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Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.priority_lasso")
lrn("surv.priority_lasso")

Meta Information

  • Task type: “surv”

  • Predict Types: “lp”, “response”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, mlr3proba, prioritylasso

Parameters

IdTypeDefaultLevelsRange
blocksuntyped-
max.coefuntyped-
block1.penalizationlogicalTRUETRUE, FALSE-
lambda.typecharacterlambda.minlambda.min, lambda.1se-
standardizelogicalTRUETRUE, FALSE-
nfoldsinteger5\([1, \infty)\)
foldiduntyped-
cvoffsetlogicalFALSETRUE, FALSE-
cvoffsetnfoldsinteger10\([1, \infty)\)
alignmentcharacterlambdalambda, fraction-
alphanumeric1\([0, 1]\)
bignumeric9.9e+35\((-\infty, \infty)\)
devmaxnumeric0.999\([0, 1]\)
dfmaxinteger-\([0, \infty)\)
epsnumeric1e-06\([0, 1]\)
epsnrnumeric1e-08\([0, 1]\)
excludeuntyped--
exmxnumeric250\((-\infty, \infty)\)
fdevnumeric1e-05\([0, 1]\)
gammauntyped--
groupedlogicalTRUETRUE, FALSE-
interceptlogicalTRUETRUE, FALSE-
keeplogicalFALSETRUE, FALSE-
lambdauntyped--
lambda.min.rationumeric-\([0, 1]\)
lower.limitsuntyped- , Inf-
maxitinteger100000\([1, \infty)\)
mnlaminteger5\([1, \infty)\)
mxitinteger100\([1, \infty)\)
mxitnrinteger25\([1, \infty)\)
nlambdainteger100\([1, \infty)\)
offsetuntyped-
parallellogicalFALSETRUE, FALSE-
penalty.factoruntyped--
pmaxinteger-\([0, \infty)\)
pminnumeric1e-09\([0, 1]\)
precnumeric1e-10\((-\infty, \infty)\)
predict.gammanumericgamma.1se\((-\infty, \infty)\)
relaxlogicalFALSETRUE, FALSE-
snumericlambda.1se\([0, 1]\)
standardize.responselogicalFALSETRUE, FALSE-
threshnumeric1e-07\([0, \infty)\)
trace.itinteger0\([0, 1]\)
type.gaussiancharacter-covariance, naive-
type.logisticcharacterNewtonNewton, modified.Newton-
type.multinomialcharacterungroupedungrouped, grouped-
upper.limitsuntypedInf-

References

Simon K, Vindi J, Roman H, Tobias H, Anne-Laure B (2018). “Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.” BMC Bioinformatics, 19. doi:10.1186/s12859-018-2344-6 .

See also

Author

HarutyunyanLiana

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPriorityLasso

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method selected_features()

Selected features, i.e. those where the coefficient is positive.

Usage

LearnerSurvPriorityLasso$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvPriorityLasso$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.priority_lasso")
print(learner)
#> <LearnerSurvPriorityLasso:surv.priority_lasso>: Priority Lasso
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, prioritylasso
#> * Predict Types:  lp, [response]
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights

# available parameters:
learner$param_set$ids()
#>  [1] "blocks"               "max.coef"             "block1.penalization" 
#>  [4] "lambda.type"          "standardize"          "nfolds"              
#>  [7] "foldid"               "cvoffset"             "cvoffsetnfolds"      
#> [10] "alignment"            "alpha"                "big"                 
#> [13] "devmax"               "dfmax"                "eps"                 
#> [16] "epsnr"                "exclude"              "exmx"                
#> [19] "fdev"                 "gamma"                "grouped"             
#> [22] "intercept"            "keep"                 "lambda"              
#> [25] "lambda.min.ratio"     "lower.limits"         "maxit"               
#> [28] "mnlam"                "mxit"                 "mxitnr"              
#> [31] "nlambda"              "offset"               "parallel"            
#> [34] "penalty.factor"       "pmax"                 "pmin"                
#> [37] "prec"                 "predict.gamma"        "relax"               
#> [40] "s"                    "standardize.response" "thresh"              
#> [43] "trace.it"             "type.gaussian"        "type.logistic"       
#> [46] "type.multinomial"     "upper.limits"