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.

Usage

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)

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 -

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)
#> 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_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)
#> 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)