The missing-data tag has no wiki summary.
2
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2answers
65 views
What machine learning techniques can, once trained, generate prediction despite some missing inputs?
I have a training set where the inputs & outputs are all present, but I suspect that in the data where I want to do prediction, I will occasionally encounter scenarios where a small fraction of ...
0
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0answers
17 views
Averaging gridded data with missing values
I would like to use various parameters of remote sensing data (i.e. chorlophyl, sea-surface temperature, wind stress, etc.) as covariates in a species distribution model.
I have sightings data, ...
1
vote
1answer
50 views
Fitting a Poisson distribution from missing observations
I am interested in fitting a Poisson/negative binomial distribution to estimate the number of times a phenomenon happens within a period, let's just say 10 years. I can count the events from monthly ...
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0answers
17 views
return back the imputed values [migrated]
is there any function in r that can help return imputed values, for example;
...
2
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0answers
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 ...
1
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0answers
33 views
R - Multidimensional Scaling and Missing Values
I include MDS analysis in a customer survey and have about 10 brands I want to include in the perceptual map at the end. Meaning the customers would have to rate 45 comparisons and give a similarity ...
0
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0answers
30 views
Monotonic and non-monotonic patterns of missing values: how do they look like?
I was reading the user's guide of SPSS on missing value analysis and I found the terms monotonic and non-monotonic pattern of missing values. The terms are not quite clear to me. They used ...
2
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1answer
94 views
How should I define missing values due to skip questions in SPSS?
I have a questionnaire that contains some skip questions. Like, say, the 3rd question is a yes/no type question. Only those who answered "yes" to the 3rd question are requested to answer the 4th, 5th ...
0
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0answers
18 views
Strategy to model, then predict / impute with very sparse variable?
Please excuse vague title. I am currently using an unsupervised SOM clustering approach to try to determine values for a variable that is mostly missing. I have ~8000 observations of 10 variables, the ...
1
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1answer
63 views
Likert Scale Analysis - Pre/Post
I was hoping somebody could help with determining the correct statistical test to use.
Basically a teaching session was done which assessed confidence and perceptions of handling a situation using 3 ...
0
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0answers
59 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 ...
2
votes
1answer
61 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 ...
0
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0answers
27 views
Can I safely ignore weekends in a foreign-exchange market time series analysis
I'm working on a paper analysing the behavior of foreign exchange markets and identifying structural breaks in currency price time series.
This is a bit of a stupid question, but can I just safely ...
1
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0answers
33 views
Does the EM algorithm for mixtures still address the missing data issue?
There is a PDF $p(D| \theta)=p(X,Z| \theta)$ with observed values $X$ but also some missing or incomplete values $Z$ (for eg. resulting from censoring).
The expectation-maximization (EM) algorithm is ...
0
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1answer
87 views
Repeated Measures ANOVA missing values - run separate models?
I am evaluating pre and post test data for 2 groups using repeated measures ANOVA. Given that I am missing a few data cells, the final n is reduced in the analysis. I'd like to know if I can run ...
0
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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):
...
3
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2answers
110 views
Imputing a missing variable based on common variables with another data set
I have 2 data sets: $A$ and $B$. The variables are common to both data sets with the exception of two, which are both missing in A. Let's call those two additional variables: $b_1$ and $b_2$. We ...
3
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1answer
36 views
When do missing data indicate a data collection bug?
I have collected this data, counting how many users engage in a certain activity how many times:
...
0
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0answers
103 views
two way repeated measures anova random factors missing data
I have a pretty simple time series experiment that I'd like to analyze in R. I have an experimental and control group and for each individual in each group I collected samples from them over a course ...
0
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0answers
32 views
Predict missing value(s) using existing measurement's data
I have a few measurements (6) with 13 different features (so I have more parameters than measurements).
Let's say I would have a new measurement with a few missing values, considering I have existing ...
5
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2answers
171 views
What is the advantage of imputation over building multiple models in regression?
I wonder if someone could provide some insight into if an why imputation for missing data is better than simply building different models for cases with missing data. Especially in the case of ...
0
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0answers
51 views
survival analysis in stata
When we analyze the length of adoption period,there are people who already adopt after the introduction of the technology.There are people who will not adopt.Some have the 1 year, 2 year.... etc ...
6
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2answers
355 views
Full information maximum likelihood for missing data in R
Context: Hierarchical regression with some missing data.
Question: How do I use full information maximum likelihood (FIML) estimation to address missing data in R? Is there a package you would ...
