Validity Analysis

Source: R/validity_analysis.R

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

validity_analysis(
  data,
  criterion,
  practical,
  SESOI_lower = SESOI_lower_validity_func,
  SESOI_upper = SESOI_upper_validity_func,
  estimator_function = validity_estimators,
  control = model_control(),
  na.rm = FALSE
)

Arguments

data

Data frame
criterion

Character vector indicating column name in the data
practical

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

Function or numeric scalar. Default is SESOI_lower_validity_func
SESOI_upper

Function or numeric scalar. Default is SESOI_upper_validity_func
estimator_function

Function for providing validity estimators. Default is validity_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")

val_analysis <- validity_analysis(
  data = agreement_data,
  criterion = "Criterion_score.trial1",
  practical = "Practical_score.trial1",
  control = model_control(
    boot_type = "perc",
    boot_samples = 1000,
    seed = 1667
  )
)
#> Bootstraping: 1000 resamples
#> Calculating confidence intervals...
#> Done!

val_analysis
#> Bootstrap with 1000 resamples and 95% perc confidence intervals.
#> 
#>     estimator      value      lower      upper
#>   SESOI lower -1.0846145 -1.3126039 -0.7649283
#>   SESOI upper  1.0846145  0.7649283  1.3126039
#>   SESOI range  2.1692290  1.5298567  2.6252079
#>     Intercept -3.9394072 -7.7384917  0.6781176
#>         Slope  0.9494885  0.8613078  1.0270365
#>           RSE  0.8760378  0.6186607  1.0160992
#>   Pearson's r  0.9875619  0.9766460  0.9941832
#>     R Squared  0.9752786  0.9538374  0.9884002
#>  SESOI to RSE  2.4761819  1.8120685  3.6148831
#>          PPER  0.7692369  0.6237352  0.9134460
#>           SDC  1.8335682  1.2948716  2.1267200

plot(val_analysis)