simone: SIMoNe algorithm for network inference
In simone: Statistical Inference for MOdular NEtworks (SIMoNe)
Description
Usage
Arguments
Details
Value
Note
Author(s)
See Also
Examples
Description
The simone function offers an interface to infer networks based
on partial correlation coefficients in various contexts and methods
(steady-state data, time-course data, multiple sample setup,
clustering prior)
Usage
1
2
3
4
5
Arguments
X
a n x p matrix of data, typically n
expression levels associated to the same p genes. Can also
be a data.frame with n entries, each column
corresponding to a variable (a gene). Specifying colnames to
X may be convenient in view of results analysis, since it
will be used to annotate the plots. Note that this is the only
required argument.
type
a character string indicating the data specification (either
"steady-state" or "time-course" data). Default is
"steady-state".
clustering
a logical indicating if the network inference should be perfomed by
penalizing the edges according to a latent clustering discovered
during the network structure recovery. Default is FALSE.
tasks
A factor with n entries indicating the task belonging
for each observation in the multiple sample framework. Default is
factor(rep(1, nrow(X))), that is, all observations come from a
unique homogeneous sample.
control
A list that is used to specify low-level options for the
algorithm, defined through the setOptions function.
Details
Any inference method available ("neighborhood selection",
"graphical-Lasso", "VAR(1) inference" and "multitask learning" - see
simone-package) relies on an optimization problem under
the general form
Θhat (λ) = argmaxΘ L(Θ; data) - λ * penl1(Θ, Z),
where L is the log-likelihood of the model (pseudo
log-likelihood for "neighborhood selection") and λ
is a penalty parameter which controls the sparsity level of the
network. The p x p matrix
Θ describes the parameters (basically, the
edges) of the model, while Z represents a latent
clustering which is also estimated when the argument clustering
is set to TRUE.
The model and the penalty function
penl1 differ according to the context
(steady-state/time-course data, multitask learning and its associated
coupling effect). For further details on the models, please check the
papers listed in the reference section of
simone-package.
The criterion displayed during a SIMoNe run is the value of the
penalized likelihood for the current values of the estimor Θhat(λ) corresponding to
a given value of the overall penalty level λ.
The following information criteria are also computed for any value of
λ
and part of the output of simone. The BIC (Bayesian
Information Criterion)
BIC(λ) = L(Θhat(λ); data) - df(Θhat(λ)) log(n)/2,
and the AIC (Akaike Information Criterion)
AIC(λ) = L(Θhat(λ); data) - df(Θhat(λ)) .
Value
Returns an object of class simone, which is list-like and
contains the following:
networks
a list with all the inferred networks stocked as adjacency matrices (the successive values of
Θ
controled by the penalty level
λ). In
the multiple sample setup, each element of the list is a list with
as many entries as samples or levels in tasks.
penalties
a vector of the same length as networks, containing the
successive values of the penalty level.
n.edges
a vector of the same length as networks, containing the
successive numbers of edges in the inferred networks. In the multiple
sample setup, n.edges is a matrix with as many columns as
levels in tasks.
BIC
a vector of the same length as networks, containing the value
of the BIC for the successively estimated networks.
AIC
a vector of the same length as networks, containing the value
of the AIC for the successively estimated networks.
clusters
a size-p factor indicating the class
of each variable.
weights
a pxp matrix of weigths used to adapt the penalty to
each entry of the Theta matrix. It is inferred through the
algorithm according to the latent clustering of the network. When
clustering is set to FALSE, all the weights are equal
to "1", which mean no adaptive penalization.
control
a list describing all the posterior values of the parameters used by
the algorithm, to compare with the one set by the
setOptions function. As a matter of fact, many of the options
are defined depending on the nature of the data and can be automatically corrected during internal checks of the coherence of desired options to the characteristics of the data.
Note
If nothing particular is specified about the penalty through the
control list (see setOptions), the default is to
start from a value of
λ
that ensures an empty network. Then
λ is
progressively shrinked, as close to zero as possible. Along the
shrinkage of
λ,
only networks with different numbers of edges are kept in the final
output.
Author(s)
J. Chiquet
See Also
setOptions, plot.simone,
cancer and demo(package="simone").
