- Xplan
a formula or a matrix with the eXplanatory variables (training)
dataset
- ...
Arguments to be passed on to survival::coxph or to
lars::lars.
- time
for right censored data, this is the follow up time. For
interval data, the first argument is the starting time for the interval.
- time2
The status indicator, normally 0=alive, 1=dead. Other choices
are TRUE/FALSE (TRUE = death) or 1/2 (2=death). For interval
censored data, the status indicator is 0=right censored, 1=event at
time, 2=left censored, 3=interval censored. Although unusual, the
event indicator can be omitted, in which case all subjects are assumed to
have an event.
- event
ending time of the interval for interval censored or counting
process data only. Intervals are assumed to be open on the left and closed
on the right, (start, end]. For counting process data, event
indicates whether an event occurred at the end of the interval.
- type
character string specifying the type of censoring. Possible
values are "right", "left", "counting",
"interval", or "interval2". The default is "right" or
"counting" depending on whether the time2 argument is absent
or present, respectively.
- origin
for counting process data, the hazard function origin. This
option was intended to be used in conjunction with a model containing time
dependent strata in order to align the subjects properly when they cross
over from one strata to another, but it has rarely proven useful.
- typeres
character string indicating the type of residual desired.
Possible values are "martingale", "deviance", "score",
"schoenfeld", "dfbeta", "dfbetas", and
"scaledsch". Only enough of the string to determine a unique match is
required.
- collapse
vector indicating which rows to collapse (sum) over. In
time-dependent models more than one row data can pertain to a single
individual. If there were 4 individuals represented by 3, 1, 2 and 4 rows of
data respectively, then collapse=c(1,1,1,2,3,3,4,4,4,4) could be used
to obtain per subject rather than per observation residuals.
- weighted
if TRUE and the model was fit with case weights, then
the weighted residuals are returned.
- scaleX
Should the Xplan columns be standardized ?
- scaleY
Should the time values be standardized ?
- plot
Should the survival function be plotted ?)
- typelars
One of "lasso", "lar",
"forward.stagewise" or "stepwise". The names can be
abbreviated to any unique substring. Default is "lasso".
- normalize
If TRUE, each variable is standardized to have unit L2
norm, otherwise it is left alone. Default is TRUE.
- max.steps
Limit the number of steps taken; the default is 8 *
min(m, n-intercept), with m the number of variables, and n the number of
samples. For type="lar" or type="stepwise", the maximum number
of steps is min(m,n-intercept). For type="lasso" and
especially type="forward.stagewise", there can be many more terms,
because although no more than min(m,n-intercept) variables can be active
during any step, variables are frequently droppped and added as the
algorithm proceeds. Although the default usually guarantees that the
algorithm has proceeded to the saturated fit, users should check.
- use.Gram
When the number m of variables is very large, i.e. larger
than N, then you may not want LARS to precompute the Gram matrix. Default is
use.Gram=TRUE
- allres
FALSE to return only the Cox model and TRUE for additionnal
results. See details. Defaults to FALSE.
- verbose
Should some details be displayed ?
- dataXplan
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 dataXplan, the variables are
taken from environment(Xplan), typically the environment from which
plscox is called.
- subset
an optional vector specifying a subset of observations to be
used in the fitting process.
- weights
an optional vector of 'prior weights' to be used in the
fitting process. Should be NULL or a numeric vector.
- model_frame
If TRUE, the model frame is returned.
- model_matrix
If TRUE, the model matrix is returned.
- contrasts.arg
a list, whose entries are values (numeric matrices,
functions or character strings naming functions) to be used as replacement
values for the contrasts replacement function and whose names are the names
of columns of data containing factors.