Package 'priorityelasticnet' reference manual

Title:	Comprehensive Analysis of Multi-Omics Data Using an Offset-Based Method
Description:	Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) <doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.
Authors:	Laila Qadir Musib [aut, cre], Eunice Carrasquinha [aut], Helena Mouriño [aut]
Maintainer:	Laila Qadir Musib <[email protected]>
License:	GPL-3
Version:	0.1.0
Built:	2025-01-14 06:11:19 UTC
Source:	https://github.com/cran/priorityelasticnet

Calculates the offsets for the current block

Description

Calculates the offsets for the current block

Usage

calculate_offsets(
  current_missings,
  current_observations,
  mcontrol,
  current_block,
  pred,
  liste,
  X,
  blocks,
  current_intercept
)
calculate_offsets(
  current_missings,
  current_observations,
  mcontrol,
  current_block,
  pred,
  liste,
  X,
  blocks,
  current_intercept
)

Arguments

`current_missings`	index vector (indices) of current missing observations
`current_observations`	index vector (indices) of current used observations
`mcontrol`	control for missing data handling
`current_block`	index of current block
`pred`	predictions of current block
`liste`	list with offsets
`X`	original data
`blocks`	information which variable belongs to which block
`current_intercept`	the intercept estimated for the current block

Value

List with offsets, used imputation model and the blocks used for the imputation model (if applicable)

Extract coefficients from a priorityelasticnet object

Description

Extract coefficients from a priorityelasticnet object

Usage

## S3 method for class 'priorityelasticnet'
coef(object, ...)
## S3 method for class 'priorityelasticnet'
coef(object, ...)

Arguments

`object`	model of type priorityelasticnet
`...`	additional arguments, currently not used

Value

List with the coefficients and the intercepts

Compare the rows of a matrix with a pattern

Description

Compare the rows of a matrix with a pattern

Usage

compare_boolean(object, pattern)
compare_boolean(object, pattern)

Arguments

`object`	matrix
`pattern`	pattern which is compared against the rows of the matrix

Value

logical vector if the pattern matches the rows

priorityelasticnet with several block specifications

Description

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

Usage

cvm_priorityelasticnet(
  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,
  alpha = 1,
  ...
)
cvm_priorityelasticnet(
  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,
  alpha = 1,
  ...
)

Arguments

`X`	A numeric matrix of predictors.
`Y`	A response vector. For family = "multinomial", Y should be a factor with more than two levels.
`weights`	Optional observation weights. Default is NULL.
`family`	A character string specifying the model type. Options are "gaussian", "binomial", "cox", and "multinomial". Default is "gaussian".
`type.measure`	Loss function for cross-validation. Options are "mse", "deviance", "class", "auc". Default depends on the family.
`blocks.list`	list of the format `list(list(bp1=...,bp2=...,), list(bp1=,...,bp2=...,), ...)`. For the specification of the entries, see `priorityelasticnet`.
`max.coef.list`	list of `max.coef` vectors. The first entries are omitted if `block1.penalization = FALSE`. Default is `NULL`.
`block1.penalization`	Logical. If FALSE, the first block will not be penalized. Default is TRUE.
`lambda.type`	Type of lambda to select. Options are "lambda.min" or "lambda.1se". Default is "lambda.min".
`standardize`	Logical flag for variable standardization, prior to fitting the model. Default is TRUE.
`nfolds`	Number of folds for cross-validation. Default is 10.
`foldid`	Optional vector of values between 1 and `nfolds` identifying what fold each observation is in. Default is NULL.
`cvoffset`	Logical. If TRUE, a cross-validated offset is used. Default is FALSE.
`cvoffsetnfolds`	Number of folds for cross-validation of the offset. Default is 10.
`alpha`	Elastic net mixing parameter. The elastic net penalty is defined as $(1 - \alpha)/2\|\|\beta\|\|_2^2 + \alpha\|\|\beta\|\|_1$ Defaults to 1 (lasso penalty).
`...`	other arguments that can be passed to the function `priorityelasticnet`.

Value

object of class cvm_priorityelasticnet 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 priorityelasticnet model of the best solution.
coefficients: coefficients according to the results obtained with best.blocks.
call: the function call.

Note

The function description and the first example are based on the R package ipflasso.

Construct control structures for handling of missing data for `priorityelasticnet`

Description

Construct control structures for handling of missing data for priorityelasticnet

Usage

missing.control(
  handle.missingdata = c("none", "ignore", "impute.offset"),
  offset.firstblock = c("zero", "intercept"),
  impute.offset.cases = c("complete.cases", "available.cases"),
  nfolds.imputation = 10,
  lambda.imputation = c("lambda.min", "lambda.1se"),
  perc.comp.cases.warning = 0.3,
  threshold.available.cases = 30,
  select.available.cases = c("maximise.blocks", "max")
)
missing.control(
  handle.missingdata = c("none", "ignore", "impute.offset"),
  offset.firstblock = c("zero", "intercept"),
  impute.offset.cases = c("complete.cases", "available.cases"),
  nfolds.imputation = 10,
  lambda.imputation = c("lambda.min", "lambda.1se"),
  perc.comp.cases.warning = 0.3,
  threshold.available.cases = 30,
  select.available.cases = c("maximise.blocks", "max")
)

