# Estimates for a repeated-measures study with two measures of a continuous variable Returns object `estimate_mdiff_paired` is suitable for a simple paired design with a continuous outcome variable. It provides estimates and CIs for the population mean difference between the repeated measures, the standardized mean difference (SMD; Cohen's d) between the repeated measures, and the median difference between the repeated measures (raw data only). You can pass raw data or summary data. ```r estimate_mdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, comparison_mean = NULL, comparison_sd = NULL, reference_mean = NULL, reference_sd = NULL, n = NULL, correlation = NULL, comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 0.95, save_raw_data = TRUE ) ``` ## Arguments - `data`: For raw data - a data frame or tibble - `comparison_measure`: For raw data - The column name of comparison measure of the outcome variable, or a vector of numeric data - `reference_measure`: For raw data - The column name of the reference measure of the outcome variable, or a vector of numeric data - `comparison_mean`: For summary data, a numeric - `comparison_sd`: For summary data, numeric > 0 - `reference_mean`: For summary data, a numeric - `reference_sd`: For summary data, numeric > 0 - `n`: For summary data, a numeric integer > 0 - `correlation`: For summary data, correlation between measures, a numeric that is > -1 and < 1 - `comparison_measure_name`: For summary data - An optional character label for the comparison measure. Defaults to 'Comparison measure' - `reference_measure_name`: For summary data - An optional character label for the reference measure. Defaults to 'Reference measure' - `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_difference * **type** - * **comparison_measure_name** - * **reference_measure_name** - * **effect** - * **effect_size** - * **LL** - * **UL** - * **SE** - * **df** - * **ta_LL** - * **ta_UL** - * es_smd * **comparison_measure_name** - * **reference_measure_name** - * **effect** - * **effect_size** - * **LL** - * **UL** - * **numerator** - * **denominator** - * **SE** - * **d_biased** - * **df** - * es_r * **x_variable_name** - * **y_variable_name** - * **effect** - * **effect_size** - * **LL** - * **UL** - * **SE** - * **n** - * **df** - * **ta_LL** - * **ta_UL** - * es_median_difference * **type** - * **comparison_measure_name** - * **reference_measure_name** - * **effect** - * **effect_size** - * **LL** - * **UL** - * **SE** - * **ta_LL** - * **ta_UL** - * es_mean_ratio * **comparison_measure_name** - * **reference_measure_name** - * **effect** - * **effect_size** - * **LL** - * **UL** - * **comparison_mean** - * **reference_mean** - * es_median_ratio * **comparison_measure_name** - * **reference_measure_name** - * **effect** - * **effect_size** - * **LL** - * **UL** - * **comparison_median** - * **reference_median** - * raw_data * **comparison_measure** - * **reference_measure** - ## Details Reach for this function in place of a paired-samples **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.mean.ps()`. The estimated SMDs are from `CI_smd_ind_contrast()`. The estimated median differences are from `statpsych::ci.median.ps()`. ## Examples ```r # From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_mdiff_paired( data = esci::data_thomason_1, comparison_measure = Posttest, reference_measure = Pretest ) # To visualize the estimated median difference (raw data only) 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", rope = c(-2, 2) ) sd1 <- 4.28 sd2 <- 3.4 sdiff <- 2.13 cor <- (sd1^2 + sd2^2 - sdiff^2) / (2*sd1*sd2) estimate_from_summary <- esci::estimate_mdiff_paired( comparison_mean = 14.25, comparison_sd = 4.28, reference_mean = 12.88, reference_sd = 3.4, n = 16, correlation = 0.87072223749, comparison_measure_name = "After", reference_measure_name = "Before" ) # To visualize the estimated mean difference myplot_from_summary <- 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) ) ```

estimate_mdiff_paired function