multiview.cox.path  R Documentation 
Fit a Cox regression model via penalized maximum likelihood for a path of lambda values. Can deal with (start, stop] data and strata, as well as sparse design matrices.
multiview.cox.path(
x_list,
x,
y,
rho = 0,
weights = NULL,
lambda = NULL,
offset = NULL,
alpha = 1,
nlambda = 100,
lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e04),
standardize = TRUE,
intercept = TRUE,
thresh = 1e07,
exclude = integer(0),
penalty.factor = rep(1, nvars),
lower.limits = Inf,
upper.limits = Inf,
maxit = 1e+05,
trace.it = 0,
nvars,
nobs,
xm,
xs,
control,
vp,
vnames,
is.offset
)
x_list 
a list of 
x 
the 
y 
the quantitative response with length equal to 
rho 
the weight on the agreement penalty, default 0. 
weights 
observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation 
lambda 
A user supplied 
offset 
A vector of length 
alpha 
The elasticnet mixing parameter, with

nlambda 
The number of 
lambda.min.ratio 
Smallest value for 
standardize 
Logical flag for x variable standardization,
prior to fitting the model sequence. The coefficients are always
returned on the original scale. Default is

intercept 
Should intercept(s) be fitted (default 
thresh 
Convergence threshold for coordinate descent. Each
inner coordinatedescent loop continues until the maximum change
in the objective after any coefficient update is less than

exclude 
Indices of variables to be excluded from the
model. Default is none. Equivalent to an infinite penalty factor
for the variables excluded (next item). Users can supply instead
an 
penalty.factor 
Separate penalty factors can be applied to
each coefficient. This is a number that multiplies 
lower.limits 
Vector of lower limits for each coefficient;
default 
upper.limits 
Vector of upper limits for each coefficient;
default 
maxit 
Maximum number of passes over the data for all lambda values; default is 10^5. 
trace.it 
If 
nvars 
the number of variables (total) 
nobs 
the number of observations 
xm 
the column means vector (could be zeros if 
xs 
the column std dev vector (could be 1s if 
control 
the multiview control object 
vp 
the variable penalities (processed) 
vnames 
the variable names 
is.offset 
a flag indicating if offset is supplied or not 
Sometimes the sequence is truncated before nlambda
values of lambda
have been used. This happens when cox.path
detects that the
decrease in deviance is marginal (i.e. we are near a saturated fit).
An object of class "coxnet" and "glmnet".
a0 
Intercept value, 
beta 
A 
df 
The number of nonzero coefficients for each value of lambda. 
dim 
Dimension of coefficient matrix. 
lambda 
The actual sequence of lambda values used. When alpha=0, the largest lambda reported does not quite give the zero coefficients reported (lambda=inf would in principle). Instead, the largest lambda for alpha=0.001 is used, and the sequence of lambda values is derived from this. 
dev.ratio 
The fraction of (null) deviance explained. The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat  loglike), where loglike_sat is the loglikelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1dev/nulldev. 
nulldev 
Null deviance (per observation). This is defined to be 2*(loglike_sat loglike(Null)). The null model refers to the 0 model. 
npasses 
Total passes over the data summed over all lambda values. 
jerr 
Error flag, for warnings and errors (largely for internal debugging). 
offset 
A logical variable indicating whether an offset was included in the model. 
call 
The call that produced this object. 
nobs 
Number of observations. 
set.seed(2)
nobs < 100; nvars < 15
xvec < rnorm(nobs * nvars)
xvec[sample.int(nobs * nvars, size = 0.4 * nobs * nvars)] < 0
x < matrix(xvec, nrow = nobs)
beta < rnorm(nvars / 3)
fx < x[, seq(nvars / 3)] %*% beta / 3
ty < rexp(nobs, exp(fx))
tcens < rbinom(n = nobs, prob = 0.3, size = 1)
jsurv < survival::Surv(ty, tcens)
fit1 < glmnet:::cox.path(x, jsurv)
# works with sparse x matrix
x_sparse < Matrix::Matrix(x, sparse = TRUE)
fit2 < glmnet:::cox.path(x_sparse, jsurv)
# example with (start, stop] data
set.seed(2)
start_time < runif(100, min = 0, max = 5)
stop_time < start_time + runif(100, min = 0.1, max = 3)
status < rbinom(n = nobs, prob = 0.3, size = 1)
jsurv_ss < survival::Surv(start_time, stop_time, status)
fit3 < glmnet:::cox.path(x, jsurv_ss)
# example with strata
jsurv_ss2 < glmnet::stratifySurv(jsurv_ss, rep(1:2, each = 50))
fit4 < glmnet:::cox.path(x, jsurv_ss2)
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