Package 'missr'

Simulated animal health data (MCAR)

Description

A toy dataset with heart rate data for various animals.

Usage

animalhealth

Format

A 200 x 2 data frame:

animal

The animal of interest

hear_rate

The corresponding heart rate of the animal (bpm)

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Simulated company data (MNAR)

Description

A toy dataset with typical company metrics across various firms.

Usage

companydata

Format

A 500 x 5 data frame:

sales

Sales in the last fiscal year (USD, million)

marketing_spend

Marketing spend in last fiscal year (USD, million)

product_rating

Average rating across all products

employees

Total employee count in last fiscal year

gross_profit

Gross profit in last fiscal year (USD, million)

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Simulated health check data (MAR)

Description

A toy dataset with typical health check-up metrics for various individuals.

Usage

healthcheck

Format

A 200 x 5 data frame:

bone_mass

Bone mass of individual (kg)

body_fat

Body fat percentage of individual

height

Height of individual (cm)

age

Age of individual

rbc

Red blood cell count of individual (million/mm^3)

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Missing at random (MAR) test

Description

mar() performs multiple logistic regressions to test for MAR. The null hypothesis for each is that the data are not MAR.

Usage

mar(data, debug = FALSE)

Arguments

data	A data frame.
debug	A logical value used only for unit testing.

Details

In the following, each column of M with missing data is regressed on D_obs. Each regression produces a vector of p-values (one for each variable in D_obs). The smallest p-value is the most important. This is because missing data need only be dependent on one observed variable for the data to be MAR. If each reported smallest p-value is significant, the data is MAR. See vignette("background") for definitions of M and D_obs.

Value

A tibble::tibble():

missing	Column of M with missing data
p_value	Smallest p-value of the logistic regressions
explanatory	Variable corresponding to p_value
p_values	The p-values of the logistic regressions
variables	Variables corresponding to p_values
combined	Paired p_values and variables for easier interpretation

Examples

mar(healthcheck)

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Little's missing completely at random (MCAR) test

Description

mcar() performs Little's MCAR test to test for MCAR. The null hypothesis is that the data is MCAR.

Usage

mcar(data, debug = FALSE)

Arguments

data	A data frame.
debug	A logical value used only for unit testing.

Details

This function reproduces the d^2 statistic in equation (5) from [1]. This statistic is used to test for MCAR. Comments reference variables from vignette("background") (in brackets) to improve readability and traceability.

Value

A tibble::tibble():

statistic	The d^2 statistic
degrees_freedom	Degrees of freedom of chi-squared distribution
p_val	P-value of the test
missing_patterns	Number of missing patterns

Note

Code is adapted from mcar_test() from the naniar package using base R instead of the tidyverse.

References

[1] Little RJA. A Test of Missing Completely at Random for Multivariate Data with Missing Values. Journal of the American Statistical Association. 1988;83(404):1198-202.

Examples

mcar(pollutionlevels)

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Missing not at random (MNAR) classification

Description

mnar() presents the statistics from mar() and mcar(). If at least one p-value in mar() is not significant, and the p-value in mcar() is significant then the data is MNAR.

Usage

mnar(data)

Arguments

data	A data frame

Details

There exists no formal test for MNAR data. This function therefore presents the statistics for the tests in mar() and mcar(). If the results suggest the data is neither MAR nor MCAR, one can use process of elimination to deduce that the data is MNAR.

Value

A list:

mcar	Results of Little's MCAR test
mar	Results of MAR test

Examples

mnar(companydata)

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Simulated pollution level data (MCAR)

Description

A toy dataset with typical pollution level metrics for various settlements.

Usage

pollutionlevels

Format

A 200 x 4 data frame:

light

Light pollution of settlement (mag/arcsec^2)

visual

Visual pollution of settlement (VPI)

noise

Noise pollution of settlement (dB)

air

Air pollution of settlement (AQI)

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Simulated test scores data

Description

A toy dataset with test scores of various students.

Usage

testscores

Format

A 200 x 2 data frame:

id

The ID of the student

score

The student's score in the test

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