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Armin Rauschenberger
joinet
Commits
f50d3a09
Commit
f50d3a09
authored
Feb 05, 2019
by
Armin Rauschenberger
Browse files
automation
parent
88dd8b18
Changes
8
Hide whitespace changes
Inline
Side-by-side
R/functions.R
View file @
f50d3a09
...
...
@@ -243,16 +243,23 @@ cornet <- function(y,cutoff,X,alpha=1,npi=101,pi=NULL,nsigma=99,sigma=NULL,nfold
#' @export
#' @title
#'
to do
#'
Extract estimated coefficients
#'
#' @description
#' to do
#' Extracts estimated coefficients from linear and logistic regression,
#' under the penalty parameter that minimises the cross-validated loss.
#'
#' @param object
#'
cornet
object
#'
\link[cornet]{cornet}
object
#'
#' @param ...
#' to do
#' further arguments (not applicable)
#'
#' @return
#' This function returns a matrix with \eqn{n} rows and two columns,
#' where \eqn{n} is the sample size. It includes the estimated coefficients
#' from linear (first column, \code{"beta"})
#' and logistic (second column, \code{"gamma"}) regression.
#'
#' @examples
#' NA
...
...
@@ -275,16 +282,17 @@ coef.cornet <- function(object,...){
#' @export
#' @title
#'
to do
#'
Plot loss matrix
#'
#' @description
#' to do
#' Plots the loss for difference combinations of
#' the weight (pi) and scale (sigma) paramters.
#'
#' @param x
#'
to do
#'
\link[cornet]{cornet} object
#'
#' @param ...
#' further arguments
#' further arguments
(not applicable)
#'
#' @examples
#' NA
...
...
@@ -303,43 +311,49 @@ plot.cornet <- function(x,...){
graphics
::
par
(
xaxs
=
"i"
,
yaxs
=
"i"
)
graphics
::
plot.window
(
xlim
=
c
(
1-0.5
,
nsigma
+0.5
),
ylim
=
c
(
1-0.5
,
npi
+0.5
))
graphics
::
title
(
xlab
=
expression
(
sigma
),
ylab
=
expression
(
pi
),
cex.lab
=
2
)
graphics
::
title
(
xlab
=
expression
(
sigma
),
ylab
=
expression
(
pi
),
cex.lab
=
1
)
#graphics::.filled.contour(x=seq_along(x$sigma),y=seq_along(x$pi),z=x$cvm,levels=levels,col=col)
graphics
::
image
(
x
=
seq_along
(
x
$
sigma
),
y
=
seq_along
(
x
$
pi
),
z
=
x
$
cvm
,
breaks
=
levels
,
col
=
col
,
add
=
TRUE
)
graphics
::
box
()
#graphics::abline(v=ssigma,lty=2,col="grey")
#graphics::abline(h=spi,lty=2,col="grey")
ssigma
<-
which
(
x
$
sigma
%in%
x
$
sigma.min
)
spi
<-
which
(
x
$
pi
%in%
x
$
pi.min
)
if
(
length
(
ssigma
)
==
1
&
length
(
spi
)
==
1
){
# axes with labels for tuned parameters
graphics
::
axis
(
side
=
1
,
at
=
c
(
1
,
ssigma
,
nsigma
),
labels
=
signif
(
x
$
sigma
[
c
(
1
,
ssigma
,
nsigma
)],
digits
=
2
))
graphics
::
axis
(
side
=
2
,
at
=
c
(
1
,
spi
,
npi
),
labels
=
signif
(
x
$
pi
[
c
(
1
,
spi
,
npi
)],
digits
=
2
))
# point for tuned parameters
graphics
::
points
(
x
=
ssigma
,
y
=
spi
,
pch
=
4
,
col
=
"black"
,
cex
=
1
)
}
else
{
# axes with standard labels
at
<-
seq
(
from
=
1
,
to
=
nsigma
,
length.out
=
5
)
graphics
::
axis
(
side
=
1
,
at
=
at
,
labels
=
signif
(
x
$
sigma
,
digits
=
2
)[
at
])
at
<-
seq
(
from
=
1
,
to
=
nsigma
,
length.out
=
5
)
graphics
::
axis
(
side
=
2
,
at
=
at
,
labels
=
signif
(
x
$
pi
,
digits
=
2
)[
at
])
# points for selected parameters
isigma
<-
sapply
(
x
$
sigma.min
,
function
(
y
)
which
(
x
$
sigma
==
y
))
ipi
<-
sapply
(
x
$
pi.min
,
function
(
y
)
which
(
x
$
pi
==
y
))
graphics
::
points
(
x
=
isigma
,
y
=
ipi
,
pch
=
4
,
col
=
"black"
,
cex
=
1
)
}
#a <- sapply(x$sigma.min,function(y) which(x$sigma==y))
#b <- sapply(x$pi.min,function(y) which(x$pi==y))
#graphics::points(x=a,y=b,pch=4,col="black",cex=1)
}
#' @export
#' @title
#'
to do
#'
Predict binary outcome
#'
#' @description
#' to do
#'
#' Predicts the binary outcome with linear, logistic, and combined regression.
