xval.oem | R Documentation |
Fast cross validation for Orthogonalizing EM
xval.oem(
x,
y,
nfolds = 10L,
foldid = NULL,
type.measure = c("mse", "deviance", "class", "auc", "mae"),
ncores = -1,
family = c("gaussian", "binomial"),
penalty = c("elastic.net", "lasso", "ols", "mcp", "scad", "mcp.net", "scad.net",
"grp.lasso", "grp.lasso.net", "grp.mcp", "grp.scad", "grp.mcp.net", "grp.scad.net",
"sparse.grp.lasso"),
weights = numeric(0),
lambda = numeric(0),
nlambda = 100L,
lambda.min.ratio = NULL,
alpha = 1,
gamma = 3,
tau = 0.5,
groups = numeric(0),
penalty.factor = NULL,
group.weights = NULL,
standardize = TRUE,
intercept = TRUE,
maxit = 500L,
tol = 1e-07,
irls.maxit = 100L,
irls.tol = 0.001,
compute.loss = FALSE
)
x |
input matrix of dimension n x p (sparse matrices not yet implemented).
Each row is an observation, each column corresponds to a covariate. The xval.oem() function
is optimized for n >> p settings and may be very slow when p > n, so please use other packages
such as |
y |
numeric response vector of length |
nfolds |
integer number of cross validation folds. 3 is the minimum number allowed. defaults to 10 |
foldid |
an optional vector of values between 1 and |
type.measure |
measure to evaluate for cross-validation. The default is |
ncores |
Integer scalar that specifies the number of threads to be used |
family |
|
penalty |
Specification of penalty type. Choices include:
Careful consideration is required for the group lasso, group MCP, and group SCAD penalties. Groups as specified by the |
weights |
observation weights. defaults to 1 for each observation (setting weight vector to length 0 will default all weights to 1) |
lambda |
A user supplied lambda sequence. By default, the program computes
its own lambda sequence based on |
nlambda |
The number of lambda values - default is 100. |
lambda.min.ratio |
Smallest value for lambda, as a fraction of |
alpha |
mixing value for |
gamma |
tuning parameter for SCAD and MCP penalties. must be >= 1 |
tau |
mixing value for |
groups |
A vector of describing the grouping of the coefficients. See the example below. All unpenalized variables should be put in group 0 |
penalty.factor |
Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables. |
group.weights |
penalty factors applied to each group for the group lasso. Similar to |
standardize |
Logical flag for |
intercept |
Should intercept(s) be fitted ( |
maxit |
integer. Maximum number of OEM iterations |
tol |
convergence tolerance for OEM iterations |
irls.maxit |
integer. Maximum number of IRLS iterations |
irls.tol |
convergence tolerance for IRLS iterations. Only used if |
compute.loss |
should the loss be computed for each estimated tuning parameter? Defaults to |
An object with S3 class "xval.oem"
Huling. J.D. and Chien, P. (2022), Fast Penalized Regression and Cross Validation for Tall Data with the oem Package. Journal of Statistical Software 104(6), 1-24. doi:10.18637/jss.v104.i06
set.seed(123)
n.obs <- 1e4
n.vars <- 100
true.beta <- c(runif(15, -0.25, 0.25), rep(0, n.vars - 15))
x <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
y <- rnorm(n.obs, sd = 3) + x %*% true.beta
system.time(fit <- oem(x = x, y = y,
penalty = c("lasso", "grp.lasso"),
groups = rep(1:20, each = 5)))
system.time(xfit <- xval.oem(x = x, y = y,
penalty = c("lasso", "grp.lasso"),
groups = rep(1:20, each = 5)))
system.time(xfit2 <- xval.oem(x = x, y = y,
penalty = c("lasso", "grp.lasso",
"mcp", "scad",
"mcp.net", "scad.net",
"grp.lasso", "grp.lasso.net",
"grp.mcp", "grp.scad",
"sparse.grp.lasso"),
groups = rep(1:20, each = 5)))
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