Arguments

`handle.missingdata`	how blockwise missing data should be treated. Default is `none` which does nothing, `ignore` ignores the observations with missing data for the current block, `impute.offset` imputes the offset for the missing values.
`offset.firstblock`	determines if the offset of the first block for missing observations is zero or the intercept of the observed values for `handle.missingdata = ignore`
`impute.offset.cases`	which cases/observations should be used for the imputation model to impute missing offsets. Supported are complete cases (additional constraint is that every observation can only contain one missing block) and all available observations which have an overlap with the current block.
`nfolds.imputation`	nfolds for the glmnet of the imputation model
`lambda.imputation`	which lambda-value should be used for predicting the imputed offsets in cv.glmnet
`perc.comp.cases.warning`	percentage of complete cases when a warning is issued of too few cases for the imputation model
`threshold.available.cases`	if the number of available cases for `impute.offset.cases = available.cases` is below this threshold, `priorityelasticnet` tries to reduce the number of blocks taken into account for the imputation model to increase the number of observations used for the imputation model.
`select.available.cases`	determines how the blocks which are used for the imputation model are selected when `impute.offset.cases = available.cases`. `max` selects the blocks that maximise the number of observations, `maximise.blocks` tries to include as many blocks as possible, starting with the blocks with the hightes priority

Value

list with control parameters

Simulated Patient Data for Binary Classification

Description

This dataset contains simulated data for a binary classification problem, representing patient data with clinical, proteomics, and RNA variables. The data is organized into three blocks of variables: clinical variables, proteomics variables, and RNA variables. The outcome is a binary variable generated based on a logistic function.

Usage

Pen_Data
Pen_Data

Format

A data frame with 406 rows and 325 columns:

Clinical_Var1: Numeric variable representing age.
Clinical_Var2: Binary variable representing gender (0 = male, 1 = female).
Clinical_Var3: Categorical variable representing race (values 0, 1, 2, or 3).
Clinical_Var4: Binary variable representing ethnicity (0 or 1).
Clinical_Var5: Binary variable representing radiation therapy status (0 or 1).
Proteomic_Var1: Continuous variable representing a proteomic measurement.
Proteomic_Var2: Continuous variable representing a proteomic measurement.
Proteomic_Var3: Continuous variable representing a proteomic measurement.
Proteomic_Var4: Continuous variable representing a proteomic measurement.
Proteomic_Var5: Continuous variable representing a proteomic measurement.
Proteomic_Var6: Continuous variable representing a proteomic measurement.
Proteomic_Var7: Continuous variable representing a proteomic measurement.
Proteomic_Var8: Continuous variable representing a proteomic measurement.
Proteomic_Var9: Continuous variable representing a proteomic measurement.
Proteomic_Var10: Continuous variable representing a proteomic measurement.
Proteomic_Var11: Continuous variable representing a proteomic measurement.
Proteomic_Var12: Continuous variable representing a proteomic measurement.
Proteomic_Var13: Continuous variable representing a proteomic measurement.
Proteomic_Var14: Continuous variable representing a proteomic measurement.
Proteomic_Var15: Continuous variable representing a proteomic measurement.
Proteomic_Var16: Continuous variable representing a proteomic measurement.
Proteomic_Var17: Continuous variable representing a proteomic measurement.
Proteomic_Var18: Continuous variable representing a proteomic measurement.
Proteomic_Var19: Continuous variable representing a proteomic measurement.
Proteomic_Var20: Continuous variable representing a proteomic measurement.
Proteomic_Var21: Continuous variable representing a proteomic measurement.
Proteomic_Var22: Continuous variable representing a proteomic measurement.
Proteomic_Var23: Continuous variable representing a proteomic measurement.
Proteomic_Var24: Continuous variable representing a proteomic measurement.
Proteomic_Var25: Continuous variable representing a proteomic measurement.
Proteomic_Var26: Continuous variable representing a proteomic measurement.
Proteomic_Var27: Continuous variable representing a proteomic measurement.
Proteomic_Var28: Continuous variable representing a proteomic measurement.
Proteomic_Var29: Continuous variable representing a proteomic measurement.
Proteomic_Var30: Continuous variable representing a proteomic measurement.
Proteomic_Var31: Continuous variable representing a proteomic measurement.
Proteomic_Var32: Continuous variable representing a proteomic measurement.
Proteomic_Var33: Continuous variable representing a proteomic measurement.
Proteomic_Var34: Continuous variable representing a proteomic measurement.
Proteomic_Var35: Continuous variable representing a proteomic measurement.
Proteomic_Var36: Continuous variable representing a proteomic measurement.
Proteomic_Var37: Continuous variable representing a proteomic measurement.
Proteomic_Var38: Continuous variable representing a proteomic measurement.
Proteomic_Var39: Continuous variable representing a proteomic measurement.
Proteomic_Var40: Continuous variable representing a proteomic measurement.
Proteomic_Var41: Continuous variable representing a proteomic measurement.
Proteomic_Var42: Continuous variable representing a proteomic measurement.
Proteomic_Var43: Continuous variable representing a proteomic measurement.
Proteomic_Var44: Continuous variable representing a proteomic measurement.
Proteomic_Var45: Continuous variable representing a proteomic measurement.
Proteomic_Var46: Continuous variable representing a proteomic measurement.
Proteomic_Var47: Continuous variable representing a proteomic measurement.
Proteomic_Var48: Continuous variable representing a proteomic measurement.
Proteomic_Var49: Continuous variable representing a proteomic measurement.
Proteomic_Var50: Continuous variable representing a proteomic measurement.
Proteomic_Var51: Continuous variable representing a proteomic measurement.
Proteomic_Var52: Continuous variable representing a proteomic measurement.
Proteomic_Var53: Continuous variable representing a proteomic measurement.
Proteomic_Var54: Continuous variable representing a proteomic measurement.
Proteomic_Var55: Continuous variable representing a proteomic measurement.
Proteomic_Var56: Continuous variable representing a proteomic measurement.
Proteomic_Var57: Continuous variable representing a proteomic measurement.
Proteomic_Var58: Continuous variable representing a proteomic measurement.
Proteomic_Var59: Continuous variable representing a proteomic measurement.
Proteomic_Var60: Continuous variable representing a proteomic measurement.
Proteomic_Var61: Continuous variable representing a proteomic measurement.
Proteomic_Var62: Continuous variable representing a proteomic measurement.
Proteomic_Var63: Continuous variable representing a proteomic measurement.
Proteomic_Var64: Continuous variable representing a proteomic measurement.
Proteomic_Var65: Continuous variable representing a proteomic measurement.
Proteomic_Var66: Continuous variable representing a proteomic measurement.
Proteomic_Var67: Continuous variable representing a proteomic measurement.
Proteomic_Var68: Continuous variable representing a proteomic measurement.
Proteomic_Var69: Continuous variable representing a proteomic measurement.
Proteomic_Var70: Continuous variable representing a proteomic measurement.
Proteomic_Var71: Continuous variable representing a proteomic measurement.
Proteomic_Var72: Continuous variable representing a proteomic measurement.
Proteomic_Var73: Continuous variable representing a proteomic measurement.
Proteomic_Var74: Continuous variable representing a proteomic measurement.
Proteomic_Var75: Continuous variable representing a proteomic measurement.
Proteomic_Var76: Continuous variable representing a proteomic measurement.
Proteomic_Var77: Continuous variable representing a proteomic measurement.
Proteomic_Var78: Continuous variable representing a proteomic measurement.
Proteomic_Var79: Continuous variable representing a proteomic measurement.
Proteomic_Var80: Continuous variable representing a proteomic measurement.
Proteomic_Var81: Continuous variable representing a proteomic measurement.
Proteomic_Var82: Continuous variable representing a proteomic measurement.
Proteomic_Var83: Continuous variable representing a proteomic measurement.
Proteomic_Var84: Continuous variable representing a proteomic measurement.
Proteomic_Var85: Continuous variable representing a proteomic measurement.
Proteomic_Var86: Continuous variable representing a proteomic measurement.
Proteomic_Var87: Continuous variable representing a proteomic measurement.
Proteomic_Var88: Continuous variable representing a proteomic measurement.
Proteomic_Var89: Continuous variable representing a proteomic measurement.
Proteomic_Var90: Continuous variable representing a proteomic measurement.
Proteomic_Var91: Continuous variable representing a proteomic measurement.
Proteomic_Var92: Continuous variable representing a proteomic measurement.
Proteomic_Var93: Continuous variable representing a proteomic measurement.
Proteomic_Var94: Continuous variable representing a proteomic measurement.
Proteomic_Var95: Continuous variable representing a proteomic measurement.
Proteomic_Var96: Continuous variable representing a proteomic measurement.
Proteomic_Var97: Continuous variable representing a proteomic measurement.
