Description Usage Arguments Value References Examples
Computes the MDC for each metric that is passed to the function as a vector of athlete measurements, for the subject and trial vectors, the ICC's of each metric, and the desired confidence level
1 |
subject |
The vector of athletes who recorded the results for each metric (can be a numeric or factor variable) |
trial |
The vector that represents which trial each measurement came from |
... |
Numeric vectors that represent the metrics for which the SEM should be computed. These vectors hold the scores that each athlete recorded for each respective metric (at least one metric must be passed to the function). |
ICC |
A vector of the ICC's for each of the metrics included in the "..." argument. This vector must contain the same number of elements as the number of metrics that have been passed to the function in the "..." argument, and the reliability values must appear in the same order as the metrics appear in the "..." argument. |
confidence |
The degree of confidence the user wants to have that an improvement exceeding the MDC can be interpreted as real change, and not the result of measurement error, based on the standard normal distribution. The default value is 0.95. |
A data frame, with the name of each metric situated above its calculated MDC
Riemann, B. L., & Lininger, M. R. (2018). Statistical Primer for Athletic Trainers: The Essentials of Understanding Measures of Reliability and Minimal Important Change. Journal of Athletic Training, 53(1), 98-103.
1 2 3 4 5 6 | subject <- c(1, 1, 1, 2, 2, 2, 3, 3, 3)
trial <- c(1, 2, 3, 1, 2, 3, 1, 2, 3)
metric_1 <- c(250, 258, 252, 279, 270, 277, 218, 213, 218)
metric_2 <- c(10, 7, 10, 14, 18, 17, 11, 7, 8)
metric_3 <- c(1214, 1276, 1289, 1037, 1010, 1069, 1481, 1465, 1443)
MDC(subject, trial, metric_1, metric_2, metric_3, ICC = c(0.92, 0.98, 0.95), confidence = 0.95)
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