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Estimates for a repeated-measures study with two measures of a categorical variable

Source: R/estimate_pdiff_paired.R
estimate_pdiff_paired.Rd

Returns object estimate_pdiff_paired is suitable for a simple paired design with a categorical outcome variable. It provides estimates and CIs for the population proportion difference between the repeated measures. You can pass raw data or summary data.

Usage

estimate_pdiff_paired(
  data = NULL,
  comparison_measure = NULL,
  reference_measure = NULL,
  cases_consistent = NULL,
  cases_inconsistent = NULL,
  not_cases_consistent = NULL,
  not_cases_inconsistent = NULL,
  case_label = 1,
  not_case_label = NULL,
  comparison_measure_name = "Comparison measure",
  reference_measure_name = "Reference measure",
  conf_level = 0.95,
  count_NA = FALSE
)

Arguments

data

For raw data - a data.frame or tibble

comparison_measure

For raw data - The comparison measure, a factor. Can be the column name of a data frame of a vector.

reference_measure

For raw data - The reference measure, a factor. Can be the column name of a data frame of a vector.

cases_consistent

Count of cases in measure 1 that are also cases at measure 2; measure 1 = 0, measure 2 = 0; cell 0_0

cases_inconsistent

Count of cases in measure 1 that are not cases at measure 2; measure 1 = 0, measure 2 = 1; cell 0_1

not_cases_consistent

Count of not cases in measure 1 that are also not cases at measure 2; measure 1 = 1, measure 2 = 1, cell 1_1

not_cases_inconsistent

Count of not cases in measure 1 that are not cases at measure 2; measure 1 = 1, measure 2 = 0, cell 1_0

case_label

An optional numeric or character label for the case level.

not_case_label

An optional numeric or character label for the not case level.

comparison_measure_name

For summary data - An optional character label for the comparison measure. Defaults to 'Comparison measure'

reference_measure_name

For summary data - An optional character label for the reference measure. Defaults to 'Reference measure'

conf_level

The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.

count_NA

Logical to count NAs (TRUE) in total N or not (FALSE)

Value

Returns object of class esci_estimate

Details

Once you generate an estimate with this function, you can visualize it with plot_pdiff() and you can test hypotheses with test_pdiff().

The estimated proportion differences are from statpsych::ci.prop.ps().

Examples

# From summary data
# Example 1 from Bonett & Price, 2012
estimate_from_summary <- esci::estimate_pdiff_paired(
  cases_consistent = 60,
  cases_inconsistent = 50,
  not_cases_inconsistent = 22,
  not_cases_consistent = 68,
  case_label = "Answered True",
  not_case_label = "Answered False",
  reference_measure_name = "9th grade",
  comparison_measure_name = "12th grade",
  conf_level = 0.95
)

# To visualize the estimate
myplot_from_summary <- esci::plot_pdiff(estimate_from_summary)
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.

# To conduct a hypothesis test
res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)