spareg (version 1.1.1)

spar.cv: Sparse Projected Averaged Regression with Cross-Validation

Description

Apply Sparse Projected Averaged Regression to High-Dimensional Data, where the number of models and the threshold parameter is chosen using a cross-validation procedure.

Usage

spar.cv(
  x,
  y,
  family = gaussian("identity"),
  model = spar_glmnet(),
  rp = NULL,
  screencoef = NULL,
  nfolds = 10,
  nnu = 20,
  nus = NULL,
  nummods = c(20),
  measure = c("deviance", "mse", "mae", "class", "1-auc"),
  avg_type = c("link", "response"),
  parallel = FALSE,
  seed = NULL,
  ...
)
spareg.cv(
  x,
  y,
  family = gaussian("identity"),
  model = spar_glmnet(),
  rp = NULL,
  screencoef = NULL,
  nfolds = 10,
  nnu = 20,
  nus = NULL,
  nummods = c(20),
  measure = c("deviance", "mse", "mae", "class", "1-auc"),
  avg_type = c("link", "response"),
  parallel = FALSE,
  seed = NULL,
  ...
)

Value

object of class 'spar.cv' with elements

• betas p x max(nummods) sparse matrix of class 'Matrix::dgCMatrix' containing the standardized coefficients from each marginal model computed with the spar algorithm on the whole training data.

• intercepts used in each marginal model, vector of length max(nummods) computed with the spar algorithm on the whole training data.

• scr_coef p-vector of coefficients used for screening for standardized predictors

• inds list of index-vectors corresponding to variables kept after screening in each marginal model of length max(nummods)

• RPMs list of projection matrices used in each marginal model of length max(nummods)

• val_res a data.frame with CV results for each fold and for each element of nus and nummods

• nus vector of \(\nu\)'s considered for thresholding

• nummods vector of numbers of marginal models considered for validation

• family a character corresponding to family object used for the marginal generalized linear model e.g., "gaussian(identity)"

• measure character, type of validation measure used

• avg_type character, averaging type for computing the validation measure

• rp an object of class 'randomprojection'

• screencoef an object of class 'screeningcoef'

• model an object of class 'sparmodel'

• ycenter empirical mean of initial response vector

• yscale empirical standard deviation of initial response vector .

• xcenter p-vector of empirical means of initial predictor variables

• xscale p-vector of empirical standard deviations of initial predictor variables

Arguments

x

n x p numeric matrix of predictor variables.

y

quantitative response vector of length n.

family

a 'family' object used for the marginal generalized linear model; defaults to gaussian("identity").

model

function creating a 'sparmodel' object; defaults to spar_glm() for gaussian family with identity link and to spar_glmnet() for all other family-link combinations.

rp

function creating a 'randomprojection' object.

screencoef

function creating a 'screeningcoef' object

nfolds

number of folds to use for cross-validation; should be at least 2, defaults to 10.

nnu

number of different threshold values \(\nu\) to consider for thresholding; ignored when nus is provided; defaults to 20.

nus

optional vector of \(\nu\)'s to consider for thresholding; if not provided, nnu values ranging from 0 to the maximum absolute marginal coefficient are used.

nummods

vector of numbers of marginal models to consider for validation; defaults to c(20).

measure

loss to use for validation; defaults to "deviance" available for all families. Other options are "mse" or "mae" (between responses and predicted means, for all families), "class" (misclassification error) and "1-auc" (one minus area under the ROC curve) both just for binomial family.

avg_type

type of averaging the marginal models; either on link (default) or on response level. This is used in computing the validation measure.

parallel

assuming a parallel backend is loaded and available, a logical indicating whether the function should use it in parallelizing the estimation of the marginal models. Defaults to FALSE.

seed

integer seed to be set at the beginning of the SPAR algorithm. Default to NULL, in which case no seed is set.

...

further arguments mainly to ensure back-compatibility

See Also

spar, coef.spar.cv, predict.spar.cv, plot.spar.cv, print.spar.cv

Examples

# \donttest{
example_data <- simulate_spareg_data(n = 100, p = 400, ntest = 100)
spar_res <- spar.cv(example_data$x, example_data$y, nfolds = 3L,
  rp = rp_gaussian(), nummods = c(5, 10))
spar_res
coefs <- coef(spar_res)
pred <- predict(spar_res, example_data$x)
plot(spar_res)
plot(spar_res, plot_type = "val_measure", plot_along = "nummod", nu = 0)
plot(spar_res, plot_type = "val_measure", plot_along = "nu", nummod = 10)
plot(spar_res, plot_type = "val_numactive",  plot_along = "nummod", nu = 0)
plot(spar_res, plot_type = "val_numactive",  plot_along = "nu", nummod = 10)
plot(spar_res, plot_type = "res_vs_fitted",  xfit = example_data$xtest,
  yfit = example_data$ytest, opt_par = "1se")
plot(spar_res, "coefs", prange = c(1, 400))
# }
# \donttest{
spar_res <- spareg.cv(example_data$x, example_data$y,
  nummods=c(5, 10, 15, 20, 25, 30))
# }