corMatrix: Correlation matrix to be used for regression based imputatoin

In annafil/FFCRegressionImputation: Several predictive approaches to imputation using linear regressions and lasso based approach for data in the Fragile Families Challenge

Description

Creates a dataframe with imputed values using either linear regression or lasso based models. For each variable in given data frame, the function finds the best correlated predictors (number of which is set by top_predictors), and uses these to construct models for predicting missing values.

Usage

corMatrix(dataframe = "", continuous = "", categorical = "",
  varpattern = "", debug = 0, test = 0, parallel = 0)

Arguments

dataframe	(Required) An input dataframe you would like a correlation matrix for.
continuous	(Optional) A character vector containing variable names that should be treated as continuous.
categorical	(Optional) A character vector containing variable names that should be treated as categorical.
varpattern	A regular expression for subsetting variable names from input dataframe.
debug	Debug mode; shows which models are running, the quality of predictions relative to original data, and any model errors. 1=progress notifications, errors and warnings.
test	(deprecated)
parallel	(deprecated)

Value

A correlation matrix between all variables given in the input dataframe after removing columns with no variance and applying any regex filters given.

Examples

## Not run: corMatrix(dataframe, debug=1)

annafil/FFCRegressionImputation documentation built on May 12, 2019, 1:59 p.m.