pred_results: Predictive Results Wrapper Function

In PredTest: Preparing Data For, and Calculating the Prediction Test

Predictive Results Wrapper Function

Description

This function is a wrapper that conditionally handles filtering by group or time, calculates the difference vector, and evaluates hypotheses to return a list of results.

Usage

pred_results(
  dataset,
  id = NULL,
  vars,
  type = "group",
  hypothesis,
  gtvar,
  grp_a,
  grp_b,
  location = "median",
  diff_method = "wilcoxon",
  phi_0 = 0.5
)

Arguments

dataset	A data frame for research.
id	The column that identifies unique subjects. This should be NULL if type is 'group' and should not be NULL if type is 'prepost'.
vars	The column variables of interest.
type	The type of study. Valid values are 'group' for group-based data and 'prepost' for pre-post data. Defaults to 'group'.
hypothesis	A vector or string of valid hypotheses: 'increase', 'decrease', or 'different'.
gtvar	The column of interest to divide the groups (e.g., time or treatment).
grp_a	The first subset of interest within the gtvar column (e.g., 'pre' or 'control').
grp_b	The second subset of interest within the gtvar column (e.g., 'post' or 'treatment').
location	The measure of central tendency to use for the difference calculation. Valid options are 'median' or 'mean'. Defaults to 'median'.
diff_method	The method to use for testing 'different' hypotheses. Valid options are 'wilcoxon' or 't'. Defaults to 'wilcoxon'.
phi_0	The decision rule threshold for the p-value. If p-value < phi_0, then there's sufficient evidence for a success for a difference. Defaults to 0.50.

Details

This function performs error handling to ensure appropriate input values and types. It then filters the data based on the study type, calculates the difference vector, and evaluates the hypotheses using the specified method.

Value

A list containing:

results

A vector of 0s and 1s indicating whether each hypothesis was correct.

differences

A vector of the differences between groups.

variables

The column variables used in the analysis.

Examples

data("group_data_example")
data("group_cog_data")
data("pre_post_data_example")
data("pre_post_fit")

# simple group analysis
pred_results(dataset=group_data_example, vars=c('v1', 'v2'),
hypothesis=c("increase", "different"), gtvar="group", grp_a="placebo", grp_b="drug")

# simple prepost analysis
pred_results(dataset=pre_post_data_example, id="ID", vars=c('v1', 'v2', 'v3'),
type="prepost", hypothesis="increase", gtvar="time", grp_a=0, grp_b=12)

# simulated group analysis
pred_results(dataset=group_cog_data, vars=c('blind_moca_uncorrected', 'craft_verbatim'),
type="group", hypothesis="decrease", gtvar="group.factor", grp_a="Control", grp_b="ESKD")

# simulated prepost analysis
pred_results(dataset=pre_post_fit, id="ID", vars=c('Flex_right', 'Flex_left'),
type="prepost", hypothesis="increase", gtvar="Time", grp_a=0, grp_b=1)

PredTest documentation built on Sept. 16, 2024, 5:06 p.m.