Proteomic_Var98: Continuous variable representing a proteomic measurement.
Proteomic_Var99: Continuous variable representing a proteomic measurement.
Proteomic_Var100: Continuous variable representing a proteomic measurement.
Proteomic_Var101: Continuous variable representing a proteomic measurement.
Proteomic_Var102: Continuous variable representing a proteomic measurement.
Proteomic_Var103: Continuous variable representing a proteomic measurement.
Proteomic_Var104: Continuous variable representing a proteomic measurement.
Proteomic_Var105: Continuous variable representing a proteomic measurement.
Proteomic_Var106: Continuous variable representing a proteomic measurement.
Proteomic_Var107: Continuous variable representing a proteomic measurement.
Proteomic_Var108: Continuous variable representing a proteomic measurement.
Proteomic_Var109: Continuous variable representing a proteomic measurement.
Proteomic_Var110: Continuous variable representing a proteomic measurement.
Proteomic_Var111: Continuous variable representing a proteomic measurement.
Proteomic_Var112: Continuous variable representing a proteomic measurement.
Proteomic_Var113: Continuous variable representing a proteomic measurement.
Proteomic_Var114: Continuous variable representing a proteomic measurement.
Proteomic_Var115: Continuous variable representing a proteomic measurement.
Proteomic_Var116: Continuous variable representing a proteomic measurement.
Proteomic_Var117: Continuous variable representing a proteomic measurement.
Proteomic_Var118: Continuous variable representing a proteomic measurement.
Proteomic_Var119: Continuous variable representing a proteomic measurement.
Proteomic_Var120: Continuous variable representing a proteomic measurement.
Proteomic_Var121: Continuous variable representing a proteomic measurement.
Proteomic_Var122: Continuous variable representing a proteomic measurement.
Proteomic_Var123: Continuous variable representing a proteomic measurement.
Proteomic_Var124: Continuous variable representing a proteomic measurement.
Proteomic_Var125: Continuous variable representing a proteomic measurement.
Proteomic_Var126: Continuous variable representing a proteomic measurement.
Proteomic_Var127: Continuous variable representing a proteomic measurement.
Proteomic_Var128: Continuous variable representing a proteomic measurement.
Proteomic_Var129: Continuous variable representing a proteomic measurement.
Proteomic_Var130: Continuous variable representing a proteomic measurement.
Proteomic_Var131: Continuous variable representing a proteomic measurement.
Proteomic_Var132: Continuous variable representing a proteomic measurement.
Proteomic_Var133: Continuous variable representing a proteomic measurement.
Proteomic_Var134: Continuous variable representing a proteomic measurement.
Proteomic_Var135: Continuous variable representing a proteomic measurement.
Proteomic_Var136: Continuous variable representing a proteomic measurement.
Proteomic_Var137: Continuous variable representing a proteomic measurement.
Proteomic_Var138: Continuous variable representing a proteomic measurement.
Proteomic_Var139: Continuous variable representing a proteomic measurement.
Proteomic_Var140: Continuous variable representing a proteomic measurement.
Proteomic_Var141: Continuous variable representing a proteomic measurement.
Proteomic_Var142: Continuous variable representing a proteomic measurement.
Proteomic_Var143: Continuous variable representing a proteomic measurement.
Proteomic_Var144: Continuous variable representing a proteomic measurement.
Proteomic_Var145: Continuous variable representing a proteomic measurement.
Proteomic_Var146: Continuous variable representing a proteomic measurement.
Proteomic_Var147: Continuous variable representing a proteomic measurement.
Proteomic_Var148: Continuous variable representing a proteomic measurement.
Proteomic_Var149: Continuous variable representing a proteomic measurement.
Proteomic_Var150: Continuous variable representing a proteomic measurement.
Proteomic_Var151: Continuous variable representing a proteomic measurement.
Proteomic_Var152: Continuous variable representing a proteomic measurement.
Proteomic_Var153: Continuous variable representing a proteomic measurement.
Proteomic_Var154: Continuous variable representing a proteomic measurement.
Proteomic_Var155: Continuous variable representing a proteomic measurement.
Proteomic_Var156: Continuous variable representing a proteomic measurement.
Proteomic_Var157: Continuous variable representing a proteomic measurement.
Proteomic_Var158: Continuous variable representing a proteomic measurement.
Proteomic_Var159: Continuous variable representing a proteomic measurement.
Proteomic_Var160: Continuous variable representing a proteomic measurement.
Proteomic_Var161: Continuous variable representing a proteomic measurement.
Proteomic_Var162: Continuous variable representing a proteomic measurement.
Proteomic_Var163: Continuous variable representing a proteomic measurement.
Proteomic_Var164: Continuous variable representing a proteomic measurement.
Proteomic_Var165: Continuous variable representing a proteomic measurement.
Proteomic_Var166: Continuous variable representing a proteomic measurement.
Proteomic_Var167: Continuous variable representing a proteomic measurement.
Proteomic_Var168: Continuous variable representing a proteomic measurement.
Proteomic_Var169: Continuous variable representing a proteomic measurement.
Proteomic_Var170: Continuous variable representing a proteomic measurement.
Proteomic_Var171: Continuous variable representing a proteomic measurement.
Proteomic_Var172: Continuous variable representing a proteomic measurement.
Proteomic_Var173: Continuous variable representing a proteomic measurement.
Proteomic_Var174: Continuous variable representing a proteomic measurement.
RNA_Var1: Continuous variable representing an RNA measurement.
RNA_Var2: Continuous variable representing an RNA measurement.
RNA_Var3: Continuous variable representing an RNA measurement.
RNA_Var4: Continuous variable representing an RNA measurement.
RNA_Var5: Continuous variable representing an RNA measurement.
RNA_Var6: Continuous variable representing an RNA measurement.
RNA_Var7: Continuous variable representing an RNA measurement.
RNA_Var8: Continuous variable representing an RNA measurement.
RNA_Var9: Continuous variable representing an RNA measurement.
RNA_Var10: Continuous variable representing an RNA measurement.
RNA_Var11: Continuous variable representing an RNA measurement.
RNA_Var12: Continuous variable representing an RNA measurement.
RNA_Var13: Continuous variable representing an RNA measurement.
RNA_Var14: Continuous variable representing an RNA measurement.
RNA_Var15: Continuous variable representing an RNA measurement.
RNA_Var16: Continuous variable representing an RNA measurement.
RNA_Var17: Continuous variable representing an RNA measurement.
RNA_Var18: Continuous variable representing an RNA measurement.
RNA_Var19: Continuous variable representing an RNA measurement.
RNA_Var20: Continuous variable representing an RNA measurement.
RNA_Var21: Continuous variable representing an RNA measurement.
RNA_Var22: Continuous variable representing an RNA measurement.
RNA_Var23: Continuous variable representing an RNA measurement.
RNA_Var24: Continuous variable representing an RNA measurement.
RNA_Var25: Continuous variable representing an RNA measurement.
RNA_Var26: Continuous variable representing an RNA measurement.
RNA_Var27: Continuous variable representing an RNA measurement.
RNA_Var28: Continuous variable representing an RNA measurement.
