prioritylasso (version 0.3.1)

cvm_prioritylasso: prioritylasso with several block specifications

Description

Runs prioritylasso for a list of block specifications and gives the best results in terms of cv error.

Usage

cvm_prioritylasso(
  X,
  Y,
  weights,
  family,
  type.measure,
  blocks.list,
  max.coef.list = NULL,
  block1.penalization = TRUE,
  lambda.type = "lambda.min",
  standardize = TRUE,
  nfolds = 10,
  foldid,
  cvoffset = FALSE,
  cvoffsetnfolds = 10,
  ...
)

Value

object of class cvm_prioritylasso with the following elements. If these elements are lists, they contain the results for each penalized block of the best result.

lambda.ind

list with indices of lambda for lambda.type.

lambda.type

type of lambda which is used for the predictions.

lambda.min

list with values of lambda for lambda.type.

min.cvm

list with the mean cross-validated errors for lambda.type.

nzero

list with numbers of non-zero coefficients for lambda.type.

glmnet.fit

list of fitted glmnet objects.

name

a text string indicating type of measure.

block1unpen

if block1.penalization = FALSE, the results of either the fitted glm or coxph object.

best.blocks

character vector with the indices of the best block specification.

best.blocks.indices

list with the indices of the best block specification ordered by best to worst.

best.max.coef

vector with the number of maximal coefficients corresponding to best.blocks.

best.model

complete prioritylasso model of the best solution.

coefficients

coefficients according to the results obtained with best.blocks.

call

the function call.

Arguments

X

a (nxp) matrix of predictors with observations in rows and predictors in columns.

Y

n-vector giving the value of the response (either continuous, numeric-binary 0/1, or Surv object).

weights

observation weights. Default is 1 for each observation.

family

should be "gaussian" for continuous Y, "binomial" for binary Y, "cox" for Y of type Surv.

type.measure

accuracy/error measure computed in cross-validation. It should be "class" (classification error) or "auc" (area under the ROC curve) if family="binomial", "mse" (mean squared error) if family="gaussian" and "deviance" if family="cox" which uses the partial-likelihood.

blocks.list

list of the format list(list(bp1=...,bp2=...,), list(bp1=,...,bp2=...,), ...). For the specification of the entries, see prioritylasso.

max.coef.list

list of max.coef vectors. The first entries are omitted if block1.penalization = FALSE. Default is NULL.

block1.penalization

whether the first block should be penalized. Default is TRUE.

lambda.type

specifies the value of lambda used for the predictions. lambda.min gives lambda with minimum cross-validated errors. lambda.1se gives the largest value of lambda such that the error is within 1 standard error of the minimum. Note that lambda.1se can only be chosen without restrictions of max.coef.

standardize

logical, whether the predictors should be standardized or not. Default is TRUE.

nfolds

the number of CV procedure folds.

foldid

an optional vector of values between 1 and nfold identifying what fold each observation is in.

cvoffset

logical, whether CV should be used to estimate the offsets. Default is FALSE.

cvoffsetnfolds

the number of folds in the CV procedure that is performed to estimate the offsets. Default is 10. Only relevant if cvoffset=TRUE.

...

other arguments that can be passed to the function prioritylasso.

Author

Simon Klau
Maintainer: Roman Hornung ()

References

Klau, S., Jurinovic, V., Hornung, R., Herold, T., Boulesteix, A.-L. (2018). Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinformatics 19, 322

See Also

pl_data, prioritylasso, cvr2.ipflasso

Examples

cvm_prioritylasso(X = matrix(rnorm(50*500),50,500), Y = rnorm(50), family = "gaussian",
                  type.measure = "mse", lambda.type = "lambda.min", nfolds = 5,
                  blocks.list = list(list(bp1=1:75, bp2=76:200, bp3=201:500),
                                     list(bp1=1:75, bp2=201:500, bp3=76:200)))
if (FALSE) {
cvm_prioritylasso(X = pl_data[,1:1028], Y = pl_data[,1029], family = "binomial",
                  type.measure = "auc", standardize = FALSE, block1.penalization = FALSE,
                  blocks.list = list(list(1:4, 5:9, 10:28, 29:1028),
                                     list(1:4, 5:9, 29:1028, 10:28)),
                  max.coef.list = list(c(Inf, Inf, Inf, 10), c(Inf, Inf, 10, Inf)))}