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

learn more… | top users | synonyms

1
vote
0answers
9 views

Multiple imputation with additive constraint

Say I have a dataset which includes three binary (dichotomous) variables: A, B, C, and C takes the value of A OR B. Many observations have missing A and B, and non-missing C. I guess that C should ...
0
votes
1answer
33 views

using cluster information in multiple imputation

i need to impute a dataset all categorical variables before doing analysis. I can just simply impute with mode of all data or a variable. I belief that better option will be to classify the subjects ...
-2
votes
0answers
31 views

Imputing missing responses in test exam [on hold]

I am working with a database that consist in the answers of 100 students to about 300 questions (1: right answer, 0: wrong ...
1
vote
0answers
22 views

Constructing Counterfactuals and Estimating Prevalence

I'm a social scientist working on a research project where I try to estimate the prevalence of lying in responding to a certain sensitive question. The way I estimate it is to rely on a ...
0
votes
0answers
8 views

How to replace the missing data from AMELIA results

I have run a AMELIA imputation for a data set including missing data. I need to replace the missing point by the result of amelia(). But it content 5 group of imputed values. How can i choose the ...
1
vote
0answers
22 views

Multiple Imputations exclude “not applicable”

I'm wondering how I should do the following in SPSS. I have a dataset with missing data (at random). Some values are blank, because the question was "not applicable" to that person (eg. questions ...
0
votes
0answers
26 views

Include interaction in multiple imputation - r

I'm doing some imputation models of time until recurrence of tuberculosis (Cox model). This model should include an interaction between the time and the outcome of the previous episode of disease (0- ...
0
votes
0answers
13 views

Multiple Imputation: Before or after case exclusion?

I've done quite some reading on multiple imputation (MI), but can not seem to figure out the next question: I have a dataset with missing values, some rows have many missing values, others have less ...
1
vote
0answers
7 views

Best Multiple Imputation Method for Multiple Surveys Mixed Together, Presented Randomly?

I am working with a dataset containing data from 15 different surveys. The surveys were presented all as one battery to participants, with questions from all surveys essentially placed into a pool and ...
1
vote
0answers
38 views

How to: repeated measures anova and Friedman's test with multiple imputations for missing data

I have a study where patients were measured pre-operatively (time_pre), post-operatively (time_post) and long-term follow-up (time_lt). I have 60 subjects. Data consists of: 5 scale variables ...
0
votes
0answers
45 views

Validation - correctly compare and validated imputation models

I've seen a lot of interesting questions here about multiple imputation and also great answers that helped me a lot to impute my data. I've used Predictive Mean Matching, EMB and I would like to use ...
11
votes
2answers
104 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 ...
2
votes
1answer
63 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 ...
7
votes
5answers
227 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. ...
0
votes
0answers
27 views

Explaining MCMC sampling for a Multinomial Distribution and missing at random

I understand how MCMC works, and I understand how Multinomial Distribution works. I have a dataset some of the data are missing at random (MAR). I cannot connect these two dots together (MCMC -> ...
0
votes
2answers
60 views

Simultaneous imputation of multiple binary variables in R

I have a dataset with multiple correlated binary variables (0/1). Can anyone point me toward a solution for imputing completely random missing values based on information in the other variables? ...
2
votes
0answers
25 views

Age Period Cohort imputation

Suppose I have some survey data on which I'd like to conduct an analysis similar to this: Yang, Age-period-cohort analysis, Ch 7. Suppose further there are missing data I wish to handle via multiple ...
1
vote
0answers
10 views

What to do when complete cases vs. imputed cases and train vs. test disagree?

In a fairly complex survival analysis case with considerable missing data, I split the data set into training and testing and ran models for both complete cases and imputed cases (I used multiple ...
3
votes
0answers
78 views

Is the following procedure to measure the quality of an imputation ok?

I'd like to compare different kinds of imputation techniques, i.e. methods which allow to fill missing data fields in a data frame. For now, I'm only using the R package ...
2
votes
1answer
54 views

What is the current research on missing not at random data?

I have a data set with N ~ 5000 and about 1/2 missing on at least one important variable. Comparing the people with missing data to those with complete data it is fairly clear that the data are not ...
3
votes
1answer
64 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 ...
1
vote
1answer
77 views

What to do with missing values?

I have missing values for some of the variables in my data. I am using pooled OLS and have 144 observations. I have missing values for three of the variables. Less than 10% of the data for each ...
2
votes
1answer
46 views

Is it better to use data imputation for missing data or an analysis that is not affected by missing data (e.g., HLM/mixed effects modelling)?

I have treated two groups of 100 people with different treatments. I have pre-treatment and post-treatment data for most participants (as well as 1-month follow-up. I also have weekly data for some ...
1
vote
1answer
31 views

Mechanism of multiple imputation?

I am trying to understand the mechanism underline multiple imputation ideas. I am confusing on creating multiple sets of estimates and then average them. For example: ...
0
votes
0answers
7 views

Over-estimated response

I have a large survey data set, about 15 years of data. One binary variable (yes/no) in which I'm interested was over-estimated due to a data collection error for about 1 1/2 years. I'm not sure how ...
2
votes
0answers
73 views

Imputation with mice: recode variables before or after imputation?