#'
#' @details
#' For linear regression, this function tentatively transforms
#' the predicted values to predicted probabilities,
#' using a Gaussian distribution with a fixed mean (threshold)
#' and a fixed variance (estimated variance of the numeric outcome).
#'
#' @param object
#'
cornet
object
#'
\link[cornet]{cornet}
object
#'
#' @param newx
#' covariates\strong{:}
...
...
@@ -350,7 +364,7 @@ plot.cornet <- function(x,...){
#' \code{"probability"}, \code{"odds"}, \code{"log-odds"}
#'
#' @param ...
#'
to do
#'
further arguments (not applicable)
#'
#' @examples
#' NA
...
...
docs/reference/coef.cornet.html
View file @
f50d3a09
...
...
@@ -6,7 +6,7 @@
<meta
http-equiv=
"X-UA-Compatible"
content=
"IE=edge"
>
<meta
name=
"viewport"
content=
"width=device-width, initial-scale=1.0"
>
<title>
to do
— coef.cornet • cornet
</title>
<title>
Extract estimated coefficients
— coef.cornet • cornet
</title>
<!-- jquery -->
<script
src=
"https://code.jquery.com/jquery-3.1.0.min.js"
integrity=
"sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq"
crossorigin=
"anonymous"
></script>
...
...
@@ -30,9 +30,10 @@
<meta
property=
"og:title"
content=
"
to do
— coef.cornet"
/>
<meta
property=
"og:title"
content=
"
Extract estimated coefficients
— coef.cornet"
/>
<meta
property=
"og:description"
content=
"to do"
/>
<meta
property=
"og:description"
content=
"Extracts estimated coefficients from linear and logistic regression,
under the penalty parameter that minimises the cross-validated loss."
/>
<meta
name=
"twitter:card"
content=
"summary"
/>
...
...
@@ -100,14 +101,15 @@
<div
class=
"row"
>
<div
class=
"col-md-9 contents"
>
<div
class=
"page-header"
>
<h1>
to do
</h1>
<h1>
Extract estimated coefficients
</h1>
<small
class=
"dont-index"
>
Source:
<a
href=
'https://github.com/rauschenberger/colasso/blob/master/R/functions.R'
><code>
R/functions.R
</code></a></small>
<div
class=
"hidden name"
><code>
coef.cornet.Rd
</code></div>
</div>
<div
class=
"ref-description"
>
<p>
to do
</p>
<p>
Extracts estimated coefficients from linear and logistic regression,
under the penalty parameter that minimises the cross-validated loss.
</p>
</div>
...
...
@@ -119,14 +121,21 @@
<colgroup><col
class=
"name"
/><col
class=
"desc"
/></colgroup>
<tr>
<th>
object
</th>
<td><p>
cornet
object
</p></td>
<td><p>
<a
href=
'cornet.html'
>
cornet
</a>
object
</p></td>
</tr>
<tr>
<th>
...
</th>
<td><p>
to do
</p></td>
<td><p>
further arguments (not applicable)
</p></td>
</tr>
</table>
<h2
class=
"hasAnchor"
id=
"value"
><a
class=
"anchor"
href=
"#value"
></a>
Value
</h2>
<p>
This function returns a matrix with \(n\) rows and two columns,
where \(n\) is the sample size. It includes the estimated coefficients
from linear (first column,
<code>
"beta"
</code>
)
and logistic (second column,
<code>
"gamma"
</code>
) regression.
</p>
<h2
class=
"hasAnchor"
id=
"examples"
><a
class=
"anchor"
href=
"#examples"
></a>
Examples
</h2>
<pre
class=
"examples"
><div
class=
'input'
><span
class=
'fl'
>
NA
</span></div><div
class=
'output co'
>
#
>
[1] NA
</div><div
class=
'input'
>
...
...
@@ -136,7 +145,9 @@
<h2>
Contents
</h2>
<ul
class=
"nav nav-pills nav-stacked"
>
<li><a
href=
"#arguments"
>
Arguments
</a></li>
<li><a
href=
"#value"
>
Value
</a></li>
<li><a
href=
"#examples"
>
Examples
</a></li>
</ul>
...
...
docs/reference/index.html
View file @
f50d3a09
...
...
