L0ggm (version 0.1.2)

auto_correlate: Automatic correlations

Description

Automatically computes the proper correlations between continuous and categorical variables. NA values are not treated as categories

Usage

auto_correlate(
  data,
  corr = c("kendall", "pearson", "spearman"),
  ordinal_categories = 7,
  forcePD = TRUE,
  na_data = c("pairwise", "listwise"),
  empty_method = c("none", "zero", "all"),
  empty_value = c("none", "point_five", "one_over"),
  forceReturn = FALSE,
  verbose = FALSE,
  ...
)

Value

A symmetric numeric matrix of dimension \(p \times p\), where \(p\) is the number of variables in data, with values in \([-1, 1]\) and ones on the diagonal. The correlation method used for each pair of variables depends on their types when corr = "pearson"

(the default): polychoric for ordinal-ordinal pairs, polyserial for ordinal-continuous pairs, and Pearson's for continuous-continuous pairs, where ordinal means having at most ordinal_categories unique values. When corr is "kendall" or "spearman", cor is used for all pairs regardless of variable type. Row and column names are inherited from data. When forcePD = TRUE (default) and the resulting matrix is not positive definite, the nearest positive definite matrix is returned via nearPD.

Arguments

data

Matrix or data frame. Should consist only of variables to be used in the analysis

corr

Character (length = 1). The standard correlation method to be used. Defaults to "pearson". Using "pearson" will compute polychoric, tetrachoric, polyserial, and biserial correlations for categorical and categorical/continuous correlations by default. To obtain "pearson" correlations regardless, use cor. Other options of "kendall" and "spearman" are provided for completeness and use cor

ordinal_categories

Numeric (length = 1). Up to the number of categories before a variable is considered continuous. Defaults to 7 categories before 8 is considered continuous

forcePD

Boolean (length = 1). Whether positive definite matrix should be enforced. Defaults to TRUE

na_data

Character (length = 1). How should missing data be handled? Defaults to "pairwise". Available options:

• "pairwise" --- Computes correlation for all available cases between two variables

• "listwise" --- Computes correlation for all complete cases in the dataset

empty_method

Character (length = 1). Method for empty cell correction in polychoric_matrix. Defaults to "none" Available options:

• "none" --- Adds no value (empty_value = "none") to the empirical joint frequency table between two variables

• "zero" --- Adds empty_value to the cells with zero in the joint frequency table between two variables

• "all" --- Adds empty_value to all in the joint frequency table between two variables

empty_value

Character (length = 1). Value to add to the joint frequency table cells in polychoric_matrix. Defaults to "none". Accepts numeric values between 0 and 1 or specific methods:

• "none" --- Adds no value (0) to the empirical joint frequency table between two variables

• "point_five" --- Adds 0.5 to the cells defined by empty_method

• "one_over" --- Adds 1 / n where n equals the number of cells based on empty_method. For empty_method = "zero", n equals the number of zero cells

forceReturn

Boolean (length = 1). Whether correlation matrix should be forced to return. Defaults to FALSE. Set to TRUE to receive the correlation matrix "as is"

verbose

Boolean (length = 1). Whether messages should be printed. Defaults to FALSE

...

Not actually used but makes it easier for general functionality in the package

Author

Alexander P. Christensen <[e-mail]>

Examples

# Obtain correlations
R <- auto_correlate(basic_smallworld)