egg_outliers | R Documentation |
egg_model()
.Based on computed area under the curves (i.e., egg_aucs()
)
and slopes (i.e., egg_slopes()
) for several intervals using
a model fitted by egg_model()
, compute an outlier detection.
For details, see methods iqr
and zscore
of performance::check_outliers()
.
egg_outliers(
fit,
period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17),
knots = c(1, 8, 12),
from = c("predicted", "observed"),
start = 0.25,
end = 10,
step = 0.01,
filter = NULL,
outlier_method = "iqr",
outlier_threshold = list(iqr = 2)
)
fit |
A model object from a statistical model
such as from a call to |
period |
The intervals knots on which slopes are to be computed. |
knots |
The knots as defined |
from |
A string indicating the type of data to be used for the AP and AR computation, either "predicted" or "observed". Default is "predicted". |
start |
The start of the time window to compute AP and AR. |
end |
The end of the time window to compute AP and AR. |
step |
The step to increment the sequence. |
filter |
A string following |
outlier_method |
The outlier detection method(s). Default is |
outlier_threshold |
A list containing the threshold values for each method (e.g.,
|
A data.frame
listing the individuals which are not outliers based on several criteria.
data("bmigrowth")
res <- egg_model(
formula = log(bmi) ~ age,
data = bmigrowth[bmigrowth[["sex"]] == 0, ],
id_var = "ID",
random_complexity = 1
)
head(egg_outliers(
fit = res,
period = c(0, 0.5, 1.5, 3.5, 6.5, 10, 12, 17),
knots = c(1, 8, 12)
)[Outlier != 0])
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