I am using mice in R, a chained equations (sequential regression) algorithm, to impute a series of polytomous variables (e.g. ...
2
votes
0answers
27 views

Generating a categorical variable from an imputed variable

I am using multiple imputation to impute a continuous variable ($X$) with $\approx30\%$ missing values. I have a question regarding the generation of a new categorical variable ($Y$), starting from ...
1
vote
0answers
41 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 ...
0
votes
0answers
69 views

Using MICE in R: is it possible to impute only sub-sections of the data?

When using the mice library in R to impute data I encounter the following problem. I have a data matrix with missing information ...
1
vote
0answers
40 views

Missing data (multiple imputation)

Can I multiply impute missing data in some of the variables which were for some reason not measured at one time? For instance, for production I have time series data for 1961-2011: however, some ...
0
votes
0answers
22 views

IVEware imputation results

I have a large survey dataset (~150k individuals) on which I used IVEware to impute missing data. Most of the variables missing data are missing very small percentages of data (<5%). However, ...
2
votes
0answers
27 views

Calculating boxplots with imputed data

I have imputed a dataset which has over 200 variables, and 20 observations. In worst case, 80% of the data is missing, in the best case 100% is available. 5 out of the 20 participants provided data ...
0
votes
0answers
43 views

Multiple Imputation and two-level data

I have a question on multiple imputation where one variable is the sum of several sub-groups. I have about 5 variables with a significant level of missingness. However I have a sixth variable which ...
0
votes
0answers
68 views

Multiple imputation on new data in R

I am looking for a R package that can do multiple imputation on 2 sets of data in the same fashion. That is, I would like to multiply impute the training set and then impute the test set in the same ...
0
votes
1answer
43 views

multiple imputation for a longitudinal study

i have an experiment wherein respondents were tested in two time points. however, respondents were tested at t1 and t2 OR t1 and t3 OR t1 and t4. Hence, data is missing at t2,t3,and t4 for 3/4 of ...
0
votes
0answers
23 views

IVEware warnings

When using SAS callable IVEware for multiple imputation it will occasionally throw out a warning for too many iterations. Can someone give me an idea of how many warnings are acceptable (if any) for ...
2
votes
0answers
41 views

What are some of the ways to deal with missing data when measuring extreme poverty?

The UNDP have reported that the millennium goal of halving the percentage of people living below 1 USD (PPP) a day has been met (compared to 1990). I was looking at the data for that indicator and ...
0
votes
0answers
32 views

How to analyse Multiple Imputation data with SPSS?

I have to work on a dataset treated with the Multiple Imputation method to handle missing data. In fact, I have 5 different variant of the same dataset, with missing data replaced by probable values. ...
2
votes
0answers
41 views

multiple imputation

I am running multiple imputation for a set of variables including clinical data. I am wondering if I can use (or should use) outcome variable (follow-up is 99%) to predict missing clinical data. There ...
5
votes
1answer
51 views

In a longitudinal study, should I impute the outcome Y, measured at time 2, for individuals who were lost to follow-up?

I have repeat measures at 2 times points in a sample of people. There are 18k people at time 1, and 13k at time 2 (5000 lost to follow-up). I want to regress an outcome Y measured at time 2 (and the ...
2
votes
1answer
153 views

Multiple imputation for missing values

I would like to use imputation for replacing missing values in my data set. I have some constraints , for example I dont not want imputed variable x1 be less than ...
6
votes
2answers
379 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 ...
0
votes
0answers
80 views

Impute values with Amelia in R from factor variable

I've a dataset of individuals from which I would like to impute the missing values for 'Age'. Althought the set has several columns, I noticed the most relevant one in respect to Age is the column ...
1
vote
1answer
131 views

Back-transforming a reflected logged variable

In order to prepare variables for multiple imputation, I did some data transformation on skewed variables. Therefore, I reflected them (largest value+1 minus variable) and took the lg10. After ...
0
votes
0answers
81 views

Fit statistics and log-likelihood after multiple imputation estimation

I am working with four survey datasets, two of them without a certain variable which I impute using multiple imputation techniques. Afterwards, I use the complete data to estimate a set of logit ...
0
votes
0answers
29 views

Multiple Imputation Using Different Data Sets

I realize that this is contrary to "standard" multiple imputation using Rubin's rules, but I have a data set were a variable with a lot of missing values is most likely MNAR. It represents a large ...
1
vote
1answer
356 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 ...
0
votes
1answer
38 views

Multiple imputation - original model

I have a question. I have a dataset with some missing values that were not MCAR. I imputed them with fully conditional specification method iterations. I then executed my analysis on the basis of the ...
0
votes
0answers
45 views

Tips when regressing growth rates

I have 20 months of Year over Year growth rates for a X independent variable and Y dependent variable. The correlation between these two variables is 0.72. I would like to predict Y using X for ...
1
vote
1answer
40 views

How to impute or predict a characteristic when one of the IVs in the prediction is other household members having that same characteristic?

In some data set A, we have: household id, person id, age, sex, and then a simple binary likes donuts / does not like donuts variable. In some other data set B, we ...