Calculate Observed Proportion of Concordant Pairs (OPCP)

Description

This function calculates the Observed Proportion of Concordant Pairs (OPCP) using Kendall's Tau as a measure of association. The pervasive functions also provide the OPCP.

Usage

OPCP(formula, data)

Arguments

Value

A numeric value representing the OPCP.

Examples

# Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_vars <- spi_sc |>
  dplyr::select(age, Agree, Consc, Neuro, Extra, Open)

formula <- age ~ Agree + Consc + Neuro + Extra + Open
OPCP(formula = formula, data = spi_sc_vars)

Calculate Observed Proportion of Concordant Pairs (OPCP)

Description

This function calculates the Observed Proportion of Concordant Pairs (OPCP) using Kendall's Tau as a measure of association. The pervasive functions also provide the OPCP.

Usage

OPCP_glm(formula, data)

Arguments

Value

A numeric value representing the OPCP.

Examples

#Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_vars <- spi_sc |>
  dplyr::select(sex, Agree, Consc, Neuro, Extra, Open)
  spi_sc_vars$sex = spi_sc_vars$sex -1

formula <- sex ~ Agree + Consc + Neuro + Extra + Open
OPCP_glm(formula = formula, data = spi_sc_vars)

Calculate Observed Proportion of Concordant Pairs (OPCP)

Description

This function provides a matrix that contains Pearson correlations in the lower triangle and Observed Proportions of Concordant Pairs (OPCPs) in the upper triangle between multiple variables.

Usage

OPCP_mat(data, round_digits = 2)

Arguments

Value

A matrix of correlations and OPCPs.

Examples

# Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_age_sex_B5 <- spi_sc |>
  dplyr::select(age, sex, Agree, Consc, Neuro, Extra, Open)


OPCP_mat(data = spi_sc_age_sex_B5)

Association Rule Mining With Dichotomized Data

Description

This function provides the same information as pervasive_tric but with dichotomized data instead of trichotomized data. Trichotomized data is generally to be preferred when possible. Researchers interested in mixing trichotomized variables with dichotomized variables should use the arules package directly.

Usage

pervasive_dic(formula, data, min_support = 0.03)

Arguments

Value

@return A list with the following components:

• OPCP: Observed proportion of concordant pairs.

• adj_r_squared: Adjusted R-squared value for the regression model.

• exact_match_lhs, exact_match_rhs: The left and right-hand side of the rule suggested by the regression model, respectively

• exact_match_quality: Quality metrics for the rule suggested by the regression.

• exact_match_lhs_opp, exact_match_rhs_opp: The left and right-hand side of the rule suggested by the low end of the regression model, respectively

• exact_match_quality_opp: Quality metrics for the rule suggested by the low end of the regression.

• top_rule_lhs, top_rule_rhs, top_rule_quality: Information relevant to the highest lift rule meeting min_support for high values of the dependent variable.

• top_rule_opp_lhs, top_rule_opp_rhs, top_rule_opp_quality: Information relevant to the highest lift rule meeting min_support for low values of the dependent variable.

• quality_table: A table summarizing the quality statistics for extracted association rules.

• freq_tables: Frequency tables (cutoffs and membership) for dichotomization binning.

Examples

# Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_vars <- spi_sc |>
  dplyr::select(age, Agree, Consc, Neuro, Extra, Open)

formula <- age ~ Agree + Consc + Neuro + Extra + Open
example <- pervasive_dic(formula = formula, data = spi_sc_vars)
example

Association Rule Mining With Dichotomized Data

Description

This function provides similar information as pervasive_dic() and pervasive_tric() but should be the preferred option when the outcome variable is binary.

Usage

pervasive_dic_glm(formula, data, min_support = 0.03)

Arguments

Value

@return A list with the following components:

• OPCP: Observed proportion of concordant pairs.

• adj_r_squared: Adjusted R-squared value for the regression model.

• exact_match_lhs, exact_match_rhs: The left and right-hand side of the rule suggested by the regression model, respectively

• exact_match_quality: Quality metrics for the rule suggested by the regression.

• exact_match_lhs_opp, exact_match_rhs_opp: The left and right-hand side of the rule suggested by the low end of the regression model, respectively

• exact_match_quality_opp: Quality metrics for the rule suggested by the low end of the regression.

• top_rule_lhs, top_rule_rhs, top_rule_quality: Information relevant to the highest lift rule meeting min_support for high values of the dependent variable.