@@ -120,7 +120,7 @@
<td>
<p><code><a
href=
"coef.cornet.html"
>
coef(
<i>
<
cornet
>
</i>
)
</a></code>
</p>
</td>
<td><p>
to do
</p></td>
<td><p>
Extract estimated coefficients
</p></td>
</tr><tr>
<td>
...
...
@@ -156,13 +156,13 @@
<td>
<p><code><a
href=
"plot.cornet.html"
>
plot(
<i>
<
cornet
>
</i>
)
</a></code>
</p>
</td>
<td><p>
to do
</p></td>
<td><p>
Plot loss matrix
</p></td>
</tr><tr>
<td>
<p><code><a
href=
"predict.cornet.html"
>
predict(
<i>
<
cornet
>
</i>
)
</a></code>
</p>
</td>
<td><p>
to do
</p></td>
<td><p>
Predict binary outcome
</p></td>
</tr>
</tbody>
</table>
...
...
docs/reference/plot.cornet.html
View file @
f50d3a09
...
...
@@ -6,7 +6,7 @@
<meta
http-equiv=
"X-UA-Compatible"
content=
"IE=edge"
>
<meta
name=
"viewport"
content=
"width=device-width, initial-scale=1.0"
>
<title>
to do
— plot.cornet • cornet
</title>
<title>
Plot loss matrix
— plot.cornet • cornet
</title>
<!-- jquery -->
<script
src=
"https://code.jquery.com/jquery-3.1.0.min.js"
integrity=
"sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq"
crossorigin=
"anonymous"
></script>
...
...
@@ -30,9 +30,10 @@
<meta
property=
"og:title"
content=
"
to do
— plot.cornet"
/>
<meta
property=
"og:title"
content=
"
Plot loss matrix
— plot.cornet"
/>
<meta
property=
"og:description"
content=
"to do"
/>
<meta
property=
"og:description"
content=
"Plots the loss for difference combinations of
the weight (pi) and scale (sigma) paramters."
/>
<meta
name=
"twitter:card"
content=
"summary"
/>
...
...
@@ -100,14 +101,15 @@
<div
class=
"row"
>
<div
class=
"col-md-9 contents"
>
<div
class=
"page-header"
>
<h1>
to do
</h1>
<h1>
Plot loss matrix
</h1>
<small
class=
"dont-index"
>
Source:
<a
href=
'https://github.com/rauschenberger/colasso/blob/master/R/functions.R'
><code>
R/functions.R
</code></a></small>
<div
class=
"hidden name"
><code>
plot.cornet.Rd
</code></div>
</div>
<div
class=
"ref-description"
>
<p>
to do
</p>
<p>
Plots the loss for difference combinations of
the weight (pi) and scale (sigma) paramters.
</p>
</div>
...
...
@@ -119,11 +121,11 @@
<colgroup><col
class=
"name"
/><col
class=
"desc"
/></colgroup>
<tr>
<th>
x
</th>
<td><p>
to do
</p></td>
<td><p>
<a
href=
'cornet.html'
>
cornet
</a>
object
</p></td>
</tr>
<tr>
<th>
...
</th>
<td><p>
further arguments
</p></td>
<td><p>
further arguments
(not applicable)
</p></td>
</tr>
</table>
...
...
docs/reference/predict.cornet.html
View file @
f50d3a09
...
...
@@ -6,7 +6,7 @@
<meta
http-equiv=
"X-UA-Compatible"
content=
"IE=edge"
>
<meta
name=
"viewport"
content=
"width=device-width, initial-scale=1.0"
>
<title>
to do
— predict.cornet • cornet
</title>
<title>
Predict binary outcome
— predict.cornet • cornet
</title>
<!-- jquery -->
<script
src=
"https://code.jquery.com/jquery-3.1.0.min.js"
integrity=
"sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq"
crossorigin=
"anonymous"
></script>
...
...
@@ -30,9 +30,9 @@
<meta
property=
"og:title"
content=
"
to do
— predict.cornet"
/>
<meta
property=
"og:title"
content=
"
Predict binary outcome
— predict.cornet"
/>
<meta
property=
"og:description"
content=
"
to do
"
/>
<meta
property=
"og:description"
content=
"
Predicts the binary outcome with linear, logistic, and combined regression.
"
/>
<meta
name=
"twitter:card"
content=
"summary"
/>
...
...
@@ -100,14 +100,14 @@
<div
class=
"row"
>
<div
class=
"col-md-9 contents"
>
<div
class=
"page-header"
>
<h1>
to do
</h1>
<h1>
Predict binary outcome
</h1>
<small
class=
"dont-index"
>
Source:
<a
href=
'https://github.com/rauschenberger/colasso/blob/master/R/functions.R'
><code>
R/functions.R
</code></a></small>
<div
class=
"hidden name"
><code>
predict.cornet.Rd
</code></div>
</div>
<div
class=
"ref-description"
>
<p>
to do
</p>
<p>
Predicts the binary outcome with linear, logistic, and combined regression.