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
## load the breast cancer data set
data(cancer)
attach(cancer)
## launch simone with the default parameters and plot results
plot(simone(expr))
## Not run:
## try with clustering now (clustering is achieved on a 30-edges network)
plot(simone(expr, clustering=TRUE, control=setOptions(clusters.crit=30)))
## try the multiple sample
plot(simone(expr, tasks=status))
## End(Not run)
detach(cancer)
Example output
Loading required package: blockmodels
Loading required package: Rcpp
Loading required package: parallel
Loading required package: digest
----------------------------------------------------------------------
'simone' package version 1.0-3
SIMoNe page (http://julien.cremeriefamily.info/simone.html)
----------------------------------------------------------------------
Note that versions >= 1.0-0 are not compatible with versions < 1.0.0.
----------------------------------------------------------------------
Network Inference: neighborhood.selection with AND symmetrization rule applied
| penalty | edges | criteria
1 0 -10336
0.697 1 -9823
0.6667 2 -9790
0.6061 3 -9599
0.5859 4 -9480
0.5758 5 -9380
0.5657 6 -9269
0.5556 7 -9149
0.5455 8 -9012
0.5152 9 -8553
0.5051 11 -8384
0.4849 12 -8027
0.4748 14 -7840
0.4647 15 -7656
0.4546 17 -7466
0.4343 19 -7065
0.4242 22 -6854
0.4141 23 -6634
0.404 24 -6412
0.3939 25 -6191
0.3838 27 -5966
0.3636 30 -5518
0.3535 31 -5298
0.3434 33 -5081
0.3333 37 -4864
0.3232 39 -4650
0.3131 44 -4436
0.303 45 -4227
0.2929 46 -4020
0.2828 48 -3815
0.2727 52 -3613
0.2626 56 -3415
0.2525 61 -3220
0.2424 63 -3031
0.2323 71 -2847
0.2222 74 -2669
0.2121 75 -2496
0.202 77 -2329
0.1919 80 -2167
0.1818 83 -2010
0.1717 87 -1859
0.1616 91 -1714
0.1515 93 -1574
0.1414 95 -1441
0.1313 97 -1317
0.1212 98 -1199
0.1111 101 -1089
0.101 104 -987.5
0.09092 112 -894.9
0.08082 120 -809
0.07072 132 -732.9
0.06062 145 -658.5
0.05051 156 -593.5
0.04041 170 -527.7
0.03031 200 -472.5
0.02021 235 -420.5
0.01011 277 -360.4
1e-05 325 -285.1
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press "Return" or "n+Return" to go forward
Press "p+Return" to go backward
Press "q+Return" to quit
Penalty= 0.70707 0 edges
Penalty= 0.67677 1 edges
Penalty= 0.61617 2 edges
Penalty= 0.59596 3 edges
Penalty= 0.58586 4 edges
Penalty= 0.57576 5 edges
Penalty= 0.56566 6 edges
Penalty= 0.55556 7 edges
Penalty= 0.52526 8 edges
Penalty= 0.51516 9 edges
Penalty= 0.49495 11 edges
Penalty= 0.48485 12 edges
Penalty= 0.47475 14 edges
Penalty= 0.46465 15 edges
Penalty= 0.44445 17 edges
Penalty= 0.43435 19 edges
Penalty= 0.42425 22 edges
Penalty= 0.41415 23 edges
Penalty= 0.40405 24 edges
Penalty= 0.39395 25 edges
Penalty= 0.37374 27 edges
Penalty= 0.36364 30 edges
Penalty= 0.35354 31 edges
Penalty= 0.34344 33 edges
Penalty= 0.33334 37 edges
Penalty= 0.32324 39 edges
Penalty= 0.31314 44 edges
Penalty= 0.30304 45 edges
Penalty= 0.29294 46 edges
Penalty= 0.28284 48 edges
Penalty= 0.27273 52 edges
Penalty= 0.26263 56 edges
Penalty= 0.25253 61 edges
Penalty= 0.24243 63 edges
Penalty= 0.23233 71 edges
Penalty= 0.22223 74 edges
Penalty= 0.21213 75 edges
Penalty= 0.20203 77 edges
Penalty= 0.19193 80 edges