• top_rule_opp_lhs, top_rule_opp_rhs, top_rule_opp_quality: Information relevant to the highest lift rule meeting min_support for low values of the dependent variable.

• quality_table: A table summarizing the quality statistics for extracted association rules.

• freq_tables: Frequency tables (cutoffs and membership) for dichotomization binning.

Examples

# Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_vars <- spi_sc |>
  dplyr::select(sex, Agree, Consc, Neuro, Extra, Open)
spi_sc_vars$sex = spi_sc_vars$sex -1

formula <- sex ~ Agree + Consc + Neuro + Extra + Open
example <- pervasive_dic_glm(formula = formula, data = spi_sc_vars)
example

Association Rule Mining With Trichotomized Data

Description

This function extracts a specific set of association rules and reports quality measures for these rules. The OPCP and adjusted R-square for the regression model analyzed are also reported for a fuller pervasiveness context of the regression.

Usage

pervasive_tric(formula, data, min_support = 0.03)

Arguments

Value

@return A list with the following components:

• OPCP: Observed proportion of concordant pairs.

• adj_r_squared: Adjusted R-squared value for the regression model.

• exact_match_lhs, exact_match_rhs: The left and right-hand side of the rule suggested by the regression model, respectively

• exact_match_quality: Quality metrics for the rule suggested by the regression.

• exact_match_lhs_opp, exact_match_rhs_opp: The left and right-hand side of the rule suggested by the low end of the regression model, respectively

• exact_match_quality_opp: Quality metrics for the rule suggested by the low end of the regression.

• top_rule_lhs, top_rule_rhs, top_rule_quality: Information relevant to the highest lift rule meeting min_support for high values of the dependent variable.

• top_rule_opp_lhs, top_rule_opp_rhs, top_rule_opp_quality: Information relevant to the highest lift rule meeting min_support for low values of the dependent variable.

• quality_table: A table summarizing the quality statistics for extracted association rules.

• freq_tables: Frequency tables (cutoffs and membership) for trichotomization binning.

Examples

# Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_vars <- spi_sc |>
  dplyr::select(age, Agree, Consc, Neuro, Extra, Open)

formula <- age ~ Agree + Consc + Neuro + Extra + Open
example <- pervasive_tric(formula = formula, data = spi_sc_vars)
#From the results, it appears we would be rather unlikely to meet individuals
#with the patterns of personality traits suggested for old and young people
#by a linear regression when data is trichotomized.

example

Association Rule Mining With Trichotomized Data and a Binary outcome

Description

This function extracts a specific set of association rules and reports quality measures for these rules. The OPCP for the logistic regression model analyzed is also reported for a fuller pervasiveness context of the regression.

Usage

pervasive_tric_glm(formula, data, min_support = 0.03)

Arguments

Value

@return A list with the following components:

• OPCP: Observed proportion of concordant pairs.

• adj_r_squared: Adjusted R-squared value for the regression model.

• exact_match_lhs, exact_match_rhs: The left and right-hand side of the rule suggested by the regression model, respectively

• exact_match_quality: Quality metrics for the rule suggested by the regression.

• exact_match_lhs_opp, exact_match_rhs_opp: The left and right-hand side of the rule suggested by the low end of the regression model, respectively

• exact_match_quality_opp: Quality metrics for the rule suggested by the low end of the regression.

• top_rule_lhs, top_rule_rhs, top_rule_quality: Information relevant to the highest lift rule meeting min_support for high values of the dependent variable.

• top_rule_opp_lhs, top_rule_opp_rhs, top_rule_opp_quality: Information relevant to the highest lift rule meeting min_support for low values of the dependent variable.

• quality_table: A table summarizing the quality statistics for extracted association rules.

• freq_tables: Frequency tables (cutoffs and membership) for trichotomization binning.

Examples

# Example using the spi dataset from the psychTools package
sc <- psych::scoreVeryFast(psychTools::spi.keys, psychTools::spi)
spi_sc <- cbind(psychTools::spi, sc)
spi_sc_vars <- spi_sc |>
  dplyr::select(age, Agree, Consc, Neuro, Extra, Open)

formula <- age ~ Agree + Consc + Neuro + Extra + Open
example <- pervasive_tric(formula = formula, data = spi_sc_vars)
#From the results, it appears we would be rather unlikely to meet
#individuals with the patterns of personality traits suggested for
#old and young people by a linear regression when data is trichotomized.
example