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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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I have been doing multiple imputation using MICE in R. When running, some variables in the console appeared to have asterisks next to their name, e.g., x1* x2* x3*. These asterisks started to appear ...
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I'm planning to undertake Network Modelling. However, I've been told that multiple imputation is a problem for Network Modelling. I have a lot of missing data. Any suggestions? Thanks!
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When testing multiple imputation algorithms in simulations, the bias of the examined estimates and the 95% coverage rate are often used as a quality metric. I understand that it is generally ...
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I have a dataset consisting of mobility data at the county-level for the US for about one year. So the number of observations is >1m. Apart from the county code, the date, and the mobility index, ...
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I am developing a Survey Analysis Application that works with Categorical Data and looking for a way to treat missing data fairly without losing the original data variance. Thus, I came up with a way ...
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I am trying to do multiple imputation on a data set like below. This is a dummy data set. There are no missing values for variables idnum and eye. But specific idnum should have same bmi and gender ...
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I'm wondering whether the pool() function from R's mice package correctly pools shared frailty models fitted on multiply imputed ...
schotti's user avatar
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I'm doing some imputation with the MICE package. The outcome variables I am using are zero-inflated, and in the absence of imputation, I would analyze them with a zero-inflated negative binomial ...
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I've understood that the kNN imputer, being a multivariate imputer, is "better" than univariate approaches like SimpleImputer in the sense that it takes multiple variables into account, ...
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I have a problem in which I'm looking to train an individual-level predictor for outcomes. I have information on individual-level covariates, but I don't have individual-level outcome variables. ...
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I am new to multiple imputation, so apologies if the question is naive. I have been exploring multiple imputation for use on a small data set where there are just a couple of data points. I would like ...
Null's user avatar
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I am very new to SPSS and the Statistics package. I have bi-weekly data on precipitation, air temperature, and several well water temperature (n~75). However, due to various reasons I have about 60-70%...
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I have a question about using multiple imputation. I have a dataset of approximately 16,000 people with over 15 years of follow-up. To address missing data and loss-to-follow-up, I'm using multiple ...
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I'm currently working with a data set from the medical domain, in which several parameters contain missing values (up to 40% of the data). The missing data is mostly caused by some hospitals not ...
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Are there any statistical procedures to pool standardized differences across multiple imputations after matching in R? I am aware of pooled estimates using mira but I just want the pooled standardized ...
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I am working with 10 multiple imputed datasets in SAS. I used the command PROC SURVEYLOGISTIC to fit a multivariate model with 6 predictors (3 dichotomous and 3 categorical) and their interaction ...
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I am trying to use MI to deal with missing values in my data set. If I understand correctly, MI is about simulating multiple data sets from a given initial data set and imputing possible values ...
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I'm working on a project with medical data where some of it is missing. We decided to impute the data using MICE and I found enough literature about how to choose $m$ (the number of imputations) and $...
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I can derive an MCMC algorithm for sampling from the posterior distribution of a parameter vector of interest, but only starting with a dataset that has no missing values. The actual dataset that I ...
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I have a longitudinal database that has more than 50% of the missing data of the MAR type. This amount of missing values was a surprise to me because I did not foresee this in the study design, and ...
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I have data with n = 80 and 10 predictors, and ran MI using MICE, followed by Variable Selection for Multiply Imputed Data using ‘miselect’ and finally have 4 non-zeroed coefficients. Since ...
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We are interested in using a substantive model compatible fully conditional specification (smcfcs) for multiple imputations of missing data. I believe this approach will lead to more unbiased ...
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I have a dataset that has 6 metabolites that were measured over time in two groups and using a mixed-effects linear model I would like to investigate the group differences for each metabolite. Since ...
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I have a dataframe with 6 columns, X1_t1, X2_t1, X3_t1, X1_t2, X2_t2, X3_t2, each of them have some missing values, which one of the following options is recommended if I want to examine the change of ...