Penalty= 0.18183 83 edges
Penalty= 0.17173 87 edges
Penalty= 0.16162 91 edges
Penalty= 0.15152 93 edges
Penalty= 0.14142 95 edges
Penalty= 0.13132 97 edges
Penalty= 0.12122 98 edges
Penalty= 0.11112 101 edges
Penalty= 0.10102 104 edges
Penalty= 0.09092 112 edges
Penalty= 0.08082 120 edges
Penalty= 0.07072 132 edges
Penalty= 0.06062 145 edges
Penalty= 0.05051 156 edges
Penalty= 0.04041 170 edges
Penalty= 0.03031 200 edges
Penalty= 0.02021 235 edges
Penalty= 0.01011 277 edges
Penalty= 1e-05 325 edges
Network Inference: neighborhood.selection with AND symmetrization rule applied
| penalty | edges | criteria
1 0 -10336
0.697 1 -9823
0.6667 2 -9790
0.6061 3 -9599
0.5859 4 -9480
0.5758 5 -9380
0.5657 6 -9269
0.5556 7 -9149
0.5455 8 -9012
0.5152 9 -8553
0.5051 11 -8384
0.4849 12 -8027
0.4748 14 -7840
0.4647 15 -7656
0.4546 17 -7466
0.4343 19 -7065
0.4242 22 -6854
0.4141 23 -6634
0.404 24 -6412
0.3939 25 -6191
0.3838 27 -5966
0.3636 30 -5518
0.3535 31 -5298
0.3434 33 -5081
0.3333 37 -4864
0.3232 39 -4650
0.3131 44 -4436
0.303 45 -4227
0.2929 46 -4020
0.2828 48 -3815
0.2727 52 -3613
0.2626 56 -3415
0.2525 61 -3220
0.2424 63 -3031
0.2323 71 -2847
0.2222 74 -2669
0.2121 75 -2496
0.202 77 -2329
0.1919 80 -2167
0.1818 83 -2010
0.1717 87 -1859
0.1616 91 -1714
0.1515 93 -1574
0.1414 95 -1441
0.1313 97 -1317
0.1212 98 -1199
0.1111 101 -1089
0.101 104 -987.5
0.09092 112 -894.9
0.08082 120 -809
0.07072 132 -732.9
0.06062 145 -658.5
0.05051 156 -593.5
0.04041 170 -527.7
0.03031 200 -472.5
0.02021 235 -420.5
0.01011 277 -360.4
1e-05 325 -285.1
Found a network with 30 edges.
Network Inference: neighborhood.selection with AND symmetrization rule applied
| penalty | edges | criteria
1.149 0 -315771
0.801 1 -222703
0.7662 2 -213414
0.6965 3 -194713
0.6733 4 -188423
0.6617 5 -185238
0.6501 6 -182042
0.6385 7 -178837
0.6269 8 -175615
0.592 9 -165921
0.5804 11 -162676
0.5572 12 -156182
0.5456 14 -152930
0.534 15 -149679
0.5224 17 -146422
0.4992 19 -139888
0.4876 22 -136615
0.476 23 -133339
0.4644 24 -130064
0.4527 25 -126789
0.4411 27 -123511
0.4179 30 -116956
0.4063 31 -113680
0.3947 32 -110406
0.3831 34 -107131
0.3715 37 -103859
0.3599 38 -100588
0.3483 40 -97322
0.325 42 -90793
0.3134 45 -87529
0.3018 49 -84266
0.2902 54 -81004
0.267 59 -74491
0.2554 60 -71240
0.2438 61 -67992
0.2322 62 -64748
0.2206 64 -61508
0.209 67 -58269
0.1974 70 -55035
0.1857 74 -51804
0.1741 75 -48576
0.1393 79 -38921
0.1277 81 -35713
0.1161 86 -32507
0.1045 94 -29309
0.09288 97 -26115
0.08127 105 -22922
0.06966 109 -19735
0.05805 121 -16558
0.04645 135 -13387
0.03484 151 -10221
0.02323 172 -7067
0.01162 202 -3912
1.149e-05 301 -312
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press "Return" or "n+Return" to go forward
Press "p+Return" to go backward
Press "q+Return" to quit
Penalty= 0.81261 0 edges
Penalty= 0.77778 1 edges
Penalty= 0.70813 2 edges
Penalty= 0.68491 3 edges
Penalty= 0.6733 4 edges
Penalty= 0.6617 5 edges
Penalty= 0.65009 6 edges
Penalty= 0.63848 7 edges
Penalty= 0.60365 8 edges
Penalty= 0.59205 9 edges
Penalty= 0.56883 11 edges
Penalty= 0.55722 12 edges
Penalty= 0.54561 14 edges
Penalty= 0.534 15 edges
Penalty= 0.51079 17 edges
Penalty= 0.49918 19 edges
