Understanding the mice Package
The ‘mice’ (Multivariate Imputation by Chained Equations) package in R Programming Language is a powerful and flexible tool for multiple imputations. It automates the process of handling missing data by generating multiple imputations for each missing value, creating several completed datasets. These datasets can then be analyzed separately, and the results combined to produce valid statistical inferences.
Key Features of Mice
Here are the main key features of the Mice Package in R Programming Language.
- Multiple Imputation: Creates multiple datasets with imputed values to account for variability in the imputation process.
- Flexible Imputation Methods: Supports various methods for different types of data (e.g., numeric, categorical).
- Ease of Use: Provides a straightforward interface for imputing missing data and analyzing imputed datasets.
Installation and Loading
To use the mice package, you need to install and load it in your R environment.
install.packages(“mice”)
library(mice)
Workflow with mice
The general workflow for using the mice package involves the following steps:
- Check for Missing Data: Identify the missing values in your dataset.
- Impute Missing Data: Use mice to generate multiple imputations.
- Analyze Imputed Data: Perform your analysis on each imputed dataset.
- Pool Results: Combine the results from each imputed dataset to obtain final estimates.
Let’s walk through a complete example using the mice package.
Step 1: Load and Explore the Data
We will use the nhanes dataset included in the mice package, which contains data from the National Health and Nutrition Examination Survey.
# Load the mice package
library(mice)
# Load the nhanes dataset
data("nhanes", package = "mice")
# View the first few rows
head(nhanes)
Output:
age bmi hyp chl
1 1 NA NA NA
2 2 22.7 1 187
3 1 NA 1 187
4 3 NA NA NA
5 1 20.4 1 113
6 3 NA NA 184
The nhanes dataset contains some missing values:
Step 2: Impute Missing Data
Use the mice function to impute missing values. By default, mice creates five imputed datasets.
# Perform multiple imputation
imputed_data <- mice(nhanes, m = 5, method = 'pmm', seed = 123)
# View summary of the imputed data
summary(imputed_data)
Output:
Class: mids
Number of multiple imputations: 5
Imputation methods:
age bmi hyp chl
"" "pmm" "pmm" "pmm"
PredictorMatrix:
age bmi hyp chl
age 0 1 1 1
bmi 1 0 1 1
hyp 1 1 0 1
chl 1 1 1 0
The mice function creates an object containing the imputed datasets. The method = ‘pmm’ argument specifies the predictive mean matching method for numeric data.
Step 3: Analyze Imputed Data
You can analyze each imputed dataset using standard R functions. The with function from the mice package facilitates this.
# Perform linear regression on each imputed dataset
fit <- with(imputed_data, lm(bmi ~ age + hyp))
# View the summary of the fit
summary(fit)
Output:
# A tibble: 15 × 6
term estimate std.error statistic p.value nobs
<chr> <dbl> <dbl> <dbl> <dbl> <int>
1 (Intercept) 29.9 2.58 11.6 7.97e-11 25
2 age -2.52 1.16 -2.17 4.14e- 2 25
3 hyp 1.67 2.22 0.753 4.59e- 1 25
4 (Intercept) 27.6 2.76 9.99 1.22e- 9 25
5 age -2.42 1.22 -1.98 6.03e- 2 25
6 hyp 2.67 2.48 1.08 2.94e- 1 25
7 (Intercept) 28.8 2.23 12.9 9.44e-12 25
8 age -2.39 0.985 -2.43 2.38e- 2 25
9 hyp 1.66 2.00 0.829 4.16e- 1 25
10 (Intercept) 27.5 2.32 11.9 4.84e-11 25
11 age -2.85 1.19 -2.40 2.55e- 2 25
12 hyp 3.48 2.15 1.61 1.21e- 1 25
13 (Intercept) 27.9 2.63 10.6 3.96e-10 25
14 age -2.65 1.02 -2.61 1.60e- 2 25
15 hyp 2.82 2.26 1.25 2.24e- 1 25
Step 4: Pool Results
After analyzing each imputed dataset, use the pool function to combine the results.
# Pool the results from the multiple imputations
pooled_results <- pool(fit)
# View the summary of the pooled results
summary(pooled_results)
Output:
term estimate std.error statistic df p.value
1 (Intercept) 28.335723 2.733529 10.365985 15.45994 2.322665e-08
2 age -2.566184 1.137495 -2.255996 19.47629 3.574436e-02
3 hyp 2.459956 2.388573 1.029885 16.40120 3.180134e-01
The pool function combines the estimates from each imputed dataset and calculates appropriate standard errors.
Advanced Imputation Techniques
The mice package supports various imputation methods for different types of data:
- Predictive Mean Matching (pmm): Default method for numeric data.
- Logistic Regression (logreg): For binary data.
- Polynomial Regression (polyreg): For ordered categorical data.
- Multinomial Logistic Regression (polyreg): For unordered categorical data.
Visualization of Imputed Data
Visualizing the imputed data can help assess the quality of imputations. The stripplot function provides a graphical representation of the imputed values.
# Load necessary library
library(mice)
# Load the nhanes dataset
data("nhanes", package = "mice")
# View the first few rows of the dataset
head(nhanes)
# Check the unique values in the hyp variable
unique(nhanes$hyp)
# Recode the hyp variable to binary (0 and 1)
nhanes$hyp <- ifelse(nhanes$hyp == 2, 1, 0)
# Specify methods for each column
methods <- c("pmm", "pmm", "logreg", "pmm")
names(methods) <- names(nhanes)
# Perform multiple imputation with specified methods
imputed_data <- mice(nhanes, m = 5, method = methods, seed = 123)
# View summary of the imputed data
summary(imputed_data)
# Visualize imputed data
stripplot(imputed_data, pch = 20, cex = 1.2)
Output:
Mice Package in R
The stripplot output displays the distribution of observed and imputed values for each variable:
- Observed Values: Represented by points in the plot.
- Imputed Values: Typically displayed in different colors, showing where imputations were made.
For each variable (age, bmi, hyp, chl), the plot allows you to visually assess the distribution of imputed values compared to the observed ones. This helps in evaluating the plausibility of the imputed values.
Mice Package in R
Missing data is a common issue in statistical analysis, leading to biased estimates and reduced statistical power. Multiple imputation (MI) addresses this by creating several complete datasets, analyzing each, and then combining the results. MI accounts for the uncertainty associated with missing data, providing more accurate and robust statistical inferences.