plot.tune_vlmc | R Documentation |
This function plots the results of tune_vlmc()
or tune_covlmc()
.
## S3 method for class 'tune_vlmc'
plot(
x,
value = c("criterion", "likelihood"),
cutoff = c("quantile", "native"),
...
)
## S3 method for class 'tune_covlmc'
plot(
x,
value = c("criterion", "likelihood"),
cutoff = c("quantile", "native"),
...
)
x |
a |
value |
the criterion to plot (default "criterion"). |
cutoff |
the scale used for the cut off criterion (default "quantile") |
... |
additional parameters passed to |
The standard plot consists in showing the evolution of the criterion
used to select the model (AIC()
or BIC()
) as a function of the
cut off criterion expressed in the quantile scale (the quantile is used
by default to offer a common default behaviour between vlmc()
and
covlmc()
). Parameters can be used to display instead the loglikelihood()
of the model (by setting value="likelihood"
) and to use the native
scale for the cut off when available (by setting cutoff="native"
).
the tune_vlmc
object invisibly
The function sets several default before calling base::plot()
, namely:
type
: "l" by default to use a line representation;
xlab
: "Cut off (quantile scale)" by default, adapted to the actual
scale;
ylab
: the name of the criterion or "Log likelihood".
These parameters can be overridden by specifying other values when calling
the function. All parameters specified in addition to x
, value
and
cutoff
are passed to base::plot()
.
dts <- sample(as.factor(c("A", "B", "C")), 100, replace = TRUE)
tune_result <- tune_vlmc(dts)
## default plot
plot(tune_result)
## likelihood
plot(tune_result, value = "likelihood")
## parameters overriding
plot(tune_result,
value = "likelihood",
xlab = "Cut off", type = "b"
)
pc <- powerconsumption[powerconsumption$week %in% 10:12, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.5, 1))))
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
dts_best_model_tune <- tune_covlmc(dts, dts_cov, criterion = "AIC")
plot(dts_best_model_tune)
plot(dts_best_model_tune, value = "likelihood")
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