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 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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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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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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19 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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55 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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MICE R Polyreg implementation error? [migrated]

In MICE R mice.impute.polyreg.r (imputation for categorical response variables by the Bayesian polytomous regression model), it is mentioned that the method ...
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10 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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16 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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53 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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10 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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7 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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32 views

compare different Imputation method by RMSE

My original dataset : ...
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19 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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11 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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39 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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1answer
17 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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1answer
94 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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1answer
16 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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11 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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1answer
61 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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53 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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13 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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25 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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1answer
48 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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27 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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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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1answer
21 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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22 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 ...
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40 views

Multiple comparison in Multiple imputation

I am wondering if it is appropriate to use the term "multiple comparison problem" when applied to multiple imputation. I know that the multiple comparison problem arises when we have one set of data ...
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125 views

Uncertainty in random forest imputations from R missForest package

I am in the process of imputing missing values for my data set that contains approximately 20 variables and 3,000 observations. Most of the missing data values are contained in 2 of the variables (one ...
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11 views

How to display a relationship with restricted cubic splines after multiple imputation?

I aim to perform a Cox regression. My data set contains roughly 10 variables which I intend to include, for a total of 5000 patients, yielding 900 events. I want to present how a certain variable ...
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31 views

Appropriate methodologies for missing value imputation regarding continuous variables in R

I have a data frame in R of 8 continuous variables in the rows and 60 paired observations in the columns. I want to use this data frame in a subsequent analysis along with gene expression data ...
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27 views

Missing data - MI decreasing R-squared

I have quite a bit of missing data (7-10%), however this is due to individuals running out of time to complete the questionnaire and so it is missing at random (the order of the questions was random, ...
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31 views

filling missing data with other than mean values [duplicate]

What all options are available for filling missing data ? One obvious choice is the mean, but if the percentage of missing data is large, it will decrease the accuracy. So how do we deal with ...
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70 views

How to pool (average) hazard ratios?

Let's say the task did not allow me to use either the MICE package or RMS / HMISC to perform survival analysis on multiply imputed data. I had to impute 5 different data sets and calculate separate ...
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How to run Multiple Imputation with waitlist (2 time points) and treatment data (3 time points)

My data contains responses from treatment and wait list cases - so I have data for the treatment group at time 1,2 and 3, and data from the waitlist at time 1 and 2 only. How can I run multiple ...
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How do I distribute answers lacking geo information from a poll?

My data table looks like this: Region Answer1 Answer2 NaN 20 40 Region1 15 17 Region2 18 19 ... So is it possible to distribute answers for which region is not ...
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1answer
49 views

MICE: what does returned df mean?

In MICE, the object returned by pool() has a component, df, which is included in the summary of the pooled analysis. In my analysis I have about 55,000 cases, but the returned df is higher for most ...
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14 views

Checking for compatible conditional models

I am trying to implement MICE in R, using the package mice. I keep reading in papers that if the full conditionals we specify are compatible (they factor into a joint), then our inference is valid. ...
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26 views

Displaying data characteristics after multiple imputation

I have original data which I run a few commands on to get a feel for the data. For example, I have men and women, and in each group, I have the percent in each cancer type (eg brain, lung). In the ...
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59 views

Checking Cox model assumptions with multiple imputation

I have run multiple imputation using MICE. I would now like to run a Cox model on it (using with,pool), and make sure that is justified. That is, I need to make sure that the proportional hazards ...
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4answers
215 views

How do we decide on how to fill missing values in data?

I have a data set with NA values in many predictor variables. How do we impute the best values ? I have 302 variables in total. Out of them 236 belong to some abstract category, 37 to other, 9 to ...
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106 views

Imputation and linear regression analysis paradox

Missing values, especially in small datasets, can introduce biases into your model. There are several data imputation methods (MICE, Amelia II), which use EM algorithms to "fill in" the missing ...
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118 views

Combining adjusted survival estimates with multiple imputation

I've constructed a Cox PH model using multiple imputed datasets in SAS. Now I would like to estimate adjusted survival curves for each treatment group (main variable in the model). Is there a ...
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12 views

Multiple Imputation procedure for each outcome or all outcomes

What is the correct way to perform multiple imputation of covariates with regards to multiple outcomes, say 3? Is it better to impute covariates for each outcome (3 imputation models) or should I ...
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24 views

LRT with imputed data in R

I would like to estimate LRTs for nested models using imputed datasets and the survey package in R. I have this: ...
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32 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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1answer
39 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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33 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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535 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 ...