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After I imputed two datasets, the two datasets are synthesized via statistical matching. With the synthesized data, I tried to pool estimates but got errors as below. ...
pltr's user avatar
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I'm using {caret} to impute missing data resulting from non-response to survey questions. All of these variables are defined as numeric, though most are dummies. ...
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I am using Amelia for multiple imputation, and have a very large list of variables to predict missingness in my exposure and outcome. However, I don't want to impute values in these predictors, I only ...
JRB's user avatar
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I've imputed variables to conduct a multiple regression analysis in SPSS but unfortunately SPSS doesn't provide certain pooled values, like R², standardized betas, etc. It looks like I have to conduct ...
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I have a longitudinal data set with missing values. I want to multiply impute (let us say $m$ = 20 times) the missing values in the wide format using the R-package mice. Thereafter, I would like to ...
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The mice package says, "Many diagnostic plots are implemented to inspect the quality of the imputations." What are those plots? Which are useful? How do you use them?
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I am using SPSS and I'm currently working with a datafile that contains multiple imputation (40 imputations) because of missing data. My question: Should I keep the original dataset (with missing ...
StateOn's user avatar
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Can anyone provide a reference to the theory that supports multivariate imputation with chained equations (MICE). I know Rubin has provided this for MI but MICE is a Gibbs sampler (I have never seen ...
Robert's user avatar
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I am using the ranger package in R to construct random forests on 10 imputed datasets after implementing MICE to fill in missing values. The ranger package provides not only a variable importance ...
Geoffrey Kahn's user avatar
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I am using the MICE package in R to impute my dataset to deal with missing values. I have missing values in both the response and the predictor variables. I am interested in following Paul T. Von ...
Amanda Goldberg's user avatar
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Based on this article, it is apparent that MICE works with the following logic: Fill missing values in every column apart from the column in question with either random or the mean of the given ...
Ryan Brown's user avatar
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I have conducted a multiple imputation in R with 5 imputations and 50 iterations using the function mice() from the corresponding mice package. Now that I have ...
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I am working with pregnancy data where I would like to impute a variable called LABOR PRESENTATION (nomical var. with 5 categories) from several other variables but then create a variable called ...
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I would like to apply the margins function to imputed data (I used mice), but it seems not possible. Do you know if a function exists that calculates marginal effects with imputed data? Thank you! ...
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I am new to the topic of SEM, so my question may seem a bit naive. I have about 60 observed variables that will be grouped in some latent variables to explain one outcome measure. One of the variables ...
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I am reading the an online book by Stef Van Buuren (link at bottom) regarding multiple imputation. In Section 3.2.1 he lists 4 different approaches to multiple imputation: Later on in Section 3.3 he ...
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I have two sets of data provided by the government - one spans the years 2016-2020, while the other only covers 2018-2020. Data from each dataset, for each year, are used to predict some outcome in ...
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When a data set contains a fraction of missing values - Which strategy should be chosen: first impute data before providing population discriptives, or give the population insights and cross-tabs ...
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I have an imputated data with several nonmissing and not-imputated variables. However, I realised when I use mivalext_lr() to obtain pooled AUC and 95% CI of my ...
YY Shi's user avatar
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I have 2 questions about Multiple imputation (MI) in the assessment of the prognostic performance of a test. This test acts as a predictor of a specific outcome, 3 years in the future. I have 26 % of ...
Gil77's user avatar
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I have data in which the number of missing values per cluster (in this case, zip-code), are proportional to the population. Does this indicate Missing at Random (MAR)? Third column with missing ...
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I am looking for a way to implement (country) clustered standard errors on a panel regression with individual fixed effects. That is, in plm() I want to define some ...
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I'm trying to calculate population mean, median, (etc, descriptive analysis) using multiple imputed data. However, the example that I found in sources were regression and then pool them into one ...
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I am performing data imputation on a large matrix [100000,34] of past measurements that contains missing values (rows are time-steps and columns are stations). So far I've used several machine-...
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I read few responses close to the question and was suggested in using t-test or chi-sq test. However, the pattern between variables can also involve more than 2 variables (e.g. data at x tend to be ...
user291195's user avatar
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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 ...
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