Construct a Likelihood Object for Normal Data

Creates a likelihood object from a numeric variable, optionally grouped by a categorical variable. Or, optionally use another numeric variable to explore the relationship.

Usage

inz_lnorm(data, primary, secondary = NULL)

Arguments

data

A data frame containing the variables.

primary

The primary numeric variable of interest.

secondary

An optional secondary variable of interest (categorical for grouping or numeric for regression).

Value

Returns an object of class "inz_lnorm".

An object of class "inz_lnorm" is a list which contains the following:

For a single numeric variable:

x

the numeric variable.

summary_stat

the summary statistics of the variable.

x_bar

the sample mean.

n

the sample size.

raw_sum_sq

the raw sum of squares.

primary_var

the name of the numeric variable.

For numeric and categorical:

grouped_data

the numeric variable grouped by the categorical variable.

summary_stat

the summary statistics for each group.

x_bar

a vector of sample means for each group.

n

a vector of sample sizes for each group.

raw_sum_sq

a vector of the raw sum of squares for each group.

primary_var

the name of the numeric variable.

secondary_var

the name of the categorical variable.

For numeric and numeric:

x

the explanatory/independent variable.

y

the response/dependent variable.

spearman_correlation

Spearman's rank correlation coefficient.

x_label

the name of the explanatory variable.

y_label

the name of the response variable.

Details

primary and secondary arguments can be passed as a string (e.g. "Height") or as a non-string (e.g. Height).

The function behaves differently depending on the input.

Single variable - numeric: Computes summary statistics for the numeric variable.

Two variables - numeric and categorical: Groups the numeric variable by the categorical variable and computes summary statistics for each group. The function automatically identifies the variable which is numeric and uses it as the primary variable regardless of the order in which the variables are inputted.

Two variables - numeric and numeric: Computes Spearman's correlation. The secondary variable is used as the explanatory variable and the primary variable is used as the response variable for regression.

Examples

# Single variable
if (FALSE) inz_lnorm(surf_data, Hours) # \dontrun{}

# Numeric and Categorical (grouped data)
if (FALSE) inz_lnorm(surf_data, Income, Qualification) # \dontrun{}
if (FALSE) inz_lnorm(surf_data, Qualification, Income) # \dontrun{} # gives the same output

# Numeric and Numeric (regression)
if (FALSE) inz_lnorm(surf_data, Income, Hours) # \dontrun{}