Stack Exchange Network

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [multiple-imputation]

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

1
vote
1answer
16 views

Including the outcome variable in multiple imputation

I'm trying to perform binary classification on a dataset with missing values. I used sklearn's iterative imputer to impute these values and I got pretty good results. However, I realized that I was ...
0
votes
1answer
33 views

Dealing with measurements falling outside of the theoretical range/boundaries of the data

Imagine I am measuring a bounded variable (with a maximum possible value above which the data doesn't make sense) and I end up with the following dataset with my measurements and measurement errors as ...
0
votes
0answers
8 views

R-square for regression using multiple imputation

I'm using multiple imputation to see how confidently we can apply the regression coefficients found for a sample to the whole population. Does it make sense to have an R-square for the model made ...
0
votes
0answers
6 views

Is there a way to perform two stage multiple imputation in R? Can this be done using MICE?

I came across this paper by Ofer Harel and Joseph L. Schafer on two stage multiple imputation and I couldn't find any information on how to apply two - stage multiple imputation in R. Does anyone ...
0
votes
0answers
12 views

Complementary log-log or log-log transformation when combining estimates from multiple imputation after cox regression

Can anyone give me an argument for or against using the complementary log-log transformation as opposed to the log-log transformation on survival estimates after cox regression in multiple imputation ...
4
votes
2answers
48 views

Best resources on imputation in R [duplicate]

This is my first question at stats. I need to impute some factors and numbers in my data set in R. What are my best options regarding packages and also a source to read more about the theory.
0
votes
0answers
19 views

Pooling descriptive statistics of categorical variables after multiple imputation

I am doing multiple imputation using the MICE package on R. My dataset consists of both categorical and continuous variables. After doing say 20 imputations, how should I go about pooling together the ...
0
votes
1answer
41 views

Multiple Imputation of Coarsened/Interval Data

I'd like to know how to impute non-normally distributed data from interval data, where the intervals differ across different individuals. The variable I am interested in is the number of months an ...
0
votes
0answers
16 views

How to run multiple imputation by predictive mean matching in multivariate missing data?

I'm running predictive mean matching for multivariate missing data for my final project, but how does the algorithm of predictive mean matching work on some variables if there are missing value in the ...
1
vote
0answers
18 views

Getting pooled F-values and p-values for multiply imputed data in SPSS?

When working with a dataset created via multiple imputation, SPSS pools some values but not others. For example, in multiple regression, I can get coefficients, t-tests for the coefficients, t-values ...
0
votes
0answers
29 views

Quality assessment for multiple imputation when joint distribution is not multivariate normal

I have a dataset with 100+ columns and 1000+ observations with significant amount (>60%) of data missing and fraction of missing data in individual columns varying from 10% to 90%. Data in none of the ...
0
votes
0answers
23 views

Multiple Imputation Vs Pool

I have simple question after running multiple imputation what the purpose of pooling? Suppose if i run a multiple imputation using method cart , after running this imputation technique i get very ...
0
votes
0answers
14 views

Calculating Barnard-Rubin degrees of freedom from Sums of Squares in an F-test

I am wondering if it is possible to manually calculate a pooled $p$-value from an omnibus $F$-test on several multiply-imputed datasets. It is possible with a regression, where there are unique ...
0
votes
0answers
20 views

How many missings are too many to impute data with the AMELIA II package?

I have a very large longitudinal dataset. I only want to impute 3 variables individually using the AMELIA package in R. The problem is that some individuals have a lot of missing values, so I want to ...
0
votes
0answers
8 views

Comparing log normal and propensity multiple imputation models using confidence intervals

I am imputing income data with information about individuals characteristics and I am comparing the following multiple imputation models: Regression method using logarithm of income. Propensity score ...
0
votes
0answers
12 views

Calculating standard error for each omnibus effect in an ANOVA

This is the output of a regression of a continuous outcome variable on two categorical predictors: group (placebo vs active) and ...
2
votes
1answer
105 views

Bootstrap, Rubin's rules, and uncertainty of sub-estimates?

