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

Source: R/estimate_mdiff_two.R
estimate_mdiff_two.Rd

Returns object estimate_mdiff_two is suitable for a simple two-group design with a continuous outcome variable. It provides estimates and CIs for the population mean difference between the repeated measures, the standardized mean difference (SMD; Cohen's d) between the repeated measures, and the median difference between the repeated measures (raw data only). You can pass raw data or summary data.

Usage

estimate_mdiff_two(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable = NULL,
  comparison_mean = NULL,
  comparison_sd = NULL,
  comparison_n = NULL,
  reference_mean = NULL,
  reference_sd = NULL,
  reference_n = NULL,
  grouping_variable_levels = NULL,
  outcome_variable_name = "My outcome variable",
  grouping_variable_name = "My grouping variable",
  conf_level = 0.95,
  assume_equal_variance = FALSE,
  save_raw_data = TRUE,
  switch_comparison_order = FALSE
)

Arguments

data

For raw data - a data.frame or tibble

outcome_variable

For raw data - The column name of the outcome variable, or a vector of numeric data

grouping_variable

For raw data - The column name of the grouping variable, or a vector of group names

comparison_mean

For summary data, a numeric

comparison_sd

For summary data, numeric > 0

comparison_n

For summary data, a numeric integer > 0

reference_mean

For summary data, a numeric

reference_sd

For summary data, numeric > 0

reference_n

For summary data, a numeric integer > 0

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.

assume_equal_variance

Defaults to FALSE

save_raw_data

For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object

switch_comparison_order

Defaults to FALSE

Value

Returns object of class esci_estimate

  • es_mean_difference

    • type -

    • outcome_variable_name -

    • grouping_variable_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • SE -

    • df -

    • ta_LL -

    • ta_UL -

  • es_median_difference

    • type -

    • outcome_variable_name -

    • grouping_variable_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • SE -

    • ta_LL -

    • ta_UL -

  • es_smd

    • outcome_variable_name -

    • grouping_variable_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • numerator -

    • denominator -

    • SE -

    • df -

    • d_biased -

  • es_mean_ratio

    • outcome_variable_name -

    • grouping_variable_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • comparison_mean -

    • reference_mean -

  • es_median_ratio

    • outcome_variable_name -

    • grouping_variable_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • comparison_median -

    • reference_median -

  • overview

    • outcome_variable_name -

    • grouping_variable_name -

    • grouping_variable_level -

    • mean -

    • mean_LL -

    • mean_UL -

    • median -

    • median_LL -

    • median_UL -

    • sd -

    • min -

    • max -

    • q1 -

    • q3 -

    • n -

    • missing -

    • df -

    • mean_SE -

    • median_SE -

  • raw_data

    • grouping_variable -

    • outcome_variable -

Details

Reach for this function in place of an independent-samples t-test.

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.mean2().

The estimated SMDs are from CI_smd_ind_contrast().

The estimated median differences are from statpsych::ci.median2().

Examples

# From raw data
data("data_penlaptop1")

estimate_from_raw <- esci::estimate_mdiff_two(
  data = data_penlaptop1,
  outcome_variable = transcription,
  grouping_variable = condition,
  switch_comparison_order = TRUE,
  assume_equal_variance = TRUE
)

# To visualize the estimated median difference (raw data only)
myplot_from_raw <- esci::plot_mdiff(
  estimate_from_raw,
  effect_size = "median"
)
#> 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_mdiff(
  estimate_from_raw,
  effect_size = "median",
  rope = c(-2, 2)
)


# From summary data
estimate_from_summary <- esci::estimate_mdiff_two(
  comparison_mean = 12.09,
  comparison_sd = 5.52,
  comparison_n = 103,
  reference_mean = 6.88,
  reference_sd = 4.22,
  reference_n = 48,
  grouping_variable_levels = c("Ref-Laptop", "Comp-Pen"),
  outcome_variable_name = "% Transcription",
  grouping_variable_name = "Note-taking type",
  assume_equal_variance = TRUE
)

# To visualize the estimated mean difference
myplot <- esci::plot_mdiff(
  estimate_from_summary,
  effect_size = "mean"
)
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.

# To conduct a hypothesis test
res_htest_from_summary <- esci::test_mdiff(
  estimate_from_summary,
  effect_size = "mean",
  rope = c(-2, 2)
)