have a data-set with 32 effect size estimates- only 11 of which report a value for the continuous moderator of interest (the samples anxiety level). A complete case analysis (restricted to the 11 ...
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1answer
63 views

Pooling imputed, still not analysed datasets in MICE

I need to do a Multiple Imputation on a dataset with several missing values, and I need to do it with mice, because later I'll have to compare the results with those of imputations ran with other ...
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110 views

Negative imputed values

I am doing multiple imputation using chained equations in Stata to deal with item-missing data. One of the variables on which I did imputation was income. However after I did imputation, the imputed ...
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50 views

Multiple imputation to part of a dataset

I am working with a survey dataset which contains hundreds of variables. The item-missing data rate ranges from 0.2% to 10%. In order to retain study units with missing values and to maintain a ...
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multiple imputation for categorical data question

How do i design the use of Multiple Imputation based on Bayesian Inference when I am dealing with categorical data and my dataset does not contain complete prior observations at every combination ? ...
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45 views

multivariate dirichlet for multiple imputation

I dealing with 3 covariates {x1, x2, x3} all three are discrete and contain missing data. ...
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65 views

Measures of goodness-of-fit using multiply imputed data in Zelig

I am running a logistic regression model in R using multiply imputed data created using Amelia II, which I am then analyzing using Zelig. I would like to be able to report some measures of ...
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Multiple Imputation Variance

I have been reading up on multiple imputation, and I am interested in the between-imputation variance. However, not in the estimation of the parameters, but in the imputed values themselves. (1) ...
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Multiple imputation constraining for not applicable values

I want to do a multiple imputation of missing data using SPSS. I have a nominal (categorical) variable X with missing values codified as '9' and not applicable values codified as '8'. How can I ...
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73 views

Stepwise regression modeling using multiply imputed data sets

After multiply imputing data, it is natural to estimate regression models on the data. When multiple predictors are available, sometimes stepwise regression is used for model building (forward ...
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Data imputing in questionnaires

Does anyone have experience in dealing with data imputing in questionnaires? I have a table representing people's preferences in clothes. Some of values I already have but others are missing and I'd ...
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Multiple imputation with additive constraint

Say I have a dataset which includes three binary (dichotomous) variables: A, B, C, and C takes the value of A OR B. Many observations have missing A and B, and non-missing C. I guess that C should ...
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90 views

using cluster information in multiple imputation

i need to impute a dataset all categorical variables before doing analysis. I can just simply impute with mode of all data or a variable. I belief that better option will be to classify the subjects ...
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29 views

Constructing Counterfactuals and Estimating Prevalence

I'm a social scientist working on a research project where I try to estimate the prevalence of lying in responding to a certain sensitive question. The way I estimate it is to rely on a ...
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24 views

How to replace the missing data from AMELIA results

I have run a AMELIA imputation for a data set including missing data. I need to replace the missing point by the result of amelia(). But it content 5 group of imputed values. How can i choose the ...
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37 views

Multiple Imputations exclude “not applicable”

I'm wondering how I should do the following in SPSS. I have a dataset with missing data (at random). Some values are blank, because the question was "not applicable" to that person (eg. questions ...
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45 views

Include interaction in multiple imputation - r

I'm doing some imputation models of time until recurrence of tuberculosis (Cox model). This model should include an interaction between the time and the outcome of the previous episode of disease (0- ...
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17 views

Multiple Imputation: Before or after case exclusion?

I've done quite some reading on multiple imputation (MI), but can not seem to figure out the next question: I have a dataset with missing values, some rows have many missing values, others have less ...
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Best Multiple Imputation Method for Multiple Surveys Mixed Together, Presented Randomly?

I am working with a dataset containing data from 15 different surveys. The surveys were presented all as one battery to participants, with questions from all surveys essentially placed into a pool and ...
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129 views

How to: repeated measures anova and Friedman's test with multiple imputations for missing data

I have a study where patients were measured pre-operatively (time_pre), post-operatively (time_post) and long-term follow-up (time_lt). I have 60 subjects. Data consists of: 5 scale variables ...
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54 views

Validation - correctly compare and validated imputation models

I've seen a lot of interesting questions here about multiple imputation and also great answers that helped me a lot to impute my data. I've used Predictive Mean Matching, EMB and I would like to use ...
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using neighbor information in imputing data or find off-data (in R)

I have dataset with assumption that nearest neighbors are best predictors. Just a perfect example of two-way gradient visualized- Suppose we have case where few values are missing, we can easily ...
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How to summarize GAM model result from multiple imputation data in R

I am very new to R and not very experienced in statistics. I have this general question regarding applying Generalized Additive Models (GAM) in multiple imputation dataset. I used R package mice for ...
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How to perform imputation of values in very large number of data points?

I have a very large dataset and about 5% random values are missing. These variables are correlated with each other. The following example R dataset is just a toy example with dummy correlated data. ...
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50 views

Explaining MCMC sampling for a Multinomial Distribution and missing at random

I understand how MCMC works, and I understand how Multinomial Distribution works. I have a dataset some of the data are missing at random (MAR). I cannot connect these two dots together (MCMC -> ...
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233 views

Simultaneous imputation of multiple binary variables in R

I have a dataset with multiple correlated binary variables (0/1). Can anyone point me toward a solution for imputing completely random missing values based on information in the other variables? ...
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Age Period Cohort imputation

Suppose I have some survey data on which I'd like to conduct an analysis similar to this: Yang, Age-period-cohort analysis, Ch 7. Suppose further there are missing data I wish to handle via multiple ...
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What to do when complete cases vs. imputed cases and train vs. test disagree?

In a fairly complex survival analysis case with considerable missing data, I split the data set into training and testing and ran models for both complete cases and imputed cases (I used multiple ...
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Is the following procedure to measure the quality of an imputation ok?

I'd like to compare different kinds of imputation techniques, i.e. methods which allow to fill missing data fields in a data frame. For now, I'm only using the R package ...
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1answer
84 views

What is the current research on missing not at random data?

I have a data set with N ~ 5000 and about 1/2 missing on at least one important variable. Comparing the people with missing data to those with complete data it is fairly clear that the data are not ...