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

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

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 -

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()

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"
)
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> 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"
)


# 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")
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> 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_raw <- esci::test_mdiff(
  estimate_from_summary,
  effect_size = "mean"
)