Usage
larsDR_coxph(Xplan, ...)
## S3 method for class 'default':
larsDR_coxph(Xplan, time, time2, event, type,
origin, typeres = "deviance", collapse, weighted, scaleX = FALSE,
scaleY = TRUE, plot = FALSE, typelars, normalize, max.steps,
use.Gram, allres = FALSE, ...)
## S3 method for class 'formula':
larsDR_coxph(Xplan, time, time2, event, type,
origin, typeres = "deviance", collapse, weighted, scaleX = FALSE,
scaleY = TRUE, plot = FALSE, typelars, normalize, max.steps,
use.Gram, allres = FALSE, dataXplan = NULL, subset, weights,
model_frame=FALSE,model_matrix=FALSE, ...)
Arguments
Xplan
a formula or a matrix with the eXplanatory variables (training) dataset
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=
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 e
type
character string specifying the type of censoring. Possible values are "right", "left", "counting", "interval", or "interval2". The default is "right" or "counting"
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 rar
typeres
character string indicating the type of residual desired. Possible values are "martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas", and "scaledsch".
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,
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-in
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.
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 take 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 "unweighted" model matrix is returned.
...
Arguments to be passed on to survival::coxph or to lars::lars.