</p>
</div>
...
...
@@ -119,7 +119,7 @@
<colgroup><col
class=
"name"
/><col
class=
"desc"
/></colgroup>
<tr>
<th>
object
</th>
<td><p>
cornet
object
</p></td>
<td><p>
<a
href=
'cornet.html'
>
cornet
</a>
object
</p></td>
</tr>
<tr>
<th>
newx
</th>
...
...
@@ -133,10 +133,17 @@ and \(p\) columns (variables)</p></td>
</tr>
<tr>
<th>
...
</th>
<td><p>
to do
</p></td>
<td><p>
further arguments (not applicable)
</p></td>
</tr>
</table>
<h2
class=
"hasAnchor"
id=
"details"
><a
class=
"anchor"
href=
"#details"
></a>
Details
</h2>
<p>
For linear regression, this function tentatively transforms
the predicted values to predicted probabilities,
using a Gaussian distribution with a fixed mean (threshold)
and a fixed variance (estimated variance of the numeric outcome).
</p>
<h2
class=
"hasAnchor"
id=
"examples"
><a
class=
"anchor"
href=
"#examples"
></a>
Examples
</h2>
<pre
class=
"examples"
><div
class=
'input'
><span
class=
'fl'
>
NA
</span></div><div
class=
'output co'
>
#
>
[1] NA
</div><div
class=
'input'
>
...
...
@@ -146,7 +153,9 @@ and \(p\) columns (variables)</p></td>
<h2>
Contents
</h2>
<ul
class=
"nav nav-pills nav-stacked"
>
<li><a
href=
"#arguments"
>
Arguments
</a></li>
<li><a
href=
"#details"
>
Details
</a></li>
<li><a
href=
"#examples"
>
Examples
</a></li>
</ul>
...
...
man/coef.cornet.Rd
View file @
f50d3a09
...
...
@@ -2,17 +2,24 @@
% Please edit documentation in R/functions.R
\name{coef.cornet}
\alias{coef.cornet}
\title{
to do
}
\title{
Extract estimated coefficients
}
\usage{
\method{coef}{cornet}(object, ...)
}
\arguments{
\item{object}{
cornet
object}
\item{object}{
\link[cornet]{cornet}
object}
\item{...}{to do}
\item{...}{further arguments (not applicable)}
}
\value{
This function returns a matrix with \eqn{n} rows and two columns,
where \eqn{n} is the sample size. It includes the estimated coefficients
from linear (first column, \code{"beta"})
and logistic (second column, \code{"gamma"}) regression.
}
\description{
to do
Extracts estimated coefficients from linear and logistic regression,
under the penalty parameter that minimises the cross-validated loss.
}
\examples{
NA
...
...
man/plot.cornet.Rd
View file @
f50d3a09
...
...
@@ -2,17 +2,18 @@
% Please edit documentation in R/functions.R
\name{plot.cornet}
\alias{plot.cornet}
\title{
to do
}
\title{
Plot loss matrix
}
\usage{
\method{plot}{cornet}(x, ...)
}
\arguments{
\item{x}{
to do
}
\item{x}{
\link[cornet]{cornet} object
}
\item{...}{further arguments}
\item{...}{further arguments
(not applicable)
}
}
\description{
to do
Plots the loss for difference combinations of
the weight (pi) and scale (sigma) paramters.
}
\examples{
NA
...
...
man/predict.cornet.Rd
View file @
f50d3a09
...
...
@@ -2,12 +2,12 @@
% Please edit documentation in R/functions.R
\name{predict.cornet}
\alias{predict.cornet}
\title{
to do
}
\title{
Predict binary outcome
}
\usage{
\method{predict}{cornet}(object, newx, type = "probability", ...)
}
\arguments{
\item{object}{
cornet
object}
\item{object}{
\link[cornet]{cornet}
object}
\item{newx}{covariates\strong{:}
numeric matrix with \eqn{n} rows (samples)
...
...
@@ -15,10 +15,16 @@ and \eqn{p} columns (variables)}
\item{type}{\code{"probability"}, \code{"odds"}, \code{"log-odds"}}
\item{...}{
to do
}
\item{...}{
further arguments (not applicable)
}
}
\description{
to do
Predicts the binary outcome with linear, logistic, and combined regression.
}
\details{
For linear regression, this function tentatively transforms
the predicted values to predicted probabilities,
using a Gaussian distribution with a fixed mean (threshold)
and a fixed variance (estimated variance of the numeric outcome).
}
\examples{
NA
...
...
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