Skip to main content

Questions tagged [mice]

MICE is an R package which implements Multivariate Imputation by Chained Equations using Fully Conditional Specification

Filter by
Sorted by
Tagged with
0 votes
0 answers
21 views

Factor Analysis with Multiple Imputation

I have a dataset with 49 Items of a questionnaire (ordinal; 0,1,2,3,4) with 5 diagnostic group of samples (n1: 50, n2: 25; n3:30, n4:23, n5:60). However, the dataset have missings like for 10-12 ...
dododabird's user avatar
3 votes
1 answer
22 views

How to conduct statistical analysis on multiply imputed data?

I have a data.frame named mydata with 6 columns: status, times, t1, t2, t3, t4. However, t1, t2, t3, and t4 contain missing values in this dataset. I intend to impute these missing values using the ...
dbcoffee's user avatar
  • 109
0 votes
0 answers
24 views

Multiple imputation mice pmm

I need to impute 20 yes/no variables in my dataset. In this dataset, 35% of the individuals have all 20 of these variables missing, while the remaining 65% have complete data for these variables. ...
Jop's user avatar
  • 11
1 vote
0 answers
27 views

Multiple Imputation for Missing Outcome Data

I have spent an extensive amount of time trying to understand the possible role of MICE in helping to "fill in" missing outcome data. I am relatively new to both multiple imputation and ...
R Har's user avatar
  • 11
1 vote
0 answers
24 views

How to access weighted group means and standard errors after using mice and WeightIt? [closed]

Some background: I imputed and weighted data from two groups of people, one group in a certain organization and one outside of it. Ultimately, I want to compare how they develop psychological trait X ...
MHx01's user avatar
  • 33
0 votes
0 answers
18 views

Reasons for failure of convergence in multiple imputation in wide format of (longitudinally) repeated Likert items

Our group is working on a dataset of approximately 1000 patients with 10 complete variables at the time of an acute disease, who subsequently completed a questionnaire of 5 Likert items (questions ...
torwart's user avatar
  • 305
1 vote
0 answers
18 views

mice: Default ridge parameter

Some methods in mice like pmm and norm apply a default ridge parameter of 1e-5 when the underlying data is nearly multi-...
jerome's user avatar
  • 31
0 votes
0 answers
36 views

Impact of correlation method using mice::quickpred()?

I use Multiple Imputation with MICE in R for dealing with missings in my survey data. I have two huge question marks in my head at the moment: As I have quite a lot of predictors (actually all ...
rNewbie's user avatar
  • 23
2 votes
1 answer
41 views

NA results after pooling estimates and coeff of mixed effects cox model from MICE imputation dataset

I need your help with my problem. So, after step of imputation missing data through MICE method, I got multiple imputed dataset. Then, I pooled the estimates and coefficients with mixed effect cox ...
Hoang-Giang Pham's user avatar
0 votes
0 answers
24 views

Cross-sectional variables in a time-series df with multiple imputation

I have a dataset with time-series and cross-sectional variables with a sample size of less than 100. I have three variables (var1, ...
jrcalabrese's user avatar
2 votes
1 answer
62 views

Visit and order sequence for multiple imputation in mice r

I want to use the R package MICE for Multiple Imputation and I have a question concerning the order of my dataset - regarding the order of my variables on the one hand and the order of my cases on the ...
rNewbie's user avatar
  • 23
1 vote
0 answers
29 views

After the mutiple imputation (MICE package in R), I still found that some variables are still with missing values. How to deal with it?

I have a relatively large data set with around 12000 samples with 550 variables. Originally, I have around 800 variables, I used a rule that if missing rate in each variable is larger than 80% I will ...
Steven Xu's user avatar
2 votes
1 answer
46 views

How to deal with missing longitudinal outcome and longitudinal covariate?

I have data with a continuous longitudinal outcome and one of the covariates is a categorical longitudinal variable. Both of them have missingness and were collected at the same time. So this means if ...
RJ Santia's user avatar
2 votes
0 answers
39 views

Multiple imputations generate values distributed differently from original dataset... does this mean my data is MNAR? Imputations still usable?

Quick question. I'm using the mice R package to impute missing data. I go by the presumption that the missing data are MAR, but I wouldn't be surprised if a few binary variables were MNAR. I followed ...
awastus's user avatar
  • 61
0 votes
0 answers
48 views

Missing data and missing not at random

My outcome is a child cognition scores (y). I will skip the exposure for now. My covariates are regular features like , ...
Science11's user avatar
  • 577
0 votes
2 answers
147 views

how to determine which imputed data to use in R

I have a dataset of almost 100 variables. These variables are Likert scale questions from 1 to 5 or 1 to 3. I converted the variables that I wanted to impute to categorical variables. Then I used this ...
yusefsoliman's user avatar
0 votes
0 answers
33 views

