exhaustivePlot: Plot of the LML function by exhaustive search.
In gprege: Gaussian Process Ranking and Estimation of Gene Expression time-series
Description
Usage
Arguments
Value
See Also
Examples
View source: R/exhaustivePlot.R
Description
Exhaustively searches the hyperparameter space by a grid, whose resolution is passed as an argument, and plots the LML function for every point in the space.
Usage
1
exhaustivePlot(y, x, xstar, options, maxwidth, res, nlevels)
Arguments
y
the target (output) data.
x
the input data matrix.
xstar
the points to predict function values.
options
options structure as defined by gpOptions.m.
maxwidth
maximum lengthscale to search for.
res
The search resolution. Number of points to plot for in the search range.
nlevels
Number of contour levels.
Value
area
Area under the ROC curve of method-A.
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
noiseLevel <- 0.2
noiseVar <- noiseLevel^2
options <- gpOptions()
options$kern$comp <- list('rbf','white')
## Create data set
l <- 9; x <- matrix(seq(0,240,by=20), ncol=1)
trueKern <- kernCreate(x, 'rbf')
trueKern$inverseWidth <- 1/(20^2) ## Characteristic inverse-width.
K <- kernCompute(trueKern, x) + diag(dim(x)[1])*noiseVar
## Sample some true function values.
y <- gaussSamp(Sigma=K, numSamps=1)
xTest <- as.matrix(seq(0, 240, length=200))
graphics.off(); dev.new(); plot.new(); dev.new(); plot.new()
exhaustivePlot(y, x, xTest, options=options, maxwidth=100, res=50, nlevels=75)
Example output
Loading required package: gptk
Loading required package: Matrix
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.2-2 (2019-03-07) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: 'spam'
The following object is masked from 'package:Matrix':
det
The following objects are masked from 'package:base':
backsolve, forwardsolve
Loading required package: maps
See https://github.com/NCAR/Fields for
an extensive vignette, other supplements and source code
dev.new(): using pdf(file="Rplots1.pdf")
============= EXHAUSTIVE LML SEARCH =====================
Length-scale Signal Noise
7.06122 0.66369 0.02891
Max LML Estim. sig + noise
-13.14534056 0.692599
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gprege documentation built on Nov. 8, 2020, 7:48 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value See Also Examples
View source: R/exhaustivePlot.R
Description
Exhaustively searches the hyperparameter space by a grid, whose resolution is passed as an argument, and plots the LML function for every point in the space.
Usage
1 | exhaustivePlot(y, x, xstar, options, maxwidth, res, nlevels)
|
Arguments
y |
the target (output) data. |
x |
the input data matrix. |
xstar |
the points to predict function values. |
options |
options structure as defined by gpOptions.m. |
maxwidth |
maximum lengthscale to search for. |
res |
The search resolution. Number of points to plot for in the search range. |
nlevels |
Number of contour levels. |
Value
area |
Area under the ROC curve of method-A. |
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | noiseLevel <- 0.2
noiseVar <- noiseLevel^2
options <- gpOptions()
options$kern$comp <- list('rbf','white')
## Create data set
l <- 9; x <- matrix(seq(0,240,by=20), ncol=1)
trueKern <- kernCreate(x, 'rbf')
trueKern$inverseWidth <- 1/(20^2) ## Characteristic inverse-width.
K <- kernCompute(trueKern, x) + diag(dim(x)[1])*noiseVar
## Sample some true function values.
y <- gaussSamp(Sigma=K, numSamps=1)
xTest <- as.matrix(seq(0, 240, length=200))
graphics.off(); dev.new(); plot.new(); dev.new(); plot.new()
exhaustivePlot(y, x, xTest, options=options, maxwidth=100, res=50, nlevels=75)
|
Example output
Loading required package: gptk
Loading required package: Matrix
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.2-2 (2019-03-07) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: 'spam'
The following object is masked from 'package:Matrix':
det
The following objects are masked from 'package:base':
backsolve, forwardsolve
Loading required package: maps
See https://github.com/NCAR/Fields for
an extensive vignette, other supplements and source code
dev.new(): using pdf(file="Rplots1.pdf")
============= EXHAUSTIVE LML SEARCH =====================
Length-scale Signal Noise
7.06122 0.66369 0.02891
Max LML Estim. sig + noise
-13.14534056 0.692599
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