Penalty= 0.48757 22 edges
Penalty= 0.47596 23 edges
Penalty= 0.46435 24 edges
Penalty= 0.45274 25 edges
Penalty= 0.42953 27 edges
Penalty= 0.41792 30 edges
Penalty= 0.40631 31 edges
Penalty= 0.3947 32 edges
Penalty= 0.38309 34 edges
Penalty= 0.37148 37 edges
Penalty= 0.35988 38 edges
Penalty= 0.33666 40 edges
Penalty= 0.32505 42 edges
Penalty= 0.31344 45 edges
Penalty= 0.30183 49 edges
Penalty= 0.27862 54 edges
Penalty= 0.26701 59 edges
Penalty= 0.2554 60 edges
Penalty= 0.24379 61 edges
Penalty= 0.23218 62 edges
Penalty= 0.22057 64 edges
Penalty= 0.20896 67 edges
Penalty= 0.19736 70 edges
Penalty= 0.18575 74 edges
Penalty= 0.15092 75 edges
Penalty= 0.13931 79 edges
Penalty= 0.12771 81 edges
Penalty= 0.1161 86 edges
Penalty= 0.10449 94 edges
Penalty= 0.09288 97 edges
Penalty= 0.08127 105 edges
Penalty= 0.06966 109 edges
Penalty= 0.05805 121 edges
Penalty= 0.04645 135 edges
Penalty= 0.03484 151 edges
Penalty= 0.02323 172 edges
Penalty= 0.01162 202 edges
Penalty= 1e-05 301 edges
Network Inference: multi.gaussian with coopLasso coupling and AND symmetrization rule applied
| penalty | edges | criteria
1 0,0 -8731
0.18 1,1 -7295
0.15 2,2 -6966
0.14 5,5 -6728
0.13 9,9 -6365
0.12 15,15 -5874
0.11 22,22 -5324
0.1 27,27 -4725
0.09 32,32 -4125
0.08 45,45 -3534
0.07 54,54 -2927
0.06 61,64 -2332
0.05 77,77 -1812
0.04 90,92 -1366
0.03 104,115 -1008
0.02 141,149 -770
0.01 202,208 -584.6
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press return for next plot...
simone documentation built on May 2, 2019, 2:37 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Note Author(s) See Also Examples
Description
The simone function offers an interface to infer networks based
on partial correlation coefficients in various contexts and methods
(steady-state data, time-course data, multiple sample setup,
clustering prior)
Usage
1 2 3 4 5 |
Arguments
X |
a n x p matrix of data, typically n
expression levels associated to the same p genes. Can also
be a |
type |
a character string indicating the data specification (either
|
clustering |
a logical indicating if the network inference should be perfomed by
penalizing the edges according to a latent clustering discovered
during the network structure recovery. Default is |
tasks |
A factor with n entries indicating the task belonging
for each observation in the multiple sample framework. Default is
|
control |
A list that is used to specify low-level options for the
algorithm, defined through the |
Details
Any inference method available ("neighborhood selection",
"graphical-Lasso", "VAR(1) inference" and "multitask learning" - see
simone-package) relies on an optimization problem under
the general form
clustering
is set to TRUE.
The model and the penalty function
penl1 differ according to the context
(steady-state/time-course data, multitask learning and its associated
coupling effect). For further details on the models, please check the
papers listed in the reference section of
simone-package.
The criterion displayed during a SIMoNe run is the value of the penalized likelihood for the current values of the estimor Θhat(λ) corresponding to a given value of the overall penalty level λ.