Can someone provide an intuition for why, when using bootstrap to calculate the variability of an estimate (say a regression coefficient $\beta$) we don't need to incorporate the uncertainty of each ...
4
votes
2answers
173 views

Is it ever recommended to use mean/multiple imputation when using tree-based predictive models?

Everytime that I am making some predictive model and I have missing data I impute categorical variables with something like "UNKNOWN" and numerical variables with some absurd number that will never be ...
3
votes
1answer
268 views

How does mice::mice work?

The idea of multiple imputation seems to be based on the decomposition $$ p(\theta \mid y_{\text{obs}}) = \int p(\theta \mid y_{\text{obs}}, y_{\text{mis}})p( y_{\text{mis}} \mid y_{\text{obs}}) \text{...
0
votes
0answers
26 views

Diagnosing why MICE fails to impute data with either pmm or passive imputation

I'm having the worst time getting mice (version 3.3.0 under R 3.4.4 in debian stretch) to impute missing values in a particular dataset. This dataset describe a scale development effort with planned ...
0
votes
0answers
19 views

Degrees of freedom after multiple imputation

Goodmorning everyone, In my research project, I made use of multiple imputation to replace missing values.SPSS lets me then run most of the tests on the imputed data set and provides output for 5 ...
0
votes
1answer
25 views

How to handle uncertain counts in poisson test

I am curious about performing poisson test where I have uncertainty about my count. For example, I expect to see 15 bunny rabbits per hike. On a given hike, I positively identify 19 bunny rabbits, and ...
0
votes
0answers
28 views

aregImpute or mice for imputation of survival data

I would like to use multiple imputation to analyse associations between an exposure variable (exp) and different disease risks in a dataset with some missing data (...
0
votes
0answers
21 views

Large discrepancy between complete-cases and imputed data

I would like to conduct a survival analysis using a dataset with approximately 12,000 participants (1100 events). However, complete data are available for only 9500 participants (820 events). I have ...
0
votes
0answers
40 views

Random effects vs Rubin's rule to obtain pooled parameter estimates from multiply imputed datasets

I would appreciate any help to understand the statistical difference between using random effects and Rubin's rule to obtain pooled parameter estimates from multiply imputed datasets. For example, if ...
1
vote
0answers
15 views

Obtaining measures of effect for contingency tables with multiply imputed data

The epi.2by2 function in the epiR package computes a chi-square test and provides measures of effect when count data are ...
0
votes
1answer
228 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 ...
3
votes
0answers
40 views

Is Structurally Missing Data a subset of Missing at Random Data?

I'm quite familiar with MCAR, MAR and MNAR (NMAR) data but I have just come across a new (for me) term: Structurally Missing Data (SMD). According to this page, Structurally missing data is data ...
0
votes
0answers
66 views

Understanding the algorithm behind aregImpute in R::Hmisc

I am dealing with time series data with gaps. I need to capture dominant frequencies in the data, and so have to perform an FFT on the data. However, since FFT requires evenly sampled data; I need to ...
2
votes
0answers
83 views

Descriptive statistics (frequencies, counts, proportions) after multiple imputation

I recently ran a multiple imputation using the mice package in R to generated imputed datasets. I have no problems with running inferential statistics on the pooled data (logistic and Cox regressions) ...
0
votes
0answers
18 views

All items missing for various questionnaires

this is actually the first time I'm working on a big dataset and I really hope someone can give me some advice on how to handle missing data. I tried to find information regarding my problem but can't ...
0
votes
0answers
25 views

How to deal with the problem of censoring in tree-based machine learning?

