Deprecated functions

These functions have been renamed and deprecated in oem: oemfit() (use oem()), cv.oemfit() (use cv.oem()), print.oemfit(), plot.oemfit(), predict.oemfit(), and coef.oemfit().

oemfit(
  formula,
  data = list(),
  lambda = NULL,
  nlambda = 100,
  lambda.min.ratio = NULL,
  tolerance = 0.001,
  maxIter = 1000,
  standardized = TRUE,
  numGroup = 1,
  penalty = c("lasso", "scad", "ols", "elastic.net", "ngarrote", "mcp"),
  alpha = 3,
  evaluate = 0,
  condition = -1
)

cv.oemfit(
  formula,
  data = list(),
  lambda = NULL,
  type.measure = c("mse", "mae"),
  ...,
  nfolds = 10,
  foldid,
  penalty = c("lasso", "scad", "elastic.net", "ngarrote", "mcp")
)

# S3 method for oemfit
plot(
  x,
  xvar = c("norm", "lambda", "loglambda", "dev"),
  xlab = iname,
  ylab = "Coefficients",
  ...
)

# S3 method for oemfit
predict(
  object,
  newx,
  s = NULL,
  type = c("response", "coefficients", "nonzero"),
  ...
)

# S3 method for oemfit
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

formula	an object of 'formula' (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'
data	an optional data frame, list or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If not found in 'data', the variables are taken from 'environment(formula)', typically the environment from which 'oemfit' is called.
lambda	A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care. Do not supply a single value for lambda (for predictions after CV use predict() instead). Supply instead a decreasing sequence of lambda values. oemfit relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.
nlambda	The number of lambda values - default is 100.
lambda.min.ratio	Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero). The default depends on the sample size nobs relative to the number of variables nvars. If nobs > nvars, the default is 0.0001, close to zero. If nobs < nvars, the default is 0.01. A very small value of lambda.min.ratio will lead to a saturated fit in the nobs < nvars case.
tolerance	Convergence tolerance for OEM. Each inner OEM loop continues until the maximum change in the objective after any coefficient update is less than tolerance. Defaults value is 1E-3.
maxIter	Maximum number of passes over the data for all lambda values; default is 1000.
standardized	Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize.
numGroup	Integer value for the number of groups to use for OEM fitting. Default is 1.
penalty	type in lower letters. Different types include 'lasso', 'scad', 'ols' (ordinary least square), 'elastic-net', 'ngarrote' (non-negative garrote) and 'mcp'.
alpha	alpha value for scad and mcp.
evaluate	debugging argument
condition	Debugging for different ways of calculating OEM.
type.measure	type.measure measure to evaluate for cross-validation. type.measure = "mse" (mean squared error) or type.measure = "mae" (mean absolute error)
...	arguments to be passed to oemfit()
nfolds	number of folds for cross-validation. default is 10.
foldid	an optional vector of values between 1 and nfold specifying which fold each observation belongs to.
x	fitted oemfit object
xvar	what is on the X-axis. "norm" plots against the L1-norm of the coefficients, "lambda" against the log-lambda sequence, and "dev" against the percent deviance explained.
xlab	x-axis label
ylab	y-axis label
object	fitted oemfit object
newx	matrix of new values for x at which predictions are to be made. Must be a matrix.
s	Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type	not used.
digits	significant digits in print out.

Details

The sequence of models implied by 'lambda' is fit by OEM algorithm.