estimate_pdiff_ind_contrast function

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

Returns object estimate_pdiff_ind_contrast is suitable for a multi-group design (between subjects) with a categorical outcome variable. It accepts a user-defined set of contrast weights that allows estimation of any 1-df contrast. It can express estimates as a difference in proportions and as an odds ratio (2-group designs only). You can pass raw data or summary data.

estimate_pdiff_ind_contrast(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable = NULL,
  cases = NULL,
  ns = NULL,
  contrast = NULL,
  case_label = 1,
  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
• cases: For summary data - A numeric vector of 2 or more event counts, each an integer >= 0
• ns: For summary data - A numeric vector of sample sizes, same length as counts, each an integer >= corresponding event count
• contrast: A vector of group weights, same length as number of groups.
• case_label: An optional numeric or character label For summary data, used as the label and defaults to 'Affected'. For raw data, used to specify the level used for the proportion.
• grouping_variable_levels: For summary data - An optional vector of group labels, same length as cases
• 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)

Returns

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 -

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 proportion differences are from statpsych::ci.lc.prop.bs().

The estimated odds ratios (if returned) are from statpsych::ci.oddsratio().

Examples

# From raw data
data("data_campus_involvement")

estimate_from_raw <- esci::estimate_pdiff_ind_contrast(
  esci::data_campus_involvement,
  CommuterStatus,
  Gender,
  contrast = c("Male" = -1, "Female" = 1)
)

# 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_ind_contrast(
  cases = c(78, 10),
  ns = c(252, 20),
  case_label = "egocentric",
  grouping_variable_levels = c("Original", "Replication"),
  contrast = c(-1, 1),
  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)