Generates plots for TaskSurv, depending on argument type
:
"target"
: CallsGGally::ggsurv()
on asurvival::survfit()
object. This computes the Kaplan-Meier survival curve for the observations if this task."duo"
: Passes data and additional arguments down toGGally::ggduo()
.columnsX
is target,columnsY
is features."pairs"
: Passes data and additional arguments down toGGally::ggpairs()
. Color is set to target column.
Usage
# S3 method for class 'TaskSurv'
autoplot(
object,
type = "target",
theme = theme_minimal(),
reverse = FALSE,
...
)
Arguments
- object
(TaskSurv).
- type
(
character(1)
):
Type of the plot. See above for available choices.- theme
(
ggplot2::theme()
)
Theggplot2::theme_minimal()
is applied by default to all plots.- reverse
(
logical()
)
IfTRUE
andtype = 'target'
, it plots the Kaplan-Meier curve of the censoring distribution. Default isFALSE
.- ...
(
any
): Additional arguments.rhs
is passed down to$formula
of TaskSurv for stratification for type"target"
. Other arguments are passed to the respective underlying plot functions.
Value
ggplot2::ggplot()
object.
Examples
library(mlr3)
library(mlr3viz)
library(mlr3proba)
library(ggplot2)
task = tsk("lung")
head(fortify(task))
#> time status age meal.cal pat.karno ph.ecog ph.karno sex wt.loss
#> <int> <int> <int> <int> <int> <int> <int> <fctr> <int>
#> 1: 455 1 68 1225 90 0 90 m 15
#> 2: 210 1 57 1150 60 1 90 m 11
#> 3: 1022 0 74 513 80 1 50 m 0
#> 4: 310 1 68 384 60 2 70 f 10
#> 5: 361 1 71 538 80 2 60 f 1
#> 6: 218 1 53 825 80 1 70 m 16
autoplot(task) # KM
autoplot(task) # KM of the censoring distribution
autoplot(task, rhs = "sex")
autoplot(task, type = "duo")
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01
#> Warning: pseudoinverse used at -0.005
#> Warning: neighborhood radius 1.005
#> Warning: reciprocal condition number 0
#> Warning: There are other near singularities as well. 1.01