estimate_mdiff_one function

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

Returns object estimate_mdiff_one is suitable for a single-group design with a continuous outcome variable that is compared to a reference or population value. 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.

estimate_mdiff_one(
  data = NULL,
  outcome_variable = NULL,
  comparison_mean = NULL,
  comparison_sd = NULL,
  comparison_n = NULL,
  reference_mean = 0,
  outcome_variable_name = "My outcome variable",
  conf_level = 0.95,
  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
• 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: Reference value, defaults to 0
• 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.
• save_raw_data: For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object

Returns

Returns object of class esci_estimate

• overview

  • outcome_variable_name -
  • mean -
  • mean_LL -
  • mean_UL -
  • median -
  • median_LL -
  • median_UL -
  • sd -
  • min -
  • max -
  • q1 -
  • q3 -
  • n -
  • missing -
  • df -
  • mean_SE -
  • median_SE -

• es_mean

  • outcome_variable_name -
  • effect -
  • effect_size -
  • LL -
  • UL -
  • SE -
  • df -
  • ta_LL -
  • ta_UL -

• es_median

  • outcome_variable_name -
  • effect -
  • effect_size -
  • LL -
  • UL -
  • SE -
  • df -
  • ta_LL -
  • ta_UL -

• raw_data

  • grouping_variable -
  • outcome_variable -

• es_mean_difference

  • outcome_variable_name -
  • effect -
  • effect_size -
  • LL -
  • UL -
  • SE -
  • df -
  • ta_LL -
  • ta_UL -
  • type -

• es_median_difference

  • outcome_variable_name -
  • effect -
  • effect_size -
  • LL -
  • UL -
  • SE -
  • df -
  • ta_LL -
  • ta_UL -
  • type -

• es_smd

  • outcome_variable_name -
  • effect -
  • effect_size -
  • LL -
  • UL -
  • numerator -
  • denominator -
  • SE -
  • df -
  • d_biased -

Details

Reach for this function in place of a z-test or one-sample 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.mean1() (renamed ci.mean as of statpsych 1.6).

The estimated SMDs are from CI_smd_one().

The estimated median differences are from statpsych::ci.median1() (renamed ci.median as of statpsych 1.6)

Examples

# From raw data
data("data_penlaptop1")
estimate_from_raw <- esci::estimate_mdiff_one(
  data = data_penlaptop1[data_penlaptop1$condition == "Pen", ],
  outcome_variable = transcription,
  reference_mean = 10
)

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

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

# From summary data
mymean <- 12.09
mysd <- 5.52
myn <- 103

estimate_from_summary <- esci::estimate_mdiff_one(
  comparison_mean = mymean,
  comparison_sd = mysd,
  comparison_n = myn,
  reference_mean = 12
)

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

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