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

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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 ...
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24 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 ...
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26 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: ...
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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 ...
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55 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. ...
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23 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 ...
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21 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 ...
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41 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 ...
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26 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 ...
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18 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, ...
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23 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 ...
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34 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 ...
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41 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 ...
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30 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 ...
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21 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 ...
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36 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 ...
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27 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. ...
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26 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 ...
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1answer
31 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 a set of predictors X measured at time 1 ...
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94 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 ...
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247 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 ...
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51 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 ...
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1answer
92 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 ...
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69 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 ...
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28 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 ...
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206 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 ...
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36 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 ...
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39 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 ...
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37 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 ...
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210 views

Converting R data frame to mids object with as.mids returns - error ini$imp[[i]] : subscript out of bounds [closed]

I am currently working on a project addressing individuals' sexual partnering behavior. There are missing data - which I impute in R using mice. The imputations go off without issue. The trouble ...
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3answers
492 views

How to get pooled p-values on tests done in multiple imputed datasets?

Using Amelia in R, I obtained multiple imputed datasets. After that, I performed a repeated measures test in SPSS. Now, I want to pool test results. I know that I can use Rubin's rules (implemented ...
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107 views

Conditional/normalized multiple imputation

Suppose I have a data set with a certain outcome $Y$, covariates $X$, and a certain status variable $Z$, which can take a finite (small) number of values, say 1, 2 and 3. Any of these variables may be ...
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73 views

Top-coded survey data

Top-coded income data are often modeled with a Pareto distribution, but that is controversial. What would be wrong with declaring those values as missing and then using multiple imputation (MI) to ...
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Imputation using imputed data

I have a data set with two files, one with outcome data, some of which are missing, and another with demographic data, some of which have been multiply imputed. I want to include the demographic data ...
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285 views

Using heckman in combination with mi estimate (Stata)

This is a Stata specific question, and may be better directed to Stata's own Statalist, but I'm trying here first. Here is the situation. I have a multiply-imputed data set, where the number of ...
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68 views

Running linear mixed effects model with Amelia package - How to run model diagnostics?

I'm trying to run a fairly simple linear mixed effects model in R, using the Zelig model ls.mixed (multi-level least-squares ...
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2answers
157 views

Missing data in repeated measures design

I have data from a a simple 2(voice system, manual system) x 2(easy, hard) within subjects experiment. Multiple DVs were collected. Due to issues with both the systems tested, data for one system ...
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1answer
58 views

Means of determining monotonicity of missing pattern other than eyeballing? And how monotone is monotone for the purpose of multiple imputation?

I have a large dataset with a large number of variables. Missingness as high as ~25% in some variables, many vars with no missing. Judgment of the monotonicity of the missing pattern is important for ...
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66 views

Multiple imputation for variables used to calculate regression weights

My basic question: is there anything that you can't impute using MI? My more complicated question: Consider the regression $Y=\rho T+X'\beta+\epsilon$. For whatever reason, you want to weight the ...
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1answer
37 views

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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1answer
43 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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77 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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281 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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106 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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2answers
192 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
131 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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1answer
611 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
275 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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45 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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118 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 ...