Description Usage Arguments Value Examples
Orthogonalizing EM for big.matrix objects
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  big.oem(
x,
y,
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 = 1e07,
irls.maxit = 100L,
irls.tol = 0.001,
compute.loss = FALSE,
gigs = 4,
hessian.type = c("full", "upper.bound")
)

x 
input big.matrix object pointing to design matrix Each row is an observation, each column corresponds to a covariate 
y 
numeric response vector of length nobs. 
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. Not implemented yet. 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 x variable standardization, prior to fitting the models.
The coefficients are always returned on the original scale. Default is 
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 
gigs 
maximum number of gigs of memory available. Used to figure out how to break up calculations involving the design matrix x 
hessian.type 
only for logistic regression. if 
An object with S3 class "oem"
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39  ## Not run:
set.seed(123)
nrows < 50000
ncols < 100
bkFile < "bigmat.bk"
descFile < "bigmatk.desc"
bigmat < filebacked.big.matrix(nrow=nrows, ncol=ncols, type="double",
backingfile=bkFile, backingpath=".",
descriptorfile=descFile,
dimnames=c(NULL,NULL))
# Each column value with be the column number multiplied by
# samples from a standard normal distribution.
set.seed(123)
for (i in 1:ncols) bigmat[,i] = rnorm(nrows)*i
y < rnorm(nrows) + bigmat[,1]  bigmat[,2]
fit < big.oem(x = bigmat, 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"),
groups = rep(1:20, each = 5))
fit2 < oem(x = bigmat[,], y = y,
penalty = c("lasso", "grp.lasso"),
groups = rep(1:20, each = 5))
max(abs(fit$beta[[1]]  fit2$beta[[1]]))
layout(matrix(1:2, ncol = 2))
plot(fit)
plot(fit, which.model = 2)
## End(Not run)

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