RNA_Var29: Continuous variable representing an RNA measurement.
RNA_Var30: Continuous variable representing an RNA measurement.
RNA_Var31: Continuous variable representing an RNA measurement.
RNA_Var32: Continuous variable representing an RNA measurement.
RNA_Var33: Continuous variable representing an RNA measurement.
RNA_Var34: Continuous variable representing an RNA measurement.
RNA_Var35: Continuous variable representing an RNA measurement.
RNA_Var36: Continuous variable representing an RNA measurement.
RNA_Var37: Continuous variable representing an RNA measurement.
RNA_Var38: Continuous variable representing an RNA measurement.
RNA_Var39: Continuous variable representing an RNA measurement.
RNA_Var40: Continuous variable representing an RNA measurement.
RNA_Var41: Continuous variable representing an RNA measurement.
RNA_Var42: Continuous variable representing an RNA measurement.
RNA_Var43: Continuous variable representing an RNA measurement.
RNA_Var44: Continuous variable representing an RNA measurement.
RNA_Var45: Continuous variable representing an RNA measurement.
RNA_Var46: Continuous variable representing an RNA measurement.
RNA_Var47: Continuous variable representing an RNA measurement.
RNA_Var48: Continuous variable representing an RNA measurement.
RNA_Var49: Continuous variable representing an RNA measurement.
RNA_Var50: Continuous variable representing an RNA measurement.
RNA_Var51: Continuous variable representing an RNA measurement.
RNA_Var52: Continuous variable representing an RNA measurement.
RNA_Var53: Continuous variable representing an RNA measurement.
RNA_Var54: Continuous variable representing an RNA measurement.
RNA_Var55: Continuous variable representing an RNA measurement.
RNA_Var56: Continuous variable representing an RNA measurement.
RNA_Var57: Continuous variable representing an RNA measurement.
RNA_Var58: Continuous variable representing an RNA measurement.
RNA_Var59: Continuous variable representing an RNA measurement.
RNA_Var60: Continuous variable representing an RNA measurement.
RNA_Var61: Continuous variable representing an RNA measurement.
RNA_Var62: Continuous variable representing an RNA measurement.
RNA_Var63: Continuous variable representing an RNA measurement.
RNA_Var64: Continuous variable representing an RNA measurement.
RNA_Var65: Continuous variable representing an RNA measurement.
RNA_Var66: Continuous variable representing an RNA measurement.
RNA_Var67: Continuous variable representing an RNA measurement.
RNA_Var68: Continuous variable representing an RNA measurement.
RNA_Var69: Continuous variable representing an RNA measurement.
RNA_Var70: Continuous variable representing an RNA measurement.
RNA_Var71: Continuous variable representing an RNA measurement.
RNA_Var72: Continuous variable representing an RNA measurement.
RNA_Var73: Continuous variable representing an RNA measurement.
RNA_Var74: Continuous variable representing an RNA measurement.
RNA_Var75: Continuous variable representing an RNA measurement.
RNA_Var76: Continuous variable representing an RNA measurement.
RNA_Var77: Continuous variable representing an RNA measurement.
RNA_Var78: Continuous variable representing an RNA measurement.
RNA_Var79: Continuous variable representing an RNA measurement.
RNA_Var80: Continuous variable representing an RNA measurement.
RNA_Var81: Continuous variable representing an RNA measurement.
RNA_Var82: Continuous variable representing an RNA measurement.
RNA_Var83: Continuous variable representing an RNA measurement.
RNA_Var84: Continuous variable representing an RNA measurement.
RNA_Var85: Continuous variable representing an RNA measurement.
RNA_Var86: Continuous variable representing an RNA measurement.
RNA_Var87: Continuous variable representing an RNA measurement.
RNA_Var88: Continuous variable representing an RNA measurement.
RNA_Var89: Continuous variable representing an RNA measurement.
RNA_Var90: Continuous variable representing an RNA measurement.
RNA_Var91: Continuous variable representing an RNA measurement.
RNA_Var92: Continuous variable representing an RNA measurement.
RNA_Var93: Continuous variable representing an RNA measurement.
RNA_Var94: Continuous variable representing an RNA measurement.
RNA_Var95: Continuous variable representing an RNA measurement.
RNA_Var96: Continuous variable representing an RNA measurement.
RNA_Var97: Continuous variable representing an RNA measurement.
RNA_Var98: Continuous variable representing an RNA measurement.
RNA_Var99: Continuous variable representing an RNA measurement.
RNA_Var100: Continuous variable representing an RNA measurement.
RNA_Var101: Continuous variable representing an RNA measurement.
RNA_Var102: Continuous variable representing an RNA measurement.
RNA_Var103: Continuous variable representing an RNA measurement.
RNA_Var104: Continuous variable representing an RNA measurement.
RNA_Var105: Continuous variable representing an RNA measurement.
RNA_Var106: Continuous variable representing an RNA measurement.
RNA_Var107: Continuous variable representing an RNA measurement.
RNA_Var108: Continuous variable representing an RNA measurement.
RNA_Var109: Continuous variable representing an RNA measurement.
RNA_Var110: Continuous variable representing an RNA measurement.
RNA_Var111: Continuous variable representing an RNA measurement.
RNA_Var112: Continuous variable representing an RNA measurement.
RNA_Var113: Continuous variable representing an RNA measurement.
RNA_Var114: Continuous variable representing an RNA measurement.
RNA_Var115: Continuous variable representing an RNA measurement.
RNA_Var116: Continuous variable representing an RNA measurement.
RNA_Var117: Continuous variable representing an RNA measurement.
RNA_Var118: Continuous variable representing an RNA measurement.
RNA_Var119: Continuous variable representing an RNA measurement.
RNA_Var120: Continuous variable representing an RNA measurement.
RNA_Var121: Continuous variable representing an RNA measurement.
RNA_Var122: Continuous variable representing an RNA measurement.
RNA_Var123: Continuous variable representing an RNA measurement.
RNA_Var124: Continuous variable representing an RNA measurement.
RNA_Var125: Continuous variable representing an RNA measurement.
RNA_Var126: Continuous variable representing an RNA measurement.
RNA_Var127: Continuous variable representing an RNA measurement.
RNA_Var128: Continuous variable representing an RNA measurement.
RNA_Var129: Continuous variable representing an RNA measurement.
RNA_Var130: Continuous variable representing an RNA measurement.
RNA_Var131: Continuous variable representing an RNA measurement.
RNA_Var132: Continuous variable representing an RNA measurement.
RNA_Var133: Continuous variable representing an RNA measurement.
RNA_Var134: Continuous variable representing an RNA measurement.
RNA_Var135: Continuous variable representing an RNA measurement.
RNA_Var136: Continuous variable representing an RNA measurement.
RNA_Var137: Continuous variable representing an RNA measurement.
RNA_Var138: Continuous variable representing an RNA measurement.
RNA_Var139: Continuous variable representing an RNA measurement.
RNA_Var140: Continuous variable representing an RNA measurement.
RNA_Var141: Continuous variable representing an RNA measurement.
RNA_Var142: Continuous variable representing an RNA measurement.
RNA_Var143: Continuous variable representing an RNA measurement.
RNA_Var144: Continuous variable representing an RNA measurement.
RNA_Var145: Continuous variable representing an RNA measurement.
Pen_out: Binary outcome variable generated using a logistic function applied to a linear predictor based on the combined variables.