The following information criteria are also computed for any value of
λ
and part of the output of simone. The BIC (Bayesian
Information Criterion)
and the AIC (Akaike Information Criterion)
Value
Returns an object of class simone, which is list-like and
contains the following:
networks |
a list with all the inferred networks stocked as adjacency matrices (the successive values of
Θ
controled by the penalty level
λ). In
the multiple sample setup, each element of the list is a list with
as many entries as samples or levels in |
penalties |
a vector of the same length as |
n.edges |
a vector of the same length as |
BIC |
a vector of the same length as |
AIC |
a vector of the same length as |
clusters |
a size-p factor indicating the class of each variable. |
weights |
a pxp matrix of weigths used to adapt the penalty to
each entry of the |
control |
a list describing all the posterior values of the parameters used by
the algorithm, to compare with the one set by the
|
Note
If nothing particular is specified about the penalty through the
control list (see setOptions), the default is to
start from a value of
λ
that ensures an empty network. Then
λ is
progressively shrinked, as close to zero as possible. Along the
shrinkage of
λ,
only networks with different numbers of edges are kept in the final
output.
Author(s)
J. Chiquet
See Also
setOptions, plot.simone,
cancer and demo(package="simone").
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## load the breast cancer data set
data(cancer)
attach(cancer)
## launch simone with the default parameters and plot results
plot(simone(expr))
## Not run:
## try with clustering now (clustering is achieved on a 30-edges network)
plot(simone(expr, clustering=TRUE, control=setOptions(clusters.crit=30)))
## try the multiple sample
plot(simone(expr, tasks=status))
## End(Not run)
detach(cancer)
|
Example output
Loading required package: blockmodels
Loading required package: Rcpp
Loading required package: parallel
Loading required package: digest
----------------------------------------------------------------------
'simone' package version 1.0-3
SIMoNe page (http://julien.cremeriefamily.info/simone.html)
----------------------------------------------------------------------
Note that versions >= 1.0-0 are not compatible with versions < 1.0.0.
----------------------------------------------------------------------
Network Inference: neighborhood.selection with AND symmetrization rule applied
| penalty | edges | criteria
1 0 -10336
0.697 1 -9823
0.6667 2 -9790
0.6061 3 -9599
0.5859 4 -9480
0.5758 5 -9380
0.5657 6 -9269
0.5556 7 -9149
0.5455 8 -9012
0.5152 9 -8553
0.5051 11 -8384
0.4849 12 -8027
0.4748 14 -7840
0.4647 15 -7656
0.4546 17 -7466
0.4343 19 -7065
0.4242 22 -6854
0.4141 23 -6634
0.404 24 -6412
0.3939 25 -6191
0.3838 27 -5966
0.3636 30 -5518
0.3535 31 -5298
0.3434 33 -5081
0.3333 37 -4864
0.3232 39 -4650
0.3131 44 -4436
0.303 45 -4227
0.2929 46 -4020
0.2828 48 -3815
0.2727 52 -3613
0.2626 56 -3415
0.2525 61 -3220
0.2424 63 -3031
0.2323 71 -2847
0.2222 74 -2669
0.2121 75 -2496
0.202 77 -2329
