estimate_mdiff_one {esci}

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

Description

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.

Usage

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

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)

Value

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 -

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)
)