Censoring occurs when the outcome of a unit is not observed, because the unit is lost to follow up in a longitudinal study. Let $Y_t$ be the survival time at $t$. Then a unit is censored at $t'$ if $...
0
votes
1answer
36 views

Trend line for TS and using smoothing with mice

I have a time Series data like ...
1
vote
1answer
26 views

Multiple Imputation of Mutually Dependent Data

I'm trying to construct summary variables for proportion of time spent in different employment statuses over an individual's working life (e.g. % of time spent unemployed between ages 18-21). My ...
2
votes
0answers
41 views

How to test multiple regression assumptions when multiple imputation has been used?

I used multiple imputation on SPSS to deal with missing data in my study. I then carried out multiple regression from the imputed and original data-sets, using a split-file. I now have output for each ...
0
votes
1answer
96 views

Regression with missing Y’s

I use publicly available EU-Silc data to estimate the market price of social dwellings (subsidized dwellings). However my X variables are almost perfectly available,...
3
votes
1answer
124 views

What should I do after multiple imputation in the data?

I have a data set with missing observations. I used VIM package in R for imputation. After imputation, I will try to run a ...
3
votes
0answers
85 views

How to use MICE in R to fill missing values in test set?

It seems that MICE does not have a "predict" function which allows to use a fitted mids object to predict the missing values in test data set. I can certainly ...
0
votes
1answer
44 views

Imputing nested time series data with R

Does anyone know what is the superior algorithm to impute data in time series? I had strong dropouts over time because it was free to participants how many times to participate in my study (otherwise ...
0
votes
1answer
49 views

Proper Imputation and bias-correction on degrading signal with Kalman Filtering?

A signal degrades in its quality. Some signals are far more robust to degradation while others are not. We will simulate degradation by randomly removing values from a function and then applying ...
2
votes
1answer
143 views

Why don't people impute missing exposure data in database studies?

Investigators doing studies in large databases (e.g., EMR) in which there is often a lot of missing data usually (in my experience) want to exclude all subjects missing the exposure or outcome of ...
-1
votes
0answers
10 views

Multiple Imputation on multi-site data

Suppose I would like to assess the relationship between Y and X (i.e. Y|X) on data collected from several different sites (i.e. 5) with one covariate Z and multiple auxiliary covariates which may ...
1
vote
0answers
25 views

How do I impute clustered data that is not time-series data?

The goal of my research is to understand whether MRI imaging characteristics can predict tumor pathology. The data consists of resected tumor samples, with multiple samples per patient. On the MRI, we ...
1
vote
0answers
22 views

Choosing Among Multiply Imputed Datasets

I am using multiple imputation to estimate treatment effects in a dataset that contains missing data. In some of my imputed datasets, the algorithm used in the analysis fails to converge; it's not ...
1
vote
0answers
49 views

When to use Multiple Imputation over Maximum Likelihood for Missing Data and vice-versa?

I've seen these being called the best techniques for dealing with missing data. But I'm wondering when to use one over the other and why? Edit: Why is this getting downvotes? I'm legitimately ...
0
votes
0answers
21 views

Imputing missing data with MICE where each observation has different levels

I have a set of observations that each consists of different levels. For example, I ask a $P$ individuals $N$ questions, each question with a possible $k_n$ discrete responses. This produces a table ...
1
vote
1answer
49 views

Per protocol or Imputation when missing is small (<5%)

if ~2% of my data is missing on the outcome (continuous scale), out of a total of 200, two in control and three in intervention group, do I need to impute? Or can I make a case that with such small ...
1
vote
1answer
304 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 ...
0
votes
0answers
35 views

Multiple imputation with composite variables

In my analyses, I often use urinary concentrations as measure of exposure to various compounds. As these are generally spot urines, they are 'adjusted' for dilution using urinary creatinine ...
0
votes
0answers
69 views

Procedure for identifying predictors via LASSO on imputed data

First time poster here, hello Everyone! I've tried to make my question as concise as possible. I am looking to identify the best set of predictors for rehospitalisation in my data set of 60 ...