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AdaptGauss/man/GMMplot_ggplot2.Rd at master · Mthrun/AdaptGauss · GitHub
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\
name
{
GMMplot_ggplot2
}
\
alias
{
GMMplot_ggplot2
}
%
-
Also
NEED
an
'
\a
lias
'
for
EACH
other
topic
documented
here.
\
title
{
Plots
the
Gaussian
Mixture
Model (
GMM
)
withing
ggplot2
}
\
description
{
PlotMixtures
and
PlotMixturesAndBoundaries
for
ggplot2
}
\
usage
{
GMMplot_ggplot2(
Data
,
Means
,
SDs
,
Weights
,
BayesBoundaries
,
SingleGausses
=
TRUE
,
Hist
=
FALSE
)
}
%
-
maybe
also
'
usage
'
for
other
objects
documented
here.
\
arguments
{
\
item
{
Data
}{vector (
1
:
N
)
of
data
points
}
\
item
{
Means
}{
vector
[
1
:
L
]
of
Means
of
Gaussians (
of
GMM
),
L
==
Number
of
Gaussians
}
\
item
{
SDs
}{
vector
of
standard
deviations
,
estimated
Gaussian
Kernels
,
has
to
be
the
same
length
as
Means
}
\
item
{
Weights
}{
vector
of
relative
number
of
points
in
Gaussians (
prior
probabilities
),
has
to
be
the
same
length
as
Means
}
\
item
{
BayesBoundaries
}{
Optional
,
x
values
for
baye
boundaries
,
if
missing
'
BayesDecisionBoundaries
'
is
called
}
\
item
{
SingleGausses
}{
Optional
,
SingleGausses
=
T
than
components
of
the
mixture
in
blue
will
be
shown.
}
\
item
{
Hist
}{
Optional
,
geom_histogram
overlayed
}
}
\
value
{
ggplot2
object
}
\
author
{
Joern
Loetsch
,
Michael
Thrun (
ctb
)
}
\
note
{
MT
standardized
code
for
CRAN
and
added
dec
boundaries
and
doku
}
\
seealso
{
\
code
{\
link
{
PlotMixturesAndBoundaries
}}, \
code
{\
link
{
PlotMixtures
}}, \
code
{\
link
{
BayesDecisionBoundaries
}}
}
\
examples
{
data
=
c(rnorm(
1000
),rnorm(
2000
)
+
2
,rnorm(
1000
)
*
2
-
1
)
\
donttest
{
GMMplot_ggplot2(
data
,c(
-
1
,
0
,
2
),c(
2
,
1
,
1
),c(
0.25
,
0.25
,
0.5
),
SingleGausses
=
TRUE
)
}
}
%
Add
one
or
more
standard
keywords
,
see
file
'
KEYWORDS
'
in
the
%
R
documentation
directory.
\
keyword
{
GMM
}%
use
one
of
RShowDoc(
"
KEYWORDS
"
)
\
keyword
{
ggplot2
}%
__ONLY
ONE__
keyword
per
line
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