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
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/refmodel.R
\name{get-refmodel}
\alias{get-refmodel}
\alias{get_refmodel}
\alias{get_refmodel.refmodel}
\alias{get_refmodel.vsel}
\alias{get_refmodel.default}
\alias{get_refmodel.stanreg}
\alias{init_refmodel}
\title{Get reference model structure}
\usage{
get_refmodel(object, ...)
\method{get_refmodel}{refmodel}(object, ...)
\method{get_refmodel}{vsel}(object, ...)
\method{get_refmodel}{default}(
object,
data,
y,
formula,
ref_predfun,
proj_predfun,
div_minimizer,
fetch_data,
family = NULL,
wobs = NULL,
folds = NULL,
cvfits = NULL,
offset = NULL,
cvfun = NULL,
dis = NULL,
...
)
\method{get_refmodel}{stanreg}(
object,
data = NULL,
ref_predfun = NULL,
proj_predfun = NULL,
div_minimizer = NULL,
folds = NULL,
...
)
init_refmodel(
object,
data,
formula,
family,
ref_predfun = NULL,
div_minimizer = NULL,
proj_predfun = NULL,
folds = NULL,
extract_model_data = NULL,
cvfun = NULL,
cvfits = NULL,
dis = NULL,
...
)
}
\arguments{
\item{object}{Object on which the reference model is created. See possible
types below.}
\item{...}{Arguments passed to the methods.}
\item{data}{Data on which the reference model was fitted.}
\item{y}{Target response.}
\item{formula}{Reference model's lme4-like formula.}
\item{ref_predfun}{Prediction function for the linear predictor of the
reference model.}
\item{proj_predfun}{Prediction function for the linear predictor of the
projections.}
\item{div_minimizer}{Maximum likelihood estimator for the underlying
projection.}
\item{fetch_data}{Wrapper function for fetching the data without directly
accessing it. It should have a prototype fetch_data(data, data_points,
newdata = NULL), where data_points is a vector of data indices and newdata,
if not NULL, is a data frame with new data for testing.}
\item{family}{A family object that represents the observation model for the
reference model.}
\item{wobs}{A weights vector for the observations in the data. The default is
a vector of ones.}
\item{folds}{Only used for K-fold variable selection. It is a vector of fold
indices for each data point in data.}
\item{cvfits}{Only used for K-fold variable selection. A list of K-fold
fitted objects on which reference models are created.}
\item{offset}{A vector of offsets per observation to add to the linear
predictor.}
\item{cvfun}{Only used for K-fold variable selection. A function that, given
a folds vector, fits a reference model per fold and returns the fitted
object.}
\item{dis}{A dispersion vector for each observation.}
\item{extract_model_data}{A function with prototype
extract_model_data(object, newdata, wrhs, orhs), where object is a
reference model fit, newdata is either NULL or a data frame with new
observations, wrhs is a right hand side formula to recover the weights from
the data frame and orhs is a right hand side formula to recover the offset
from the data frame.}
}
\value{
An object of type \code{refmodel} (the same type as returned by
\link{init_refmodel}) that can be passed to all the functions that take the
reference fit as the first argument, such as \link{varsel},
\link{cv_varsel}, \link{project}, \link[=proj-pred]{proj_predict} and
\link[=proj-pred]{proj_linpred}.
}
\description{
Generic function that can be used to create and fetch the reference model
structure for all those objects that have this method. All these
implementations are wrappers to the \code{\link{init_refmodel}}-function so
the returned object has the same type.
}
\examples{
\donttest{
if (requireNamespace('rstanarm', quietly=TRUE)) {
### Usage with stanreg objects
dat <- data.frame(y = rnorm(100), x = rnorm(100))
fit <- rstanarm::stan_glm(y ~ x, family = gaussian(), data = dat)
ref <- get_refmodel(fit)
print(class(ref))
# variable selection, use the already constructed reference model
vs <- varsel(ref)
# this will first construct the reference model and then execute
# exactly the same way as the previous command (the result is identical)
vs <- varsel(fit)
}
}
}
|
