grpPUlasso: Solve PU problem with lasso or group lasso penalty.
In PUlasso: High-Dimensional Variable Selection with Presence-Only Data

Description Usage Arguments Value Examples

View source: R/grpPUlasso.R

Fit a model using PUlasso algorithm over a regularization path. The regularization path is computed at a grid of values for the regularization parameter lambda.

grpPUlasso(
  X,
  z,
  py1,
  initial_coef = NULL,
  group = 1:ncol(X),
  penalty = NULL,
  lambda = NULL,
  nlambda = 100,
  lambdaMinRatio = ifelse(N < p, 0.05, 0.005),
  maxit = ifelse(method == "CD", 1000, N * 10),
  maxit_inner = 1e+05,
  weights = NULL,
  eps = 1e-04,
  inner_eps = 0.01,
  verbose = FALSE,
  stepSize = NULL,
  stepSizeAdjustment = NULL,
  batchSize = 1,
  updateFrequency = N,
  samplingProbabilities = NULL,
  method = c("CD", "GD", "SGD", "SVRG", "SAG"),
  trace = c("none", "param", "fVal", "all")
)

`X`	Input matrix; each row is an observation. Can be a matrix or a sparse matrix.
`z`	Response vector representing whether an observation is labeled or unlabeled.
`py1`	True prevalence Pr(Y=1)
`initial_coef`	A vector representing an initial point where we start PUlasso algorithm from.
`group`	A vector representing grouping of the coefficients. For the least ambiguity, it is recommended if group is provided in the form of vector of consecutive ascending integers.
`penalty`	penalty to be applied to the model. Default is sqrt(group size) for each of the group.
`lambda`	A user supplied sequence of lambda values. If unspecified, the function automatically generates its own lambda sequence based on nlambda and lambdaMinRatio.
`nlambda`	The number of lambda values.
`lambdaMinRatio`	Smallest value for lambda, as a fraction of lambda.max which leads to the intercept only model.
`maxit`	Maximum number of iterations.
`maxit_inner`	Maximum number of iterations for a quadratic sub-problem for CD.
`weights`	observation weights. Default is 1 for each observation.
`eps`	Convergence threshold for the outer loop. The algorithm iterates until the maximum change in coefficients is less than eps in the outer loop.
`inner_eps`	Convergence threshold for the inner loop. The algorithm iterates until the maximum change in coefficients is less than eps in the inner loop.
`verbose`	A logical value. if TRUE, the function prints out the fitting process.
`stepSize`	A step size for gradient-based optimization. if NULL, a step size is taken to be stepSizeAdj/mean(Li) where Li is a Lipschitz constant for ith sample
`stepSizeAdjustment`	A step size adjustment. By default, adjustment is 1 for GD and SGD, 1/8 for SVRG and 1/16 for SAG.
`batchSize`	A batch size. Default is 1.
`updateFrequency`	An update frequency of full gradient for method =="SVRG"
`samplingProbabilities`	sampling probabilities for each of samples for stochastic gradient-based optimization. if NULL, each sample is chosen proportionally to Li.
`method`	Optimization method. Default is Coordinate Descent. CD for Coordinate Descent, GD for Gradient Descent, SGD for Stochastic Gradient Descent, SVRG for Stochastic Variance Reduction Gradient, SAG for Stochastic Averaging Gradient.
`trace`	An option for saving intermediate quantities. All intermediate standardized-scale parameter estimates(trace=="param"), objective function values at each iteration(trace=="fVal"), or both(trace=="all") are saved in optResult. Since this is computationally very heavy, it should be only used for decently small-sized dataset and small maxit. A default is "none".

coef A p by length(lambda) matrix of coefficients

std_coef A p by length(lambda) matrix of coefficients in a standardized scale

lambda The actual sequence of lambda values used.

nullDev Null deviance defined to be 2*(logLik_sat -logLik_null)

deviance Deviance defined to be 2*(logLik_sat -logLik(model))

optResult A list containing the result of the optimization. fValues, subGradients contain objective function values and subgradient vectors at each lambda value. If trace = TRUE, corresponding intermediate quantities are saved as well.

iters Number of iterations(EM updates) if method = "CD". Number of steps taken otherwise.