Estimates for a single-group design with a categorical outcome variable compared to a reference or population value.

Returns object estimate_pdiff_one is suitable for a single-group design (between subjects) with a categorical outcome variable. It calculates the effect sizes with respect to a reference or population proportion (default value of 0). It returns the estimated difference between the in proportion from the reference/population value. You can pass raw data or summary data.

Usage

estimate_pdiff_one(
  data = NULL,
  outcome_variable = NULL,
  comparison_cases = NULL,
  comparison_n = NULL,
  reference_p = 0,
  case_label = 1,
  outcome_variable_name = "My outcome variable",
  conf_level = 0.95,
  count_NA = FALSE
)

Arguments

data

For raw data - a dataframe or tibble

outcome_variable

For raw data - The column name of the outcome variable, which must be 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 >= count

reference_p

Reference proportion, numeric >=0 and <=1

case_label

An optional numeric or character label for the count level.

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.

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 an object of class esci_estimate

• overview

  • outcome_variable_name -

  • outcome_variable_level -

  • cases -

  • n -

  • P -

  • P_LL -

  • P_UL -

  • P_SE -

  • P_adjusted -

  • ta_LL -

  • ta_UL -

• es_proportion_difference

  • outcome_variable_name -

  • case_label -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • SE -

  • effect_size_adjusted -

  • ta_LL -

  • ta_UL -

  • cases -

  • n -

  • type -

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.prop1() (renamed ci.prop as of statpsych 1.6).

Examples

# From raw data
data("data_campus_involvement")

estimate_from_raw <- esci::estimate_pdiff_one(
  esci::data_campus_involvement,
  CommuterStatus,
  reference_p = 0.50
)

# 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_one(
  comparison_cases = 8,
  comparison_n = 22,
  reference_p = 0.5
)

# 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)