`loa_lme.Rd`

This function allows for the calculation of (parametric) bootstrapped limits of agreement when there are multiple observations per subject.
The package author recommends using `tolerance_limit`

as an alternative to this function.

```
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
)
```

- 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.

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.

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. https://doi.org/10.1371/journal.pone.0168321