0.1919 80 -2167
0.1818 83 -2010
0.1717 87 -1859
0.1616 91 -1714
0.1515 93 -1574
0.1414 95 -1441
0.1313 97 -1317
0.1212 98 -1199
0.1111 101 -1089
0.101 104 -987.5
0.09092 112 -894.9
0.08082 120 -809
0.07072 132 -732.9
0.06062 145 -658.5
0.05051 156 -593.5
0.04041 170 -527.7
0.03031 200 -472.5
0.02021 235 -420.5
0.01011 277 -360.4
1e-05 325 -285.1
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press "Return" or "n+Return" to go forward
Press "p+Return" to go backward
Press "q+Return" to quit
Penalty= 0.70707 0 edges
Penalty= 0.67677 1 edges
Penalty= 0.61617 2 edges
Penalty= 0.59596 3 edges
Penalty= 0.58586 4 edges
Penalty= 0.57576 5 edges
Penalty= 0.56566 6 edges
Penalty= 0.55556 7 edges
Penalty= 0.52526 8 edges
Penalty= 0.51516 9 edges
Penalty= 0.49495 11 edges
Penalty= 0.48485 12 edges
Penalty= 0.47475 14 edges
Penalty= 0.46465 15 edges
Penalty= 0.44445 17 edges
Penalty= 0.43435 19 edges
Penalty= 0.42425 22 edges
Penalty= 0.41415 23 edges
Penalty= 0.40405 24 edges
Penalty= 0.39395 25 edges
Penalty= 0.37374 27 edges
Penalty= 0.36364 30 edges
Penalty= 0.35354 31 edges
Penalty= 0.34344 33 edges
Penalty= 0.33334 37 edges
Penalty= 0.32324 39 edges
Penalty= 0.31314 44 edges
Penalty= 0.30304 45 edges
Penalty= 0.29294 46 edges
Penalty= 0.28284 48 edges
Penalty= 0.27273 52 edges
Penalty= 0.26263 56 edges
Penalty= 0.25253 61 edges
Penalty= 0.24243 63 edges
Penalty= 0.23233 71 edges
Penalty= 0.22223 74 edges
Penalty= 0.21213 75 edges
Penalty= 0.20203 77 edges
Penalty= 0.19193 80 edges
Penalty= 0.18183 83 edges
Penalty= 0.17173 87 edges
Penalty= 0.16162 91 edges
Penalty= 0.15152 93 edges
Penalty= 0.14142 95 edges
Penalty= 0.13132 97 edges
Penalty= 0.12122 98 edges
Penalty= 0.11112 101 edges
Penalty= 0.10102 104 edges
Penalty= 0.09092 112 edges
Penalty= 0.08082 120 edges
Penalty= 0.07072 132 edges
Penalty= 0.06062 145 edges
Penalty= 0.05051 156 edges
Penalty= 0.04041 170 edges
Penalty= 0.03031 200 edges
Penalty= 0.02021 235 edges
Penalty= 0.01011 277 edges
Penalty= 1e-05 325 edges
Network Inference: neighborhood.selection with AND symmetrization rule applied
| penalty | edges | criteria
1 0 -10336
0.697 1 -9823
0.6667 2 -9790
0.6061 3 -9599
0.5859 4 -9480
0.5758 5 -9380
0.5657 6 -9269
0.5556 7 -9149
0.5455 8 -9012
0.5152 9 -8553
0.5051 11 -8384
0.4849 12 -8027
0.4748 14 -7840
0.4647 15 -7656
0.4546 17 -7466
0.4343 19 -7065
0.4242 22 -6854
0.4141 23 -6634
0.404 24 -6412
0.3939 25 -6191
0.3838 27 -5966
0.3636 30 -5518
0.3535 31 -5298
0.3434 33 -5081
0.3333 37 -4864
0.3232 39 -4650
0.3131 44 -4436
0.303 45 -4227
0.2929 46 -4020
0.2828 48 -3815
0.2727 52 -3613
0.2626 56 -3415
0.2525 61 -3220
0.2424 63 -3031
0.2323 71 -2847
0.2222 74 -2669
0.2121 75 -2496
0.202 77 -2329
0.1919 80 -2167
0.1818 83 -2010
0.1717 87 -1859
0.1616 91 -1714
0.1515 93 -1574
0.1414 95 -1441
0.1313 97 -1317
0.1212 98 -1199
0.1111 101 -1089
0.101 104 -987.5
0.09092 112 -894.9
0.08082 120 -809
0.07072 132 -732.9
0.06062 145 -658.5
0.05051 156 -593.5
0.04041 170 -527.7
0.03031 200 -472.5
0.02021 235 -420.5
0.01011 277 -360.4
1e-05 325 -285.1
Found a network with 30 edges.
