Package {glemb}

Convert a glemb object to a mids object

Description

Converts a glemb object to the mids format used by the mice package, enabling use of mice::pool() for results pooling.

Usage

as.mids.glemb(x, data, ...)

Arguments

Details

The function stacks the original data (as imputation 0) and all m imputed datasets in long format, adds .imp and .id columns, and calls mice::as.mids() to construct the mids object.

Value

A mids object.

See Also

mice::as.mids(), mice::pool()

Examples

if (requireNamespace("mice", quietly = TRUE)) {
  imp <- glemb(nlsymiss, m = 5,
               noms = c(race, pov, momwork, divorce, gender),
               output = "list", seed = 1)
  mids_obj <- as.mids.glemb(imp, data = nlsymiss)
  fit <- with(mids_obj, lm(anti ~ self + pov + race + childage + divorce +
                             gender + momwork))
  mice::pool(fit)
}

Multiple Imputation Using the General Location Model with the EMB Algorithm

Description

glemb() performs multiple imputation of mixed continuous and categorical data using the General Location Model (GLM) combined with the Expectation-Maximization with Bootstrapping (EMB) algorithm.

Usage

glemb(
  data,
  m = 20L,
  noms = NULL,
  idvars = NULL,
  cat.interact = 2L,
  cat.prior = NULL,
  empri = NULL,
  maxits = 1000L,
  seed = NULL,
  output = "mids",
  max.cells = 500000L,
  meanmodel = "main",
  p2s = 1L
)

Arguments

Details

Statistical model

The General Location Model (Olkin & Tate 1961; Little & Schluchter 1985) factorises the joint distribution of continuous variables y and categorical variables x as:

p(\mathbf{y}, \mathbf{x}) = p(\mathbf{y} \mid \mathbf{x})\, p(\mathbf{x})

The categorical margin p(\mathbf{x}) is modelled with a log-linear model restricted to at most cat.interact-way interactions among the categorical variables. The continuous conditional p(\mathbf{y} \mid \mathbf{x} = c) is multivariate normal with a covariance matrix shared across all cells. How the cell means are parameterised depends on meanmodel. With "main" (default) the mean of each continuous variable depends on an intercept plus the main effects of the categorical variables — the same parameterisation as a standard regression with dummy variables. With "saturated" each cell has its own unrestricted mean vector. Both models imply that the conditional mean of every continuous variable is a linear function of all other variables; the saturated model additionally allows all interactions among the categorical variables to enter the means. The conditional distribution of every categorical variable is a logistic regression function of all the other variables.

EMB algorithm

Proper multiple imputations are generated via the EMB algorithm (Honaker, King & Blackwell 2011):

1. Draw a bootstrap sample (with replacement) from the incomplete data.

2. Run the ECM algorithm (mix::ecm.mix()) on the bootstrap sample to obtain parameter estimates.

3. Draw one set of imputed values from the posterior predictive distribution given those parameters (mix::imp.mix()).

4. Repeat steps 1–3 m times to produce m completed datasets.

Sparsity

When the number of cells in the multiway table for the categorical variables is large relative to sample size, some cells may contain few or no observations. A Dirichlet prior (controlled by cat.prior) is applied by adding cat.prior pseudo-observations per cell to the bootstrap sample before estimation. glemb() warns when the number of cells exceeds n / 5.

Value

An object of class "glemb", which is a list with components:

imputations

A list of m completed data frames.

m

Number of imputations.

call

The matched call.

categorical

Names of variables treated as categorical.

continuous

Names of variables treated as continuous.

idvars

Names of ID variables excluded from the imputation model.

cat.interact

Interaction order used in the log-linear model.

cat.prior

Dirichlet pseudocount used (after automatic selection if applicable).

empri

Ridge prior strength used (after automatic selection if applicable).

maxits

Maximum ECM iterations per bootstrap sample.

seed

Seed used, or NULL.

meanmodel

Mean model used: "main" or "saturated".

References

Olkin, I. & Tate, R.F. (1961). Multivariate correlation models with mixed discrete and continuous variables. Annals of Mathematical Statistics, 32, 448–465. doi:10.1214/aoms/1177705052

Little, R.J.A. & Schluchter, M.D. (1985). Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika, 72, 497–512. doi:10.1093/biomet/72.3.497

Honaker, J., King, G. & Blackwell, M. (2011). Amelia II: A program for missing data. Journal of Statistical Software, 45(7), 1–47. doi:10.18637/jss.v045.i07

Schafer, J.L. (1997). Analysis of Incomplete Multivariate Data. Chapman & Hall.

See Also

print.glemb(), summary.glemb(), as.mids.glemb()

Examples

# Impute the NLSY data, treating five variables as categorical
imp <- glemb(nlsymiss, m = 5, noms = c(race, pov, momwork, divorce, gender),
             seed = 123)

# Fit a regression to each completed dataset and pool the results
if (requireNamespace("mice", quietly = TRUE)) {
  fit <- with(imp, lm(anti ~ self + pov + race + childage + divorce +
                        gender + momwork))
  summary(mice::pool(fit))
}

# Quoted names work too; output = "list" returns a glemb object
imp <- glemb(nlsymiss, m = 5,
             noms = c("race", "pov", "momwork", "divorce", "gender"),
             output = "list", seed = 123)
head(imp$imputations[[3]])


# Allow three-way interactions among categorical variables
imp <- glemb(nlsymiss, m = 5, noms = c(race, pov, momwork),
             cat.interact = 3, output = "list", seed = 123)

# Use a saturated mean model (cell-specific intercepts; more flexible,
# requires more data)
imp <- glemb(nlsymiss, m = 5, noms = c(race, pov, momwork),
             meanmodel = "saturated", output = "list", seed = 123)

Children's behavior data from the NLSY, with missing values

Description

A subsample of 581 children interviewed in 1990 as part of the National Longitudinal Survey of Youth (NLSY), used as a running example in Allison (2001). Five of the nine variables have missing values, and only 225 cases are complete. The data mix continuous and categorical variables, which makes them a natural application of the General Location Model.

Usage

nlsymiss

Format

A data frame with 581 rows and 9 variables:

anti

Antisocial behavior score (0–6).

self

Self-esteem score (9–24); 148 missing.

pov

Family in poverty (1 = yes, 0 = no); 150 missing.

childage

Child's age in years.

divorce

Mother divorced (1 = yes, 0 = no).

gender

Child's gender (1 = female, 0 = male).

momage

Mother's age at the child's birth.

momwork

Mother employed (1 = yes, 0 = no); 86 missing.

race

Race/ethnicity: 1 = neither Black nor Hispanic, 2 = Black, 3 = Hispanic; 113 missing.

Source

U.S. Bureau of Labor Statistics, National Longitudinal Survey of Youth, Children data, 1990.

References

Allison, P.D. (2001). Missing Data. Sage Publications.

Examples

summary(nlsymiss)
colSums(is.na(nlsymiss))

Print a glemb object

Description

Print a glemb object

Usage

## S3 method for class 'glemb'
print(x, digits = 3L, ...)

Arguments

Value

x, invisibly.

Examples

imp <- glemb(nlsymiss, m = 5, noms = c(race, pov, momwork, divorce, gender),
             output = "list", seed = 1)
print(imp)

Summarise a glemb object

Description

Prints a summary of the imputation model and the missingness in the first imputed dataset.

Usage

## S3 method for class 'glemb'
summary(object, digits = 3L, ...)

Arguments

Value

object, invisibly.

Examples

imp <- glemb(nlsymiss, m = 5, noms = c(race, pov, momwork, divorce, gender),
             output = "list", seed = 1)
summary(imp)