Reliability Analysis

Source: R/reliability_analysis.R

reliability_analysis represents a wrapper function for the bmbstats function. reliability_analysis runs the bootstrap reliability analysis of the data data frame using estimator_function to return the estimators

reliability_analysis(
  data,
  trial1,
  trial2,
  SESOI_lower = SESOI_lower_reliability_func,
  SESOI_upper = SESOI_upper_reliability_func,
  estimator_function = reliability_estimators,
  control = model_control(),
  na.rm = FALSE
)

Arguments

data

Data frame
trial1

Character vector indicating column name in the data
trial2

Character vector indicating column name(s) in the data
SESOI_lower

Function or numeric scalar. Default is SESOI_lower_reliability_func
SESOI_upper

Function or numeric scalar. Default is SESOI_upper_reliability_func
estimator_function

Function for providing reliability estimators. Default is reliability_estimators
control

Control object returned from model_control function. Use boot_type, boot_samples, boot_strata, and confidence to setup bootstrap.
na.rm

Should NAs be removed? Default is FALSE

Value

Object of class bmbstats

Examples

data("agreement_data")

rel_analysis <- reliability_analysis(
  data = agreement_data,
  trial1 = "Practical_score.trial1",
  trial2 = "Practical_score.trial2",
  control = model_control(
    boot_type = "perc",
    boot_samples = 1000,
    seed = 1667
  )
)
#> Bootstraping: 1000 resamples
#> Calculating confidence intervals...
#> Done!

rel_analysis
#> Bootstrap with 1000 resamples and 95% perc confidence intervals.
#> 
#>     estimator      value      lower      upper
#>   SESOI lower -1.1802122 -1.4096624 -0.8515401
#>   SESOI upper  1.1802122  0.8515401  1.4096624
#>   SESOI range  2.3604244  1.7030803  2.8193248
#>     Intercept  4.3847186 -1.9395860  8.7328415
#>         Slope  0.9170754  0.8265514  1.0405939
#>           RSE  1.3749629  0.8584588  1.7824954
#>   Pearson's r  0.9718531  0.9425451  0.9890019
#>     R Squared  0.9444984  0.8883912  0.9781248
#>  SESOI to RSE  1.7167185  1.1541459  2.7827378
#>          PPER  0.5986068  0.4293347  0.8198081
#>            TE  0.9722456  0.6070220  1.2604146
#>           SDC  2.8778305  1.7967749  3.7308058

plot(rel_analysis)