Network Inference: neighborhood.selection with AND symmetrization rule applied
| penalty | edges | criteria
1.149 0 -315771
0.801 1 -222703
0.7662 2 -213414
0.6965 3 -194713
0.6733 4 -188423
0.6617 5 -185238
0.6501 6 -182042
0.6385 7 -178837
0.6269 8 -175615
0.592 9 -165921
0.5804 11 -162676
0.5572 12 -156182
0.5456 14 -152930
0.534 15 -149679
0.5224 17 -146422
0.4992 19 -139888
0.4876 22 -136615
0.476 23 -133339
0.4644 24 -130064
0.4527 25 -126789
0.4411 27 -123511
0.4179 30 -116956
0.4063 31 -113680
0.3947 32 -110406
0.3831 34 -107131
0.3715 37 -103859
0.3599 38 -100588
0.3483 40 -97322
0.325 42 -90793
0.3134 45 -87529
0.3018 49 -84266
0.2902 54 -81004
0.267 59 -74491
0.2554 60 -71240
0.2438 61 -67992
0.2322 62 -64748
0.2206 64 -61508
0.209 67 -58269
0.1974 70 -55035
0.1857 74 -51804
0.1741 75 -48576
0.1393 79 -38921
0.1277 81 -35713
0.1161 86 -32507
0.1045 94 -29309
0.09288 97 -26115
0.08127 105 -22922
0.06966 109 -19735
0.05805 121 -16558
0.04645 135 -13387
0.03484 151 -10221
0.02323 172 -7067
0.01162 202 -3912
1.149e-05 301 -312
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press "Return" or "n+Return" to go forward
Press "p+Return" to go backward
Press "q+Return" to quit
Penalty= 0.81261 0 edges
Penalty= 0.77778 1 edges
Penalty= 0.70813 2 edges
Penalty= 0.68491 3 edges
Penalty= 0.6733 4 edges
Penalty= 0.6617 5 edges
Penalty= 0.65009 6 edges
Penalty= 0.63848 7 edges
Penalty= 0.60365 8 edges
Penalty= 0.59205 9 edges
Penalty= 0.56883 11 edges
Penalty= 0.55722 12 edges
Penalty= 0.54561 14 edges
Penalty= 0.534 15 edges
Penalty= 0.51079 17 edges
Penalty= 0.49918 19 edges
Penalty= 0.48757 22 edges
Penalty= 0.47596 23 edges
Penalty= 0.46435 24 edges
Penalty= 0.45274 25 edges
Penalty= 0.42953 27 edges
Penalty= 0.41792 30 edges
Penalty= 0.40631 31 edges
Penalty= 0.3947 32 edges
Penalty= 0.38309 34 edges
Penalty= 0.37148 37 edges
Penalty= 0.35988 38 edges
Penalty= 0.33666 40 edges
Penalty= 0.32505 42 edges
Penalty= 0.31344 45 edges
Penalty= 0.30183 49 edges
Penalty= 0.27862 54 edges
Penalty= 0.26701 59 edges
Penalty= 0.2554 60 edges
Penalty= 0.24379 61 edges
Penalty= 0.23218 62 edges
Penalty= 0.22057 64 edges
Penalty= 0.20896 67 edges
Penalty= 0.19736 70 edges
Penalty= 0.18575 74 edges
Penalty= 0.15092 75 edges
Penalty= 0.13931 79 edges
Penalty= 0.12771 81 edges
Penalty= 0.1161 86 edges
Penalty= 0.10449 94 edges
Penalty= 0.09288 97 edges
Penalty= 0.08127 105 edges
Penalty= 0.06966 109 edges
Penalty= 0.05805 121 edges
Penalty= 0.04645 135 edges
Penalty= 0.03484 151 edges
Penalty= 0.02323 172 edges
Penalty= 0.01162 202 edges
Penalty= 1e-05 301 edges
Network Inference: multi.gaussian with coopLasso coupling and AND symmetrization rule applied
| penalty | edges | criteria
1 0,0 -8731
0.18 1,1 -7295
0.15 2,2 -6966
0.14 5,5 -6728
0.13 9,9 -6365
0.12 15,15 -5874
0.11 22,22 -5324
0.1 27,27 -4725
0.09 32,32 -4125
0.08 45,45 -3534
0.07 54,54 -2927
0.06 61,64 -2332
0.05 77,77 -1812
0.04 90,92 -1366
0.03 104,115 -1008
0.02 141,149 -770
0.01 202,208 -584.6
Press return for next plot...
Press return for next plot...
Press return for next plot...
Press return for next plot...
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.