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Missing_Data_R.html
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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Missing Data in R</title>
<meta charset="utf-8" />
<meta name="author" content="Asmae Toumi" />
<meta name="date" content="2020-07-30" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Missing Data in R
## Coast to Coast useR Meetup
### Asmae Toumi
### 2020-07-30
---
# CAUSES AND CONSEQUENCES OF MISSING DATA
- `Causes`:
- Participant skipped the question
- Participant refuses to cooperate
- Data/coding error
- Drop outs from longitudinal research
- Question not asked
- Censoring
- `Consequences`:
- Less data than planned
- Insufficient statistical power
- Inconsistent sample sizes across analyses
- Difficulty calculating even the most simple summary statistics
- Difficulty determining appropriate confidence interval, p-values
- Systematic biases in the analysis
**In sum, missing data can render analysis and interpretation difficult or impossible when unaddressed**
---
class: center, middle
# IMPUTING MISSING DATA
---
# MISSING DATA ASSUMPTIONS
According to Rubin (1976), there are 3 types of missing data assumptions:
- `MCAR`: Missing Completely at Random
Missing data is at random and the *pattern* of missing values is not related to the structure of the data.
- `MAR`: Missing at Random
Missing data is not completely random and the probability of missingness relies on the observed data, not the missing data.
- `MNAR`: Missing Not at Random
The missing data is not random and it's associated with factors that are unobservable and unknown to the analyst.
---
# OPTIONS YOU CAN TAKE
- `List-wise deletion`
- Also called Complete Case Analysis (CCA)
- Lose statistical power
- Large standard errors
- `Mean/median substitution`
- Disturbs the distribution
- Underestimates the variance
---
# A BETTER OPTION
- `Multiple Imputation`
- Accounts for the uncertainty around the "true" value
- Obtains approximately unbiased estimates
- Flexible to data type and analysis
The assumption is that **missing values can be imputed using predictions derived by the observable portion of the dataset**.
If this assumption is not met, missing data cannot be imputed using multiple imputation.
---
# APPROACHES TO MULTIPLE IMPUTATION
- `Joint Multivariate Normal Distribution Multiple Imputation`:
- Assumption: observed data follows a multivariate normal distribution
- Disadvantage: if the data doesn't follow a the above distribution, the imputed values will be incorrect
- 2 packages do this: `Amelia` and `norm`
- `Conditional Multiple Imputation`:
- Follows an iterative procedure, modeling the conditional distribution of a certain variable given the other variables
- Advantage: flexible because a distribution is assumed for **each** variable as opposed to the whole dataset
- `mice` does this (van Buuren, 2011)
---
# MICE
`mice` package computes missing data in 3 steps: mids (*multiply imputed data*), mira (*multiply imputed repeated analysis*) and mipo (*multiply imputed pooled object*)
---
# STEPS IN MICE
`mice()` command assumes the specific distribution of the missing variable and draws from that distribution in order to replace missing values with possible values. Multiple complete datasets get generated, called **mids** which stands for **multiply imputed dataset**
`with()` command runs regressions for each complete dataset, generating `n` sets of coefficients. These analysis results are stored in an object of class **mira** which stands for **multiply imputed repeated analysis**
`pool()` command takes the mean of the `n` regression coefficients and estimates the variance (within and between the complete datasets)
---
class: center, middle
# PRACTICAL APPLICATION OF MICE
See `Practice.Rmd`
---
# Roadblocks
- Violation of assumptions
- Too much missing data
- Wrong method in imputation
- High multicollinearity
`In conclusion, missing data imputation is a powerful method but it can be hard to implement correctly.`
---
# RECAP OF NANIAR'S SUITE OF VISUALIZATIONS
- Shadow matrices, a tidy data structure for missing data:
- `bind_shadow()`
- `nabular()`
- Shorthand summaries for missing data:
- `n_miss()`
- `n_complete()`
- `pct_miss()`
- `pct_complete()`
- Numerical summaries of missing data in variables and cases:
- `miss_var_summary()`
- `miss_var_table()`
- `miss_case_summary()`
- `miss_case_table()`
- Visualisation for missing data:
- `geom_miss_point()`
- `gg_miss_var()`
- `gg_miss_case()`
- `gg_miss_fct()`
---
# REFERENCES
- Naniar package: https://github.com/njtierney/naniar
- Gallery of missing data visualizations: https://cran.r-project.org/web/packages/naniar/vignettes/naniar-visualisation.html
- Van Buuren's course materials: https://stefvanbuuren.name/Winnipeg/Lectures/Winnipeg.pdf
- Vignettes for the `mice` package:
- (I): https://stefvanbuuren.name/Winnipeg/Practicals/Practical_I.html
- (II): https://stefvanbuuren.name/Winnipeg/Practicals/Practical_II.html
- (III): https://stefvanbuuren.name/Winnipeg/Practicals/Practical_III.html
- (IV): https://stefvanbuuren.name/Winnipeg/Practicals/Practical_IV.html
- Getting Started with Multiple Imputation in R, University of Virginia StatLab: https://uvastatlab.github.io/2019/05/01/getting-started-with-multiple-imputation-in-r/
</textarea>
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