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Questions tagged [multiple-imputation]

Use this tag for questions involving multiple imputation, which refers to a set of stochastic imputation routines aimed at preserving the multivariate features of the data.

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22 votes
2 answers
18k views

Multiple imputation for outcome variables

I've got a dataset on agricultural trials. My response variable is a response ratio: log(treatment/control). I'm interested in what mediates the difference, so I'm running RE meta-regressions (...
generic_user's user avatar
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13 votes
5 answers
11k views

Multiple imputation for missing values

I would like to use imputation for replacing missing values in my data set under certain constraints. For example, I'd like the imputed variable x1 to be greater ...
rose's user avatar
  • 503
33 votes
3 answers
37k views

Imputation before or after splitting into train and test?

I have a data set with N ~ 5000 and about 1/2 missing on at least one important variable. The main analytic method will be Cox proportional hazards. I plan to use multiple imputation. I will also be ...
Peter Flom's user avatar
  • 124k
16 votes
2 answers
21k views

How to get pooled p-values on tests done in multiple imputed datasets?

Using Amelia in R, I obtained multiple imputed datasets. After that, I performed a repeated measures test in SPSS. Now, I want to pool test results. I know that I can use Rubin's rules (implemented ...
wisc88's user avatar
  • 315
10 votes
2 answers
21k 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 ...
user81715's user avatar
  • 159
23 votes
4 answers
13k views

Multiple imputation and model selection

Multiple imputation is fairly straightforward when you have an a priori linear model that you want to estimate. However, things seem to be a bit trickier when you actually want to do some model ...
D L Dahly's user avatar
  • 3,743
38 votes
1 answer
61k views

Multiple Imputation by Chained Equations (MICE) Explained

I have seen Multiple Imputation by Chained Equations (MICE) used as a missing data handling method. Is anyone able to provide a simple explanation of how MICE works?
Mike Tauber's user avatar
  • 1,067
8 votes
2 answers
3k 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 ...
Misha's user avatar
  • 1,323
5 votes
1 answer
4k views

Calculating pooled p-values manually

For reasons I won't go into I need to calculate parameter estimates from several imputed datasets. Based on this CV post about Rubin's rules I have determined how to manually calculate both the pooled ...
llewmills's user avatar
  • 2,161
20 votes
2 answers
24k views

How do the number of imputations & the maximum iterations affect accuracy in multiple imputation?

The help page for MICE defines the function as: ...
ross-validated's user avatar
12 votes
1 answer
46k views

"the leading minor of order 1 is not positive definite" error using 2l.norm in mice

I am having a problem using the 2l.norm method of multilevel imputation in mice. Unfortunately I cannot post a reproducible ...
Robert Long's user avatar
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12 votes
5 answers
11k views

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. <...
John 's user avatar
  • 2,268
12 votes
2 answers
3k views

How can I pool bootstrapped p-values across multiply imputed data sets?

I am concerned with the problem that I would like to bootstrap the p-value for an estimate of $\theta$ from multiply imputed (MI) data, but that it is unclear to me how to combine the p-values across ...
tomka's user avatar
  • 6,624
8 votes
1 answer
2k views

Combining LASSO coefficients across imputed datasets

I am using the LASSO with multiple imputed datasets and I am not sure how should I combine the coefficients obtained on the different imputed datasets. I could simply average them (as I would do had I ...
PCalderon's user avatar
7 votes
2 answers
3k views

Factor analysis on multiply imputed data

I have a data set with approximately 500 observations on eight key variables. There are a lot of missing data; only about 1/12 of the observations are complete. I am using ...
Peter Flom's user avatar
  • 124k
6 votes
1 answer
5k 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 ...
Nirvan Sengupta's user avatar
6 votes
3 answers
4k 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 ...
tomka's user avatar
  • 6,624
6 votes
2 answers
3k views

Imputation of missing response variables

I am doing multiple imputation on a database of observations on hospital patients. There is one observation of many covariates per patient. There are 2 binary outcome variables: Alive/Dead after 30 ...
Joe King's user avatar
  • 3,864
6 votes
2 answers
8k 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 ...
ziweiguan's user avatar
  • 519
5 votes
1 answer
3k views

Manipulating data for propensity score matching following multiple imputation with mice package

I've completed multiple imputation of my dataset for the first time using the mice package in R. I'm familiar with the procedure for using the ...
Aneesh's user avatar
  • 73
4 votes
1 answer
5k views

Is there an R function that performs LASSO regression on multiple imputed datasets and pools results together?

I have a dataset with 283 observation of 60 variables. My outcome variable is dichotomous (Diagnosis) and can be either of two diseases. I am comparing two types of diseases that often show much ...
Karima21's user avatar
2 votes
1 answer
971 views

Imputing missing values of one of the independent variable using dependent variable in addition to other independent variables?

I want to impute missing values of an independent variable say variable X1, the other independent variables are weakly related to X1. However, the dependent variable has strong relation with X1. I ...
Vikrant Arora's user avatar
2 votes
2 answers
1k views

Trouble with imputed data set

1) I had a dataset with missing data for baseline variables and outcome variables. Through multiple imputation in SPSS (10 imputations, 50 iterations, PMM for scale variables) I imputed the missing ...
Tom's user avatar
  • 21
2 votes
2 answers
309 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 ...
Linda E's user avatar
  • 21
1 vote
2 answers
355 views

Framework of multiple imputation

I read this paper about "Multiple Imputation For Missing Data: What Is It And How Can I Use It?" Does any one have information about step by step framework of multiple imputation in general ...
zhyan's user avatar
  • 335
0 votes
1 answer
171 views

Will MICE imputation accuracy be harmed by removing all duplicates?