Predictions from priorityelasticnet

Description

Makes predictions for a priorityelasticnet object. It can be chosen between linear predictors or fitted values.

Usage

## S3 method for class 'priorityelasticnet'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  handle.missingtestdata = c("none", "omit.prediction", "set.zero", "impute.block"),
  include.allintercepts = FALSE,
  use.blocks = "all",
  alpha = 1,
  ...
)
## S3 method for class 'priorityelasticnet'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  handle.missingtestdata = c("none", "omit.prediction", "set.zero", "impute.block"),
  include.allintercepts = FALSE,
  use.blocks = "all",
  alpha = 1,
  ...
)

Arguments

`object`	An object of class `priorityelasticnet`.
`newdata`	(nnew `x` p) matrix or data frame with new values.
`type`	Specifies the type of predictions. `link` gives the linear predictors for all types of response and `response` gives the fitted values.
`handle.missingtestdata`	Specifies how to deal with missing data in the test data; possibilities are `none`, `omit.prediction`, `set.zero` and `impute.block`
`include.allintercepts`	should the intercepts from all blocks included in the prediction? If `FALSE`, only the intercept from the first block is included (default in the past).
`use.blocks`	determines which blocks are used for the prediction, the default is all. Otherwise one can specify the number of blocks which are used in a vector
`alpha`	Elastic net mixing parameter used in the model fitting.
`...`	Further arguments passed to or from other methods.

Details

handle.missingtestdata specifies how to deal with missing data. The default none cannot handle missing data, omit.prediction does not make a prediction for observations with missing values and return NA. set.zero ignores the missing data for the calculation of the prediction (the missing value is set to zero). impute.block uses an imputation model to impute the offset of a missing block. This only works if the priorityelasticnet object was fitted with handle.missingdata = "impute.offset". If impute.offset.cases = "complete.cases" was used, then every observation can have only one missing block. For observations with more than one missing block, NA is returned. If impute.offset.cases = "available.cases" was used, the missingness pattern in the test data has to be the same as in the train data. For observations with an unknown missingness pattern, NA is returned.

