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Limits of Agreement with Linear Mixed Effects

Source: R/loa_lme.R
loa_lme.Rd

[Deprecated]

loa_lme() is deprecated, and for new code we recommend switching to tolerance_limit(), which fits the same random-intercept model with model = "lme", gives analytic prediction and tolerance limits (no bootstrap), and is still under active development. It takes the two measurements (x and y) rather than their difference and average. Supplying condition gives each condition its own residual variance, as het_var = TRUE does here.

This function allows for the calculation of (parametric) bootstrapped limits of agreement when there are multiple observations per subject.

Usage

loa_lme(
  diff,
  avg,
  condition = NULL,
  id,
  data,
  type = c("perc", "norm", "basic"),
  conf.level = 0.95,
  agree.level = 0.95,
  replicates = 999,
  prop_bias = FALSE,
  het_var = FALSE
)

Arguments

diff

Column name of the data frame that includes the difference between the 2 measurements of interest.

avg

Column name of the data frame that includes the average of the 2 measurements of interest.

condition

Column name indicating different conditions subjects were tested under. This can be left missing if there are no differing conditions to be tested.

id

Column name indicating the subject/participant identifier

data

A data frame containing the variables within the model.

type

A character string representing the type of bootstrap confidence intervals. Only "norm", "basic", and "perc" currently supported. Bias-corrected and accelerated, bca, is the default. See ?boot::boot.ci for more details.

conf.level

The confidence level required. Default is 95%.

agree.level

The agreement level required. Default is 95%.

replicates

The number of bootstrap replicates. Passed on to the boot function. Default is 999.

prop_bias

Logical indicator (default is FALSE) of whether proportional bias should be considered for the limits of agreement calculations.

het_var

Logical indicator (default is FALSE) of whether to assume homogeneity of variance in each condition.

Value

Returns single list with the results of the agreement analysis.

  • var_comp: Table of variance components

  • loa: A data frame of the limits of agreement including the average difference between the two sets of measurements, the standard deviation of the difference between the two sets of measurements and the lower and upper confidence limits of the difference between the two sets of measurements.

  • call: The matched call.

References

Parker, R. A., Weir, C. J., Rubio, N., Rabinovich, R., Pinnock, H., Hanley, J., McLoughan, L., Drost, E.M., Mantoani, L.C., MacNee, W., & McKinstry, B. (2016). "Application of mixed effects limits of agreement in the presence of multiple sources of variability: exemplar from the comparison of several devices to measure respiratory rate in COPD patients". PLOS One, 11(12), e0168321. doi:10.1371/journal.pone.0168321

See also

tolerance_limit()

Examples

# \donttest{
data("recpre_long")
# Instead of loa_lme(), use:
tolerance_limit(data = recpre_long, x = "PM", y = "AM", id = "id",
                condition = "trial_condition", model = "lme")
#> Agreement between Measures (Difference: x-y)
#> 95% CI for Bias; 95% Prediction Interval
#> Tolerance Limits: at least 95% of differences with 95% confidence
#> Model: random intercept for each id (lme); residual SD by condition
#> 
#>  Condition  Bias          Bias CI     SD Prediction Interval  Tolerance Limits
#>    23C/5.5 0.274 [0.0967, 0.4513] 0.2479   [-0.3141, 0.8621] [-0.5643, 1.1123]
#>    33C/5.5 0.239 [0.0948, 0.3832] 0.2016   [-0.2392, 0.7172] [-0.4426, 0.9206]
#>    33C/7.5 0.240 [0.0764, 0.4036] 0.2288   [-0.3028, 0.7828] [-0.5337, 1.0137]
#> 
#> 
# }