Multiple imputation - complicated variable selection in linked datasets

I attended the course on multiple imputations, where it was stated (to my understanding) that when imputing the missing data on some predictors we should use all variables that will be fitted in the ...
Milo's user avatar
  • 315
1 vote
1 answer
46 views

Is it statistically sound to use r mice to predict values instead of classic lm

I have access to a population of around 5 million people. I also have access to multiple social and demographic variables (age, gender, level of education...). I randomly sampled 10,000 people from ...
Charly Marie's user avatar
0 votes
0 answers
66 views

Congeniality between imputation model and analysis model

I have a question about congeniality between imputation model and analysis model. Suppose: Research goal: To estimate the prevalence of disease A among a population as well as among different ...
Guoqiang Zhang's user avatar
0 votes
0 answers
19 views

Convergence issues in imputation model

My glm model contains repeated measures of smoking (main exposure, binary:0,1), alcohol (binary 0,1), visit(binary1,0), education ("polr" with three levels 1,2,3), calorie (polr with 5 ...
Komal Mehta's user avatar
0 votes
0 answers
23 views

R summarize predicted value by groups from multiple datasets

I ran mice function and generated 3 mice imputed datasetes. I have a nonstandard model that is not supported by with function so. I used this example and ...
Science11's user avatar
  • 577
1 vote
0 answers
78 views

Multilevel multiple imputation in practice using R

I'm currently involved in a project where I want to address missing data using multiple imputation. I'm using healthcare data in a longitudinal setting with 16 time points, where observations are ...
actual-garlic's user avatar
1 vote
0 answers
47 views

MICE for longitudinal data - shall I include both id and time variable for imputing outcome

The missing variable in my longitudinal data set is the outcome variable. I try to use mice in R to do multiple imputation. The final model is mixed effect model fitted by lmer. The data set contains ...
Charlotte's user avatar
1 vote
0 answers
224 views

Data contains missing values after multiple imputation using mice without logged events (i.e., no evidence for constant values or multicollinearity) [closed]

After the multiple imputation (pmm method) using the mice package, there are still missing values in my dataset (although the number of missing values was reduced). I have checked that there was no ...
Dale's user avatar
  • 171
1 vote
1 answer
307 views

How to calculate a linear combination of regression coefficients after multiple imputation?

A method to handle missing data is with multivariate imputation by chained equations. During this process, we create many datasets (as many as you specify) with imputed values for the missing data. ...
Reid's user avatar
  • 13
0 votes
1 answer
65 views

MICE multiple imputation in R - imputation number

I'm running MICE for 100 imputations with big data (~600k rows). Due to storage restrictions at work (which I am not permitted to change), I can't save all 100 imputations in one go, and I'd hit ...
MICE man's user avatar
3 votes
0 answers
41 views

How to pool estimates from multiply-imputed datasets with complex sampling designs?

Analysts often use Rubin's rule (RR) to obtain a pooled estimate of a popular quantity from multiple (imputed) datasets. While popular statistical software (such as the R ...
socialscientist's user avatar
0 votes
1 answer
195 views

Can you impute (predict) missing continuous data using categorical data as the predictor?

I just read that to use MICE Imputation, variables with missing values need to have a relationship to other variables. In my case, I will anonymize the variable just for convenience purposes: ...
Atthoriq Pangestu's user avatar
1 vote
1 answer
540 views

Multiple imputation MICE categorical variable: problem with pooling after running ANOVA

I am trying to answer a question about satisfaction and its relation with a certain variable (numeric, 1-10). However, my data contains a lot of missing values in the satisfaction outcome, therefore I ...
Sharon's user avatar
  • 11
1 vote
0 answers
28 views

Which statistical test to use for Experimental vs Control at multiple time points

my study design is as follows: Mice received a cranial trauma and post trauma were administered either active agent (experimental group) or placebo (control group). There were xxx separate groups that ...
Jannis Mende's user avatar
0 votes
0 answers
141 views

Is there a way to fit a machine learning model to MICE imputed datasets and pool the results?

I have a medical dataset that has a lot of missing values. I imputed five datasets using MICE in R. I want to fit a classification machine learning model to the dataset. I want to identify the most ...
Just a stat student's user avatar
5 votes
1 answer
145 views

Is there a way to impute chi-square data?

I've tried looking here, as well as the go-to book Flexible Imputation of Missing Data, but cannot seem to find any reliable information on how to simulate chi-square missingness (as well as imputing ...
Shawn Hemelstrand's user avatar
0 votes
0 answers
444 views

Multiple logistic regression odds ratio on multiply imputed data in R

I am running a hierarchical logistic regression analysis using multiply imputed data in R (using the mice and miceafter packages). I am unable to get the odds ratio and 95% CI per variable adjusted ...
Mona's user avatar
  • 1
1 vote
0 answers
230 views

Mice imputation with a small number of missing values - test/train set may have no missing values

When performing resampling, I wish to perform the same imputation on the test set as I performed on the training set, which is accepted practice. So, when imputing with MICE, I generate a predictor ...
panda's user avatar
  • 121
1 vote
0 answers
81 views

To what extent does imputed data on item level have to respect the range of plausible values when one is interested in the aggregated scores? (MICE)

I’m aiming to impute data on Likert-Scale item level for a nested dataset using the MICE package. The data is nested in the sense that participants (>2000) belong to different clusters (around 100 ...
Rasul89's user avatar
  • 153
1 vote
1 answer
381 views

What is the limit of missing values for multiple imputation in the mice package?

