SimplyAgree Update: v0.2.0
SimplyAgree Update: v0.2.0
Aaron R. Caldwell
Welcome to version 0.2.0 (July 2023)
I am happy to announce that an early release of the SimplyAgree. As
always, detailed documentation of the package can be found the
website. This update is mainly
about two new functions: tolerance_limit and agreement_limit. Additionally, both functions allow the user to apply a log transformation to the data and interpret the results on the back transformed ratio scale.
The newest version can be installed using devtools:
devtools::install_github("arcaldwell49/SimplyAgree")
Rationale
I have never really liked that agreement limits are calculated from 3
different functions so I have created 1 function, agreement_limit, to
calculate the 3 main types of agreement limits included in the package.
Additionally, users will have the option of using Bland-Altman style
confidence limits or Zou’s MOVER confidence limits.
The tolerance limits are inspired the recent work of Francq[1]. The tolerance limit is essentially a confidence limit on a prediction interval of the difference scores. While the concept might sound confusing the average user can just consider the prediction interval to be an equivalent of the agreement limit and the tolerance limit to be the equivalent of the confidence limits for agreement limits. This is advantageous for 2 reasons:
- Tolerance and prediction intervals often have better coverage
- Tolerance limits can be directly calculated from the estimated marginal means from a generalized least squares (GLS) model and are therefore more flexible
Agreement Limits
The agreement limits function is very simliar to the other “agree” functions in the package.
library(SimplyAgree)
data('reps')
# Simple
agreement_limit(x = "x", y ="y", data = reps)
MOVER Limits of Agreement (LoA)
95% LoA @ 5% Alpha-Level
Independent Data Points
Bias Bias CI Lower LoA Upper LoA LoA CI
0.4383 [-0.1669, 1.0436] -1.947 2.824 [-3.0117, 3.8884]
SD of Differences = 1.217
# Replicates
agreement_limit(x = "x", y ="y", data = reps, id = "id", data_type = "rep")
MOVER Limits of Agreement (LoA)
95% LoA @ 5% Alpha-Level
Data with Replicates
Bias Bias CI Lower LoA Upper LoA LoA CI
0.7152 [-1.5287, 2.9591] -2.232 3.662 [-7.5482, 8.9786]
SD of Differences = 1.5036
# Nested
agreement_limit(x = "x", y ="y", data = reps, id = "id", data_type = "nest")
MOVER Limits of Agreement (LoA)
95% LoA @ 5% Alpha-Level
Nested Data
Bias Bias CI Lower LoA Upper LoA LoA CI
0.7046 [-1.5572, 2.9664] -2.153 3.562 [-7.4979, 8.9071]
SD of Differences = 1.4581
Tolerance Limit
The tolerance limit function operates in a similar fashion. The only difference is that it cannot handle data with replicates explicitly.
data('reps')
# Simple
tolerance_limit(x = "x", y ="y", data = reps)
Agreement between Measures (Difference: x-y)
95% Prediction Interval with 95% Tolerance Limits
Bias Bias CI Prediction Interval Tolerance Limits
0.4383 [-0.1669, 1.0436] [-2.1998, 3.0764] [-2.993, 3.8697]
# Nested
tolerance_limit(x = "x", y ="y", data = reps, id = "id")
Agreement between Measures (Difference: x-y)
95% Prediction Interval with 95% Tolerance Limits
Bias Bias CI Prediction Interval Tolerance Limits
0.7046 [-1.5571, 2.9663] [-4.4686, 5.8778] [-8.6099, 10.0191]
Log transformation
Additionally, users can set the argument log_tf to TRUE, and this will
log-transform the raw data. This can be advantageous in situations where
the error is proportional to the mean (heteroscedascity). The
interpretation is slightly different since the data is on the log-scale.
When back transformed the differences between the two measures is on the
ratio scale (e.g., x/y versus x-y).
tolerance_limit(x = "x", y ="y",
data = reps, id = "id",
log_tf = TRUE)
Agreement between Measures (Ratio: x/y)
95% Prediction Interval with 95% Tolerance Limits
Bias Bias CI Prediction Interval Tolerance Limits
1.117 [0.7251, 1.7202] [0.4162, 2.9969] [0.1888, 6.6065]
Concluding Remarks
It has taken about half of this year to get tolerance limits into the package. If you have any questions, concerns, or have suggestions please feel free to reach out. I have a “Contact Me” form on my website, and feel free to send a message at any time. I would appreciate any feedback. I will probably wait a month before submitting to CRAN.
I hope you all enjoy the new SimplyAgree!
Cheers everyone,
AC
[1] Francq, B. G., Berger, M., & Boachie, C. (2020). To tolerate or to agree: A tutorial on tolerance intervals in method comparison studies with BivRegBLS R Package. Statistics in Medicine, 39(28), 4334-4349.