PredTest (version 0.1.0)

pred_weights: Calculate Predictive Weights

Description

This function calculates predictive weights by computing the inverse square sum of a correlation matrix derived from the specified variables. In 'group' analysis, it directly uses the variables for correlation. In 'prepost' analysis, it calculates the difference between two time points before correlation.

Usage

pred_weights(
  dataset,
  vars,
  gtvar,
  type = "group",
  id = NULL,
  pre = NULL,
  post = NULL,
  corr_method = "pearson"
)

Value

A numeric vector of predictive weights for each variable analyzed.

Arguments

dataset

A data frame containing the dataset to be analyzed.

vars

A vector of strings specifying the names of the variables to be used in the correlation analysis.

gtvar

The name of the categorical variable used to identify groups in 'prepost' type analysis.

type

The type of analysis. Valid values are 'group' for group-based correlation analysis or 'prepost' for pre-post analysis. Defaults to 'group'.

id

The variable in the dataset that uniquely identifies subjects in a 'prepost' analysis. This should not be NULL if type is 'prepost'.

pre

Specifies the baseline time point for 'prepost' analysis.

post

Specifies the follow-up time point for 'prepost' analysis.

corr_method

The method of correlation. Valid options are 'pearson', 'kendall', or 'spearman'. Defaults to 'pearson'.

Details

This function performs error handling to ensure appropriate input values and types. It calculates the correlation matrix for the specified variables and then computes the predictive weights as the inverse square sum of the correlation matrix.

Examples

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

# end points for variables
grp_endpts <- c(
    "mean_suv","blind_moca_uncorrected","craft_verbatim","craft_delay_verbatim",
    "number_span_forward","number_span_backward","fluency_f_words_correct",
    "oral_trail_part_a","oral_trail_part_b","fluency_animals","fluency_vegetables",
    "verbal_naming_no_cue"
    )
prepost_endpts <- c(
    "COPM_p", "COPM_s", "A1_work", "A2_work", "Grip_dom",
    "Grip_ndom",  "Flex_right", "Flex_left"
    )
# simple group
pred_weights(dataset=group_data_example, vars=c('v1', 'v2'), gtvar='group')
# simple prepost
pred_weights(dataset=pre_post_data_example, vars=c('v1','v2','v3'),
gtvar='time', id='ID', pre=0,post=12)
# simulated group
pred_weights(dataset=group_cog_data, vars=grp_endpts, gtvar="group.factor",
type="group",corr_method="pearson")
# simulated prepost
pred_weights(dataset=pre_post_fit, id="ID", vars=prepost_endpts,
gtvar="Time", type="prepost",pre=0, post=1, corr_method="pearson")