oem.xtx | R Documentation |
Orthogonalizing EM with precomputed XtX
oem.xtx(
xtx,
xty,
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"),
lambda = numeric(0),
nlambda = 100L,
lambda.min.ratio = NULL,
alpha = 1,
gamma = 3,
tau = 0.5,
groups = numeric(0),
scale.factor = numeric(0),
penalty.factor = NULL,
group.weights = NULL,
maxit = 500L,
tol = 1e-07,
irls.maxit = 100L,
irls.tol = 0.001
)
xtx |
input matrix equal to |
xty |
numeric vector of length |
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 |
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 |
scale.factor |
of length |
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 |
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 |
An object with S3 class "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
fit <- oem(x = x, y = y,
penalty = c("lasso", "elastic.net",
"ols",
"mcp", "scad",
"mcp.net", "scad.net",
"grp.lasso", "grp.lasso.net",
"grp.mcp", "grp.scad",
"sparse.grp.lasso"),
standardize = FALSE, intercept = FALSE,
groups = rep(1:20, each = 5))
xtx <- crossprod(x) / n.obs
xty <- crossprod(x, y) / n.obs
fit.xtx <- oem.xtx(xtx = xtx, xty = xty,
penalty = c("lasso", "elastic.net",
"ols",
"mcp", "scad",
"mcp.net", "scad.net",
"grp.lasso", "grp.lasso.net",
"grp.mcp", "grp.scad",
"sparse.grp.lasso"),
groups = rep(1:20, each = 5))
max(abs(fit$beta[[1]][-1,] - fit.xtx$beta[[1]]))
max(abs(fit$beta[[2]][-1,] - fit.xtx$beta[[2]]))
layout(matrix(1:2, ncol = 2))
plot(fit.xtx)
plot(fit.xtx, which.model = 2)
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