Value

Predictions that depend on type.

Examples


pl_bin <- priorityelasticnet(X = matrix(rnorm(50*190),50,190), Y = rbinom(50,1,0.5),
                       family = "binomial", type.measure = "auc",
                       blocks = list(block1=1:13,block2=14:80, block3=81:190),
                       block1.penalization = TRUE, lambda.type = "lambda.min",
                       standardize = FALSE, nfolds = 3, alpha = 1)

newdata_bin <- matrix(rnorm(10*190),10,190)

predict(object = pl_bin, newdata = newdata_bin, type = "response", alpha = 1)

pl_bin <- priorityelasticnet(X = matrix(rnorm(50*190),50,190), Y = rbinom(50,1,0.5),
                       family = "binomial", type.measure = "auc",
                       blocks = list(block1=1:13,block2=14:80, block3=81:190),
                       block1.penalization = TRUE, lambda.type = "lambda.min",
                       standardize = FALSE, nfolds = 3, alpha = 1)

newdata_bin <- matrix(rnorm(10*190),10,190)

predict(object = pl_bin, newdata = newdata_bin, type = "response", alpha = 1)

Priority Elastic Net for High-Dimensional Data

Description

This function performs penalized regression analysis using the elastic net method, tailored for high-dimensional data with a known group structure. It also includes an optional feature to launch a Shiny application for model evaluation with weighted threshold optimization.

Usage

priorityelasticnet(
  X,
  Y,
  weights = NULL,
  family = c("gaussian", "binomial", "cox", "multinomial"),
  alpha = 0.5,
  type.measure,
  blocks,
  max.coef = NULL,
  block1.penalization = TRUE,
  lambda.type = "lambda.min",
  standardize = TRUE,
  nfolds = 10,
  foldid = NULL,
  cvoffset = FALSE,
  cvoffsetnfolds = 10,
  mcontrol = missing.control(),
  scale.y = FALSE,
  return.x = TRUE,
  adaptive = FALSE,
  initial_global_weight = TRUE,
  verbose = FALSE,
  ...
)
priorityelasticnet(
  X,
  Y,
  weights = NULL,
  family = c("gaussian", "binomial", "cox", "multinomial"),
  alpha = 0.5,
  type.measure,
  blocks,
  max.coef = NULL,
  block1.penalization = TRUE,
  lambda.type = "lambda.min",
  standardize = TRUE,
  nfolds = 10,
  foldid = NULL,
  cvoffset = FALSE,
  cvoffsetnfolds = 10,
  mcontrol = missing.control(),
  scale.y = FALSE,
  return.x = TRUE,
  adaptive = FALSE,
  initial_global_weight = TRUE,
  verbose = FALSE,
  ...
)

Arguments

`X`	A numeric matrix of predictors.
`Y`	A response vector. For family = "multinomial", Y should be a factor with more than two levels.
`weights`	Optional observation weights. Default is NULL.
`family`	A character string specifying the model type. Options are "gaussian", "binomial", "cox", and "multinomial". Default is "gaussian".
`alpha`	The elastic net mixing parameter, with $0 \le \alpha \le 1$ . The penalty is defined as $(1-\alpha)/2\|\|\beta\|\|_2^2 + \alpha\|\|\beta\|\|_1$ . Default is 1.
`type.measure`	Loss function for cross-validation. Options are "mse", "deviance", "class", "auc". Default depends on the family.
`blocks`	A list where each element is a vector of indices indicating the predictors in that block.
`max.coef`	A numeric vector specifying the maximum number of non-zero coefficients allowed in each block. Default is NULL, meaning no limit.
`block1.penalization`	Logical. If FALSE, the first block will not be penalized. Default is TRUE.
`lambda.type`	Type of lambda to select. Options are "lambda.min" or "lambda.1se". Default is "lambda.min".
`standardize`	Logical flag for variable standardization, prior to fitting the model. Default is TRUE.
`nfolds`	Number of folds for cross-validation. Default is 10.
`foldid`	Optional vector of values between 1 and `nfolds` identifying what fold each observation is in. Default is NULL.
`cvoffset`	Logical. If TRUE, a cross-validated offset is used. Default is FALSE.
`cvoffsetnfolds`	Number of folds for cross-validation of the offset. Default is 10.
`mcontrol`	Control parameters for handling missing data. Default is `missing.control()`.
`scale.y`	Logical. If TRUE, the response variable Y is scaled. Default is FALSE.
`return.x`	Logical. If TRUE, the function returns the input matrix X. Default is TRUE.
`adaptive`	Logical. If `TRUE`, the adaptive elastic net is used, where penalties are adjusted based on the importance of the coefficients from an initial model fit. Default is `FALSE`.
`initial_global_weight`	Logical. If TRUE (the default), global initial weights will be calculated based on all predictors. If FALSE, initial weights will be calculated separately for each block.
`verbose`	Logical. If TRUE prints detailed logs of the process. Default is FALSE.
`...`	Additional arguments to be passed to `cv.glmnet`.