I'm working with a very large amount of data, using the PMM method via R Mice. The data has a healthy number of continuous variables. I'm removing all the duplicates entries before starting the ...
Bobby O's user avatar
17 votes
1 answer
1k views

Pooling calibration plots after multiple imputation

I would like advice on pooling the calibration plots/statistics after multiple imputation. In the setting of developing statistical models in order to predict a future event (e.g. using data from ...
IWS's user avatar
  • 2,764
13 votes
2 answers
852 views

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 ...
rdorlearn's user avatar
  • 3,643
10 votes
1 answer
2k views

How to use restricted cubic splines with the R mice imputation package

I am wondering how to integrate restricted cubic splines (such as in the rms package) in the imputation models within R mice imputation package. Context: I am doing biomedical research and have ...
IWS's user avatar
  • 2,764
8 votes
1 answer
5k views

Rubin's rule from scratch for multiple imputations

I have multiple set of imputations generated from multiple instances of random forest (such that the predictors are all the variables except the one column to impute). I was referred to Rubin's rule ...
daddymaterial's user avatar
8 votes
2 answers
4k views

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 ...
user2816711's user avatar
6 votes
2 answers
5k views

How to impute an ordinal variable with MICE but prevent it from taking one value?

I have an ordinal variable, overall_tumor_grade, that can take on values of 1, 2, ...
JJM's user avatar
  • 155
6 votes
1 answer
3k views

Hot deck imputation: validity of double imputation and selection of deck variables for a regression

Background: I had a data set containing 212 observations with a lots of missing values. Most of the IVs and DVs are categorical (DVs are ordinal) in nature. There are 3 DVs and about 30 IVs. My ...
Blain Waan's user avatar
  • 3,605
5 votes
1 answer
3k views

Dealing with 'Don't Know' answers for a categorical outcome variable

I have a survey data with categorical outcome variable (yes, no, don't know) which reflects the acceptance of some situation by respondents. My concern is how to deal with Don't know answers, I really ...
Alia's user avatar
  • 51
5 votes
0 answers
635 views

Permuting the formula argument to Hmisc:aregImpute

In Frank Harrell's RMS Short Course today, I became aware that multiple imputation with Hmisc:aregImpute is not invariant to the ordering of terms in its formula ...
David C. Norris's user avatar
3 votes
1 answer
3k 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. ...
torwart's user avatar
  • 305
3 votes
2 answers
509 views

Draw survival curves of 2 groups after multiple imputation on covariates

I wonder how to draw survival curves (Kaplan-Meier) when there is no missing information on the survival variables but on the stratification covariate. For example, we know for all patients the follow-...
Flora Grappelli's user avatar
3 votes
1 answer
2k views

Predictive Mean Matching as Single Imputation?

Multiple imputation is known to be advantageous compared to single imputation. However, in practice there are often non-statistical reasons why multiple imputation can not be used (e.g. the data ...
Joachim Schork's user avatar
3 votes
1 answer
53 views

Bootstrap standard errors after multiple imputation

Following Rubin's rules for multiple imputation, I've calculated pooled estimates, group means in this case, with pooled standard errors. I checked this with a bootstrap and, assuming pooled standard ...
jay.sf's user avatar
  • 856
2 votes
1 answer
4k views

Analyzing multiply imupted data from Amelia in R: Why do results from zelig and mice differ?

Motivated by my answer to this question, I played around with analyzing mulitply imputed data from the Amelia package in R. As I have explained in my answer, the ...
crsh's user avatar
  • 378
2 votes
0 answers
91 views

Method for predicting price based on Geographical market, Product, and Company

I have a dataset which tracks the prices of 21 products, charged by 24 companies, in 150 different cities across the globe. However, the data set has missing values--that is, I might have Company X's ...
Sam's user avatar
  • 71
2 votes
1 answer
6k 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 ...
Sasha's user avatar
  • 23
2 votes
2 answers
5k views

Multiple imputation for missing data in longitudinal study

I have longitudinal data from N = 80 people who participated in 12 short monthly assessments. Around 40 participated in 10-12 of the interviews, the rest dropped out due to different reasons. Aim of ...
Elisa's user avatar
  • 33
2 votes
1 answer
906 views

Impute missing data for one variable in longitudinal data set?

I've got a longitudinal dataset with three variables: FIPS, eighteenplus, & year. FIPS is the id , and I'd like to impute data for eighteenplus. Eighteenplus data were only available for 1990 ...
Steve K's user avatar
  • 21
2 votes
1 answer
4k views

Multiple imputation: What has to be reported in a paper

I'm just wondering which results has to be reported in a paper if multiple imputation (MI) has been performed: the estimates (confidence intervals (CI), P-values) from the complete case (CC) or from ...
giordano's user avatar
  • 1,039
2 votes
3 answers
2k 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 ...
stats134711's user avatar
1 vote
1 answer
455 views

Interpreting this regression coefficient

Quick background: I am working on a political science project that involves analyzing the impact of different variables on the extent to which a candidate mentions other users when he or she tweets. ...
Lukas Pleva's user avatar
1 vote
2 answers
237 views

I cannot understand the formula for between-imputation variance in multiple imputation

Multiple Imputation (MI) for estimating desired a desired statistic but with missing data Following ^Shafer (page 4), and ^Austin et al. (section "Analyses in the M imputed data sets"), ...
travelingbones's user avatar
1 vote
1 answer
210 views

multiple imputation for prediction

I am doing some experimentation with multiple imputation (MI) for prediction, more specifically in the context of binary classification. I'm doing this because there is not much to be found with ...
cliffhanger-be's user avatar
1 vote
0 answers
346 views

Residual standard error for multiple imputed regression

How to calculate the standard deviation of the residuals for a pooled regression? (The idea, formula and/or R code are all welcomed) As far as I understand it is the standard deviation of the ...
Samuel Saari's user avatar