I have two questions about the mice package. The first, is the mincor in the quickpred argument. When on the cran it says it is the absolute minimum correlation compared. Does this mean that if I set ...
Kledson Lemes's user avatar
2 votes
2 answers
332 views

Training set does not have missing values, but test set does. How to handle?

For a modeling challenge, I was given a training and test set separately. Since my training set did not have a substantial number of missing records, I omitted all rows with NA's. And trained my model ...
Dan's user avatar
  • 21
2 votes
0 answers
48 views

Mice package for imputation - chains not intermingling

I'm running an imputation using the mice package in R (imputing 7 variables with missing values on the basis of 10 total variables). The imputation runs fine, and ...
Henry Brice's user avatar
3 votes
1 answer
60 views

What is the compatibility of imputer and analyst models?

In a paper from Atem, et al 2018 (DOI: 10.1002/bimj.201800275), they claim the following in section 3 regarding the so called "imputer/imputation model" - i.e. the model used to impute ...
AdamO's user avatar
  • 63.8k
2 votes
1 answer
40 views

Advice on handling missing data (high percentage of missingness for only one item)

I'm working with data for a 30-item questionnaire that was administered at 24, 30, and 36 months of age. The data is largely complete, with the exception of age 36 months; at this age, we have one ...
wooden05's user avatar
0 votes
0 answers
82 views

How to choose the best converging imputations for my CFA model and pool them

I have 70 imputations of my original data set. I want to choose 50 of them which converged after less than 120 iterations on my CFA model: ...
juliawwu's user avatar
1 vote
0 answers
52 views

How to tune hyperparameters on 100 data sets generated by MICE

I am coding in Python and my data consists of 100 imputed data sets created by MICE in R. I am running Scikit-learn ML (Supervised Regression) algorithms to improve the prediction of Warfarin. My aim ...
ClaireSnibbe's user avatar
0 votes
1 answer
1k views

Iterations in Multiple Imputation

Despite reading this other StatsExchange post, I am still struggling to understand what iterations do in multiple imputation, i.e. the parameter "maxit" in the mice() function. My ...
Ator's user avatar
  • 5
1 vote
1 answer
174 views

Calculation for background characteristic of data sets that were imputed by mice and matched by MatchThem

I have performed multiple imputation in mice and created a dataset of 100 imputed data sets and then used MatchThem to perform propensity score matching. I can assess the balance of matched data sets ...
Totti's user avatar
  • 35
1 vote
1 answer
442 views

Confirming cubic spline was done on imputed datasets (imputed by mice Package) and the estimate is the pooled based on Rubin's rule

I am performing restricted cubic spline (Cox proportional hazard ratio) after imputing 10 datasets using mice package. My variables as follow: Outcome: DM Exposure: BMI time to events: time Covariates:...
Bkry's user avatar
  • 37
3 votes
1 answer
56 views

Missingness of data due to network issues

I have a time-series dataset that has 120 missing rows due to consecutive network issues and I am trying to impute these values using MICE in Python. As the source of missingness is a total ...
Hanna's user avatar
  • 145
0 votes
1 answer
600 views

Choosing MICE multiple datasets

I have read and watched several tutorials about MICE. My confusion is about step 1: creating several copies of the original dataset and imputing different values in each copy. In some tutorials, I ...
Hanna's user avatar
  • 145
0 votes
1 answer
566 views

Multiple Imputation for Predictors Only, Excluding Missing Outcome Data

I am working with a dataset containing ~300 predictors and ~3000 observations and building a predictive model using elastic net (and hoping to generalize to an external validation set). While the ...
NB3's user avatar
  • 15
1 vote
0 answers
807 views

Categorizing continuous variable after multiple imputation in mice

I am trying to create new variables after multiple imputation. I have the following variables: data: mydata total number of observations=500 HDL: continuous (no missing values) Physical activity: ...
Bkry's user avatar
  • 37
2 votes
1 answer
185 views

Pooling Survreg Results Across Multiply Imputed Datasets - Warning: log(1 - 2 * pnorm(width/2)) : NaNs produced

I am trying to run an interval regression using the survival r package (as described here https://stats.oarc.ucla.edu/r/dae/interval-regression/), but I am running into difficulties when trying to ...
Rachel's user avatar
  • 33

1
2 3 4 5