Multiple imputation refers to a set of stochastic imputation routines aimed at preserving the multivariate features of the data
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Imputation variance and explained variance (in vector autoregression)
I have a question concerning the coefficients of VAR models used on multiple imputed data (high missigness in some variables: up to 40%).
In particular I would like to know how the coefficients are ...
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13 views
Selecting cases for analysis based on multiply imputed values
I have a large public dataset describing traffic crashes. I am interested in events occurring in a certain type of street intersection only. The dataset has a lot of missing values, and I plan to use ...
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17 views
Using MatchIt to match groups in a retrospective analysis
I am interested in using the R package MatchIt to preprocess my data as to obtain matched groups based on a predefined treatment variable. However I am facing a few issues.
The first issue is that ...
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46 views
Meaning of warning messages in logistic regression using multiply imputed data set in SPSS
I had a data set containing 2 dependent variables (one binary and the other ordinal) and a set of 30 co-variates (both continuous and categorical) with a lots of missing values. I heard that multiple ...
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31 views
multiple imputation with binary variables
I have 54 missing values in my dataset of 459 cases. Variables are all binary (0-1). I want to try a multiple imputation to avoid a listwise deletion, using the mi ...
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47 views
worked example of multiple imputation in R
I have data that I have read into R 47 columns across and 592 rows deep. In 5 of those columns are a large number of missing values. So I want to perform multiple imputation to fill in the gaps. I ...
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1answer
68 views
Bootstrapped confidence intervals for the parameters of a linear model applied to multiply imputed data
I would like to construct CIs for $\beta$ in the linear model
$Y = X\beta + \epsilon$
I observe $\{X', Y'\}$ which is $\{X,Y\}$ contaminated with values missing at random. $\epsilon$ is not Gaussian ...
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1answer
84 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 ...
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70 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 ...
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1answer
63 views
Warning:“NAs introduced by coercion” in MICE with unique ID
I am having a problem using MICE, where it generates the following warning:
Warning message:
In var(data[, j], na.rm = TRUE) : NAs introduced by coercion
This ...
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0answers
27 views
complicated model without a huge dataset
I've got a set of data that I'm trying to model. Lots of the data is missing, so I'm using multiple imputation. I've got about 360 observations and 13 variables. I'm also using GAMs, but that ...
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1answer
44 views
Estimation with transformations of variables after multiple imputation
I would really appreciate some advice.
I have two sequential measurements of severity of illness $(s_1, s_2)$ that are incompletely observed along with an outcome measure ($y$). I am trying to ...
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1answer
34 views
Orthogonal sets of variables in multiple imputation --> separate imputation models?
First, thanks to those who gave me useful input on this project in a previous thread on this site.I've got a new-ish question at this point on the mechanics of MI (using MI via chained equations):
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2answers
74 views
Summary of residuals in R
Disregarding "Deviance" in the image, the output of multiple regression analysis in R looks pretty much like this.
As far as I understand, residuals are errors. Do the 5 value summary refer to ...
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1answer
176 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.
...
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1answer
164 views
Multiple Imputation - calculating effect size and reporting results
I'm analyzing the change of various psychometric features that are supposed to change during treatment and their relation to some other features (I don't think I have to go much into detail here). My ...
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1answer
150 views
Relative advantages of multiple imputation and expectation maximization (EM)
I've got a problem where
$$y = a + b $$
I observe y, but neither $a$ nor $b$. I want to estimate
$$b = f(x) + \epsilon$$
I can estimate $a$, using some sort of regression model. This gives me ...
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1answer
76 views
Why does MICE fail to impute multilevel data with 2l.norm and 2l.pan?
Why does MICE fail to impute multilevel data with 2l.norm and 2l.pan in this situation ?
Here is a reproducible example:
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2answers
94 views
Diagnosing why MICE is crashing R when attempting to impute multilevel data
I have never had problems with R crashing before.
I am using the mice package (mice 2.13) to perform multiple imputations. The code works fine on some subsets of ...
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1answer
97 views
Why does MICE fail for one dataset and not the other?
I am getting this error in MICE
Error in seq.default(1, ncol(pred)) : 'to' must be of length 1
My dataset is very large but I have been able to create a ...
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56 views
Why does multilevel imputation in MICE work OK with 2l.pan but not 2l.norm?
Using the 2l.pan method in mice, I am able to obtain imputations without a problem.
However using the ...
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1answer
64 views
Multiple imputations via MICE package: is there an upper limit for number of imputations?
When performing mulitple imputations with the MICE package in R, I found some resources (http://www.statisticalhorizons.com/more-imputations) that recommend around 10 imputations. I was wondering if ...
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1answer
119 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 ...
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57 views
Combined variance following multiple imputation with survival model
I have created 5 imputations of a dataset and have fit a survival model to them all in R. I want to combine the estimates of the coefficients and the standard errors of the coefficients. To do this I ...
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1answer
225 views
Multiple imputation with the Amelia package
I have a general question about the Amelia package. I'm no mathematician or statistician, but I had to use R and impute and analyze some data, and Amelia showed results that fitted my expectations. ...
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2answers
392 views
Multiple imputation and model fitting
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 ...
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1answer
138 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 ...
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2answers
385 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 ...
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1answer
117 views
Software implementation of stepwise regression after multiple imputation
Simple question, does anyone know of a package (R preferred, but I'll take anything, SAS, Stata, SPSS) which implements stepwise regression of multiply imputed datasets. I've read that it's possible ...
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1answer
220 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 ...
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42 views
Previously created mim dataset, how to convert to mim2 dataset? [closed]
I want to use binary_mediation, a user-written program, for my previously created mim dataset. However, binary_mediation seems to only work with mim2. I now have a mim data with _mi and _mj created. ...
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203 views
Mean comparisons following multiple imputation
I need to do some simple mean comparisons between groups (basic F-test ANOVA) on data with missing values. I use R mice package for multiple imputation, but I can only pool results for the linear ...
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1answer
460 views
Multiple imputation questions for multiple regression in SPSS
I am currently running a multiple regression model using imputed data and have a few questions.
Background:
Using SPSS 18. My data appears to be MAR. Listwise deletion of cases leaves me with only ...
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1answer
250 views
How do I fix this error in mice multiple imputation?
I am trying to do multiple imputation using the mice package in R, and the imputation keeps stopping with the following error:
...
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2answers
211 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, 3, or X if the measurement is indeterminable. There are some NAs that I want to impute using the mice package in R, ...
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84 views
Generating quartiles from an imputed variable
I am using Multiple Imputation to impute a continuous variable (X). I have a question regarding the generation of a new variable, starting from this imputed ...
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91 views
Curing noncoverage with hot-deck imputation?
Is it advisable to use hot-deck imputation to allow for poststratification in the presence of empty post-strata?
In a survey reweighting exercise, I have population totals for a complete ...
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1answer
174 views
Multiple regression with missing predictor variable
Suppose we are given a set of data of the form $(y,x_{1},x_{2},\cdots, x_{n})$ and $(y,x_{1},x_{2},\cdots, x_{n-1})$. We are given the task of predicting $y$ based on values of $x$. We estimate two ...
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1answer
108 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 ...
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1answer
164 views
Clustering variables with outliers
I am performing a clustering analysis in SAS and some of the variables that I am trying to cluster contain outliers. I've tried to transform the data (log and/or standardize them) but didn't quite ...
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246 views
Multiple imputation of time variables — which step to impute?
Lets assume I have a survival analysis study with an exposure, two covariates, and two time related variables. Say date of diagnosis and date of death. Combined, the two time related variables will be ...
