estimate_pdiff_two {esci}

Estimates for a two-group study with a categorical outcome variable

Description

Returns object estimate_pdiff_two is suitable for a simple two-group design with a categorical outcome variable. It provides estimates and CIs for the difference in proportions between the two groups, the odds ratio, and phi. You can pass raw data or summary data.

Usage

estimate_pdiff_two(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable = NULL,
  comparison_cases = NULL,
  comparison_n = NULL,
  reference_cases = NULL,
  reference_n = NULL,
  case_label = 1,
  not_case_label = NULL,
  grouping_variable_levels = NULL,
  outcome_variable_name = "My outcome variable",
  grouping_variable_name = "My grouping variable",
  conf_level = 0.95,
  count_NA = FALSE
)

Arguments

data

For raw data - a data frame or tibble

outcome_variable

For raw data - The column name of the outcome variable which is a factor, or a vector that is a factor

grouping_variable

For raw data - The column name of the grouping variable which is a factor, or a vector that is a factor

comparison_cases

For summary data, a numeric integer >= 0

comparison_n

For summary data, a numeric integer >= comparison_events

reference_cases

For summary data, a numeric integer >= 0

reference_n

For summary data, a numeric integer >= reference_events

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.

grouping_variable_levels

For summary data - An optional vector of 2 group labels

outcome_variable_name

Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed.

grouping_variable_name

Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed.

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)

Details

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

The estimated mean differences are from statpsych::ci.prop2().

The estimated odds ratio is from statpsych::ci.oddsratio().

The estimated correlation (phi) is from statpsych::ci.phi().

Value

Returns object of class esci_estimate

• es_proportion_difference

  • type -

  • outcome_variable_name -

  • case_label -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • SE -

  • effect_size_adjusted -

  • ta_LL -

  • ta_UL -

• es_odds_ratio

  • outcome_variable_name -

  • case_label -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • SE -

  • LL -

  • UL -

  • ta_LL -

  • ta_UL -

• overview

  • grouping_variable_name -

  • grouping_variable_level -

  • outcome_variable_name -

  • outcome_variable_level -

  • cases -

  • n -

  • P -

  • P_LL -

  • P_UL -

  • P_SE -

  • P_adjusted -

  • ta_LL -

  • ta_UL -

• es_phi

  • grouping_variable_name -

  • outcome_variable_name -

  • effect -

  • effect_size -

  • SE -

  • LL -

  • UL -

Examples

data("data_campus_involvement")

estimate_from_raw <- esci::estimate_pdiff_two(
  esci::data_campus_involvement,
  CommuterStatus,
  Gender
)

# To visualize the estimate
myplot_from_raw <- esci::plot_pdiff(estimate_from_raw)

# To conduct a hypothesis test
res_htest_from_raw <- esci::test_pdiff(estimate_from_raw)


# From summary_data
estimate_from_summary <- esci::estimate_pdiff_two(
  comparison_cases = 10,
  comparison_n = 20,
  reference_cases = 78,
  reference_n = 252,
  grouping_variable_levels = c("Original", "Replication"),
  conf_level = 0.95
)

# To visualize the estimate
myplot_from_summary <- esci::plot_pdiff(estimate_from_summary)

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