validate_stats_dm: Validate a stats_dm Object

In dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models

Validate a stats_dm Object

Description

validate_stats_dm is an internal (i.e., not exported) generic function to ensure that stats_dm objects, as well as their specific subclasses (basic_stats, cafs, quantiles, delta_funs, sum_dist, and fit_stats), meet the necessary structural and column requirements. Each method performs class-specific validation checks.

Usage

validate_stats_dm(stat_df)

## S3 method for class 'basic_stats'
validate_stats_dm(stat_df)

## S3 method for class 'cafs'
validate_stats_dm(stat_df)

## S3 method for class 'quantiles'
validate_stats_dm(stat_df)

## S3 method for class 'delta_funs'
validate_stats_dm(stat_df)

## S3 method for class 'densities'
validate_stats_dm(stat_df)

## S3 method for class 'sum_dist'
validate_stats_dm(stat_df)

## S3 method for class 'fit_stats'
validate_stats_dm(stat_df)

## S3 method for class 'stats_dm'
validate_stats_dm(stat_df)

Arguments

stat_df

A data.frame of class stats_dm, basic_stats, cafs, quantiles, delta_funs, sum_dist, or fit_stats containing the calculated statistics to be validated.

Details

The validation process checks for required columns and structure based on the class of stat_df. Each class has specific requirements:

• validate_stats_dm.stats_dm: Ensures stat_df is a data.frame.

• validate_stats_dm.basic_stats: Checks for the presence of "Cond", exactly two columns with prefix ⁠"Mean_⁠, and exactly one column prefixed with "P_"

• validate_stats_dm.cafs: Checks for the presence of "Bin", "Cond", and exactly one column prefixed with "P_"

• validate_stats_dm.quantiles: Requires "Prob", "Cond", and exactly two columns prefixed with "Quant_"

• validate_stats_dm.delta_funs: Ensures "Prob" exists, at least two columns prefixed with "Quant_", and at least one column each Avg_ and Delta_

• validate_stats_dm.delta_funs: Ensures "Cond", "Time", and "Stat" exists, and at least two column with "Dens_".

• validate_stats_dm.sum_dist: Checks for a "Source" column. Here, it is also checked whether cell combinations appear equally often.

• validate_stats_dm.fit_stats: Checks for if the fit statistics summarize a log-likelihood cost function or the RMSE statistic. In the former case, the columns "Log_Like", "Neg_Log_Like", "AIC", and "BIC" are expected. In the latter case, the columns "RMSE_ms" and "RMSE_s" are expected.

Value

Returns the unmodified stat_df for convenience.

dRiftDM documentation built on Dec. 1, 2025, 5:08 p.m.