Value

A list with the following components:

`lambda.ind`	Indices of the selected lambda values.
`lambda.type`	Type of lambda used.
`lambda.min`	Selected lambda values.
`min.cvm`	Cross-validated mean squared error for each block.
`nzero`	Number of non-zero coefficients for each block.
`glmnet.fit`	Fitted `glmnet` objects for each block.
`name`	Name of the model.
`block1unpen`	Fitted model for the unpenalized first block, if applicable.
`coefficients`	Coefficients of the fitted models.
`call`	The function call.
`X`	The input matrix X, if `return.x` is TRUE.
`missing.data`	Logical vector indicating missing data.
`imputation.models`	Imputation models used, if applicable.
`blocks.used.for.imputation`	Blocks used for imputation, if applicable.
`missingness.pattern`	Pattern of missing data, if applicable.
`y.scale.param`	Parameters for scaling Y, if applicable.
`blocks`	The input blocks.
`mcontrol`	Control parameters for handling missing data.
`family`	The model family.
`dim.x`	Dimensions of the input matrix X.

Note

Ensure that glmnet version >= 2.0.13 is installed. The function does not support single missing values within a block.

Examples



  # Simulation of multinomial data:
  set.seed(123)
  n <- 100
  p <- 50
  k <- 3
  x <- matrix(rnorm(n * p), n, p)
  y <- sample(1:k, n, replace = TRUE)
  y <- factor(y)
  blocks <- list(bp1 = 1:10, bp2 = 11:30, bp3 = 31:50)
  
  # Run priorityelasticnet:
  fit <- priorityelasticnet(x, y, family = "multinomial", alpha = 0.5, 
                     type.measure = "class", blocks = blocks,
                     block1.penalization = TRUE, lambda.type = "lambda.min", 
                     standardize = TRUE, nfolds = 5, 
                     adaptive = FALSE)
                     
   fit$coefficients

# Simulation of multinomial data:
  set.seed(123)
  n <- 100
  p <- 50
  k <- 3
  x <- matrix(rnorm(n * p), n, p)
  y <- sample(1:k, n, replace = TRUE)
  y <- factor(y)
  blocks <- list(bp1 = 1:10, bp2 = 11:30, bp3 = 31:50)
  
  # Run priorityelasticnet:
  fit <- priorityelasticnet(x, y, family = "multinomial", alpha = 0.5, 
                     type.measure = "class", blocks = blocks,
                     block1.penalization = TRUE, lambda.type = "lambda.min", 
                     standardize = TRUE, nfolds = 5, 
                     adaptive = FALSE)
                     
   fit$coefficients

A Shiny App for Model Evaluation and Weighted Threshold Optimization

Description

This function starts a Shiny application that enables users to interactively adjust the threshold for binary classification and view related metrics, the confusion matrix, ROC curve, and PR curve. The app also includes a feature for calculating the optimal threshold using a weighted version of Youden's J-statistic.

Usage

weightedThreshold(object, ...)
weightedThreshold(object, ...)

Arguments

`object`	A result from priorityelasticnet function with binomial model family.
`...`	Additional arguments

Details

To calculate the optimal threshold, a weighted version of Youden's J-statistic (Youden, 1950) is used. The optimal cutoff is the threshold that maximizes the distance from the identity (diagonal) line. The function optimizes the metric (w * sensitivity + (1 - w) * specificity), where 'w' is the weight parameter adjusted using the second slider. After selecting the desired value on the optimal threshold slider, the user must press the "Set" button to update the threshold slider with the calculated optimal value. Metrics will then be automatically recalculated based on the user's selection. This function adapted from 'Monahov, A. (2021). Model Evaluation with Weighted Threshold Optimization (and the “mewto” R package). Available at SSRN 3805911.'

Value

No return value. This function is used for side effects only, specifically to launch a Shiny application for model evaluation with weighted threshold optimization. The Shiny app provides an interactive interface to visualize model performance metrics and optimize thresholds for classification models based on user-defined criteria.

Package 'priorityelasticnet'

Help Index

Calculates the offsets for the current block

Description

Usage

Arguments

Value

Extract coefficients from a priorityelasticnet object

Description

Usage

Arguments

Value

Compare the rows of a matrix with a pattern

Description

Usage

Arguments

Value

priorityelasticnet with several block specifications

Description

Usage

Arguments

Value

Note

Construct control structures for handling of missing data for priorityelasticnet

Description

Usage

Arguments

Value

Simulated Patient Data for Binary Classification

Description

Usage

Format

Predictions from priorityelasticnet

Description

Usage

Arguments

Details

Value

Examples

Priority Elastic Net for High-Dimensional Data

Description

Usage

Arguments

Value

Note

Examples

A Shiny App for Model Evaluation and Weighted Threshold Optimization

Description

Usage

Arguments

Details

Value

Construct control structures for handling of missing data for `priorityelasticnet`