Estimates for a 2x2 mixed factorial design with a continuous outcome variable

Returns object estimate_mdiff_2x2_mixed is suitable for a 2x2 mixed-factorial design with a continuous outcome variable. It estimates each main effect, the simple effects for the repeated-measures factor, and the interaction. It can express these estimates as mean differences. This function accepts raw data only. Standardized mean differences are not (yet) available; stay tuned. Median differences are also not yet available.

Usage

estimate_mdiff_2x2_mixed(
  data,
  outcome_variable_level1,
  outcome_variable_level2,
  grouping_variable,
  outcome_variable_name = "My outcome variable",
  repeated_measures_name = "Time",
  conf_level = 0.95,
  save_raw_data = TRUE
)

Arguments

data: For raw data - a dataframe or tibble
outcome_variable_level1: The column name of the outcome variable for level 1 of the repeated-measures factor
outcome_variable_level2: The column name of the outcome variable for level 2 of the repeated-measures factor
grouping_variable: The column name of the grouping variable; only 2 levels allowed; must be a factor
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.
repeated_measures_name: Optional friendly name for the repeated measures factor. Defaults to 'Time'
conf_level: The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.
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 -
- effect_type -
- effects_complex -
- t -
- p -
es_smd
- outcome_variable_name -
- grouping_variable_name -
- effect -
- effect_size -
- LL -
- UL -
- numerator -
- denominator -
- SE -
- df -
- d_biased -
- effect_type -
- effects_complex -
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 -
- grouping_variable_A -
- grouping_variable_B -
- paired -

Details

Reach for this function in place of a 2x2 mixed-factorial ANOVA.

Once you generate an estimate with this function, you can visualize it with plot_mdiff() and you can visualize the interaction specifically with plot_interaction(). You can test hypotheses with test_mdiff().

The estimated mean differences are from statpsych::ci.2x2.mean.mixed().

Examples

# From raw data (summary data mode not available for this function)
example_data <- data.frame(
  pretest = c(
    19, 18, 19, 20, 17, 16, 16, 10, 12,  9, 13, 15
  ),
  posttest = c(
    18, 19, 20, 17, 20, 16, 19, 16, 16, 14, 16, 18
  ),
  condition = as.factor(
    c(
      rep("Control", times = 6),
      rep("Treated", times = 6)
    )
  )
)

estimates <- esci::estimate_mdiff_2x2_mixed(
  data = example_data,
  outcome_variable_level1 = pretest,
  outcome_variable_level2 = posttest,
  grouping_variable = condition,
  repeated_measures_name = "Time"
)
# To visualize the estimated mean difference for the interaction
myplot <- esci::plot_mdiff(estimates$interaction, effect_size = "mean")
#> 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.

# Line-plot of the interaction with fan effect representing each simple-effect CI
plot_interaction_line_CI <- esci::plot_interaction(
  estimates,
  show_CI = TRUE
)

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