2
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0answers
52 views
Dealing with zeros in a poisson regression
Our code goes through multiple stages of review. I wish to use the number of defects at an earlier stage of review as a "defect density" estimate for later stages.
It sometimes happens that code has ...
2
votes
1answer
147 views
Classification with 3 groups, repeated measurements, missing values, more predictors than subjects
I am working on a classification problem with the following characteristics:
Individuals belong to one of three groups. The groups are "somewhat ordinal": controls, subclinical and clinical group.
...
3
votes
1answer
148 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 ...
3
votes
1answer
216 views
Imputation with Random Forests
I have two questions on using random forest (specifically randomForest in R) for missing value imputation (in the predictor space).
1) How does the imputation algorithm work - specifically how and ...
0
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0answers
34 views
Centering to use a sparse covariate?
In my dataset, there's a binary response, some factors, and some covariates. In particular, there are some covariates that are always present when factor1=="A" and ...
2
votes
1answer
75 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:
...
4
votes
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 ...
0
votes
1answer
96 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 ...
0
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0answers
55 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 ...
1
vote
1answer
63 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 ...
2
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1answer
104 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 ...
0
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0answers
29 views
Find and fill values for the missing points using close/similar vectors
I have a matrix which looks like the attached image below, and I would like to know what's the statistically best educated guess for the missing steps (filled in orange) for the vector $\mathbf N$.
...
2
votes
1answer
91 views
Pooling the results of random hot-deck imputation
I am using random hot-deck imputation on a repeated measures dataset.
I am tempted to use Rubin's rules for pooling the results of multiple imputation, in particular for regression coefficients. ...
3
votes
1answer
171 views
Imputation for a zero-inflated negative binomial mixed effects model
I am working with a dataset of repeated (x4) observations on 100 subjects.
The outcome is zero-inflated and the data appears to be modelled well by a mixed effects zero-inflated negative binomial ...
2
votes
1answer
245 views
Missing values in GLM
I am applying glm on a data in which most of the values are NAs or blank. For example, in the example data produced below (4 predictors and one response variable), the default glm command will remove ...
2
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2answers
160 views
How to decide what to do with missing data when doing data analysis?
I'm doing data analysis with MATLAB and I have a bunch of files with missing data here and there and somewhere even whole days or months worth of data is missing, because sensor devices have been ...
1
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2answers
77 views
Missing values replacement?
I have a dataset of 500 people and am trying to fit a prediction model using both quantitative and categorical variables. I have adjusted the dataset as much as possible, but still have one variable ...
0
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1answer
432 views
Using the R forecast package with missing values and/or irregular time series
I am impressed by the R forecast package, as well as e.g. the zoo package for irregular time series and interpolation of missing ...
0
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0answers
102 views
Unbalanced, non-parametric repeated measures anova
I have an unbalanced repeated measures design with low sample sizes (n=3 for treatment, n=5 for control). Everyday for eight days I measured a response (in percent); however, because of the nature of ...
1
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1answer
70 views
Confusion related to calculation of conditional distribution
I have this confusion related to the calculation of a conditional distribution
suppose $y_n = N(0,w)$
$p(o_n|y_n) = N(D.y_n,\phi)$
How do I calculate
$p(y_n|o_n)$
Actually I was reading this ...
1
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0answers
26 views
Confusion related to a paper related to multivariate spatio-temporal data with missing attributes
I was reading this paper related to handling missing attributes of multivariate spatio temporal data. http://astro.temple.edu/~tua86150/Lou_IJCAI_11.pdf.
Here they have tried to exploit the spatial ...
1
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1answer
50 views
How to analyze ordered data type with past missing observations?
I am having trouble understanding what method to apply for the analysis of the following type of data:
...
5
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1answer
134 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 ...
7
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2answers
364 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 ...
1
vote
1answer
81 views
Effects of replacing missing data with mean or median?
When preparing data for use with Covariance-based Structural Equation Modeling (CB-SEM), what are the different effects of replacing missing data with mean or median? When is one of them more ...
5
votes
2answers
139 views
Other substitution matrices for missing value state in sequence analysis with TraMineR?
We have a question about how to deal with missing values/gaps within sequences. We like to set up our own substitution-cost matrix for the Optimal Matching process. As far as we know, TraMiner allows ...
2
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1answer
110 views
Type of data missingness in R
I am working on a short panel of 3 periods with a few hundred subject, and as the question and as the question suggests, I have some blanks. I know that there are not due to attrition since it is ...
