run_pca {autoMrP}

Apply PCA classifier to MrP.

Description

run_pca is a wrapper function that applies the PCA classifier to data provided by the user, evaluates prediction performance, and chooses the best-performing model.

Usage

run_pca(
  y,
  L1.x,
  L2.x,
  L2.unit,
  L2.reg,
  loss.unit,
  loss.fun,
  data,
  cores,
  verbose
)

Arguments

y

Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in survey.

L1.x

Individual-level covariates. A character vector containing the column names of the individual-level variables in survey and census used to predict outcome y. Note that geographic unit is specified in argument L2.unit.

L2.x

Context-level covariates. A character vector containing the column names of the context-level variables in survey and census used to predict outcome y. To exclude context-level variables, set L2.x = NULL.

L2.unit

Geographic unit. A character scalar containing the column name of the geographic unit in survey and census at which outcomes should be aggregated.

L2.reg

Geographic region. A character scalar containing the column name of the geographic region in survey and census by which geographic units are grouped (L2.unit must be nested within L2.reg). Default is NULL.

loss.unit

Loss function unit. A character-valued scalar indicating whether performance loss should be evaluated at the level of individual respondents (individuals), geographic units (L2 units) or at both levels. Default is c("individuals", "L2 units"). With multiple loss units, parameters are ranked for each loss unit and the loss unit with the lowest rank sum is chosen. Ties are broken according to the order in the search grid.

loss.fun

Loss function. A character-valued scalar indicating whether prediction loss should be measured by the mean squared error (MSE), the mean absolute error (MAE), binary cross-entropy (cross-entropy), mean squared false error (msfe), the f1 score (f1), or a combination thereof. Default is c("MSE", "cross-entropy","msfe", "f1"). With multiple loss functions, parameters are ranked for each loss function and the parameter combination with the lowest rank sum is chosen. Ties are broken according to the order in the search grid.

data

Data for cross-validation. A list of k data.frames, one for each fold to be used in k-fold cross-validation.

cores

The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1.

verbose

Verbose output. A logical argument indicating whether or not verbose output should be printed. Default is FALSE.

Value

A model formula of the winning best subset classifier model.