estimate_mdiff_ind_contrast {esci}

Estimates for a multi-group design with a continuous outcome variable

Description

Returns object estimate_mdiff_ind_contrast is suitable for a multi-group design (between subjects) with a continuous outcome variable. It accepts a user-defined set of contrast weights that allows estimation of any 1-df contrast. It can express estimates as mean differences, standardized mean differences (Cohen's d) or median differences (raw data only). You can pass raw data or summary data.

Usage

estimate_mdiff_ind_contrast(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable = NULL,
  means = NULL,
  sds = NULL,
  ns = NULL,
  contrast = 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
)

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

means

For summary data - A vector of 2 or more means

sds

For summary data - A vector of standard deviations, same length as means

ns

For summary data - A vector of sample sizes, same length as means

contrast

A vector of group weights, same length as number of groups.

grouping_variable_levels

For summary data - An optional vector of group labels, same length as means

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

Details

Reach for this function in place of a one-way ANOVA.

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.lc.mean.bs().

The estimated SMDs are from CI_smd_ind_contrast() which relies on statpsych::ci.lc.stdmean.bs() unless there are only 2 groups.

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

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 -

• 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 -

Examples

# From raw data
data("data_rattanmotivation")

estimate_from_raw <- esci::estimate_mdiff_ind_contrast(
  esci::data_rattanmotivation,
  Motivation,
  Group,
  contrast = c("Challenge" = 1, "Control" = -1/2, "Comfort" = -1/2)
)

# To visualize the estimate
myplot_from_raw <- esci::plot_mdiff(
  estimate_from_raw,
  effect_size = "median"
)

# To conduct a hypothesis test
res_htest_from_raw <- esci::test_mdiff(
  estimate_from_raw,
  effect_size = "median"
)


# From summary data
data("data_halagappa")

estimate_from_summary <- estimate_mdiff_ind_contrast(
  means = data_halagappa$Mean,
  sds = data_halagappa$SD,
  ns = data_halagappa$n,
  grouping_variable_levels = as.character(data_halagappa$Groups),
  assume_equal_variance = TRUE,
  contrast = c(
    "NFree10" = 1/3,
    "AFree10" = 1/3,
    "ADiet10" = -1/3,
    "NFree17" = -1/3,
    "AFree17" = 1/3,
    "ADiet17" = -1/3
  ),
  grouping_variable_name = "Diet",
  outcome_variable_name = "% time near target"
)

# To visualize the estimate
myplot <- esci::plot_mdiff(estimate_from_summary, effect_size = "mean")

# To conduct a hypothesis test
res_htest_from_raw <- esci::test_mdiff(
  estimate_from_summary,
  effect_size = "mean"
)