optCL: Calculate the Optimal Visual Testing Confidence Level

In psre: Presenting Statistical Results Effectively

Calculate the Optimal Visual Testing Confidence Level

Description

Calculates the Optimal Visual Testing (OVT) confidence level. The OVT level is a level you can use to make confidence intervals such that the overlapping (or non-overlapping) of confidence intervals preserves the pairwise testing results. That is, statistically different estimates have confidence intervals that do not overlap and statistically indistinguishable intervals have confidence intervals that do overlap. It does not always work perfectly, but it generally results in fewer inferential errors than the nominal level.

Usage

optCL(
  obj = NULL,
  b = NULL,
  v = NULL,
  level = 0.95,
  grid_range = c(0.75, 0.99),
  grid_length = 100,
  adjust = p.adjust.methods[c(8, 1:7)],
  print_message = TRUE,
  ...
)

Arguments

obj	A model object, on which coef and vcov can be called. Either obj and varname or b and v must be specified.
b	Optional vector of coefficients to be passed into the function. it overrides the coefficients in obj. Either obj or b and v must be specified.
v	Optional variance-covariance matrix. This can be specified even if obj and varname are specified. It replaces the variance-covaraince matrix from the model.
level	The confidence level to use for testing.
grid_range	The range of values over which to do the grid search.
grid_length	The number of values in the grid.
adjust	String giving the method used to adjust the p-values for multiplicity. All methods allowed in p.adjust.methods are permitted. None is the default.
print_message	Logical indicating whether the startup message directing users to a newer version of this function and package
...	Other arguments to be passed down to 'VizTest::viztest()'.

Value

A list (of class "viztest") with the following elements: 1. tab: a data frame with results from the grid search. The data frame has four variables: 'level' - is the confidence level used in the grid search; 'psame' - the proportion of (non-)overlaps that match the normal theory tests; 'pdiff' - the proportion of pairwise tests that are statistically significant; 'easy' - the ease with which the comparisons are made. 2. pw_tests: A logical vector indicating which tests are significantly significant. 3. ci_tests: A logical vector indicating whether the confidence intervals are disjoint ('TRUE') or overlap ('FALSE'). 4. combs: The pairwise combinations of stimuli used in the test. Note, the stimuli are reordered from largest to smallest, so the numbers do not represent the position in the original ordering. 5. param_names: A vector of the names of the parameters reordered by size - largest to smallest. 6. L: The lower confidence bounds from the grid search. 7. U: The upper confidence bounds from the grid search. 8. est: A data frame with the variables 'vbl' - the parameter name; 'est' - the parameter estimate; 'se' - the parameter standard error. 9. call: model call

Examples

data(wvs)
wvs$civ2 <- "Other"
wvs$civ2 <- ifelse(wvs$civ == 9, 
                   "Western", 
                   wvs$civ2)
wvs$civ2 <- ifelse(wvs$civ == 6, 
                   "Latin American", 
                   wvs$civ2)
wvs$civ2 <- as.factor(wvs$civ2)

intmod <- lm(resemaval ~ civ2 * pct_secondary, 
             data=wvs)

ss2 <- simple_slopes(intmod, 
                     "pct_secondary", 
                     "civ2")
o2 <- optCL(b=ss2$est$slope, v=ss2$v)

psre documentation built on Dec. 19, 2025, 1:07 a.m.