View source: R/multiview.path.R
multiview.fit  R Documentation 
Fit a generalized linear model via penalized maximum likelihood for a single value of lambda. Can deal with any GLM family.
multiview.fit(
x_list,
x,
y,
rho,
weights,
lambda,
alpha = 1,
offset = rep(0, nobs),
family = gaussian(),
intercept = TRUE,
thresh = 1e07,
maxit = 1e+05,
penalty.factor = rep(1, nvars),
exclude = c(),
lower.limits = Inf,
upper.limits = Inf,
warm = NULL,
from.multiview.path = FALSE,
save.fit = FALSE,
trace.it = 0,
user_lambda = FALSE
)
x_list 
a list of 
x 
the columnbinded entries of 
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 single value for the 
alpha 
The elasticnet mixing parameter, with

offset 
A vector of length 
family 
A description of the error distribution and link function to be used in the model. This is the result of a call to a family function. Default is stats::gaussian. (See stats::family for details on family functions.) 
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

maxit 
Maximum number of passes over the data; default is

penalty.factor 
Separate penalty factors can be applied to
each coefficient. This is a number that multiplies 
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 
lower.limits 
Vector of lower limits for each coefficient;
default 
upper.limits 
Vector of upper limits for each coefficient;
default 
warm 
Either a 
from.multiview.path 
Was 
save.fit 
Return the warm start object? Default is 
trace.it 
Controls how much information is printed to
screen. If 
user_lambda 
a flag indicating if user supplied the lambda sequence 
WARNING: Users should not call multiview.fit
directly. Higherlevel functions in this package call
multiview.fit
as a subroutine. If a warm start object is
provided, some of the other arguments in the function may be
overriden.
multiview.fit
solves the elastic net problem for a single,
userspecified value of lambda. multiview.fit
works for any GLM
family. It solves the problem using iteratively reweighted least
squares (IRLS). For each IRLS iteration, multiview.fit
makes a
quadratic (Newton) approximation of the loglikelihood, then calls
elnet.fit
to minimize the resulting approximation.
In terms of standardization: multiview.fit
does not standardize
x
and weights
. penalty.factor
is standardized so that to sum
to nvars
.
An object with class "multiview"
. The list
returned contains more keys than that of a "multiview"
object.
a0 
Intercept value. 
beta 
A 
df 
The number of nonzero coefficients. 
dim 
Dimension of coefficient matrix. 
lambda 
Lambda value used. 
lambda_scale 
The multiview lambda scale factor 
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 intercept model. 
npasses 
Total passes over the data. 
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. 
warm_fit 
If 
family 
Family used for the model. 
converged 
A logical variable: was the algorithm judged to have converged? 
boundary 
A logical variable: is the fitted value on the boundary of the attainable values? 
obj_function 
Objective function value at the solution. 
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