`vignettes/PLNnetwork.Rmd`

`PLNnetwork.Rmd`

This vignette illustrates the standard use of the
`PLNnetwork`

function and the methods accompanying the R6
Classes `PLNnetworkfamily`

and
`PLNnetworkfit`

.

The packages required for the analysis are **PLNmodels**
plus some others for data manipulation and representation:

We illustrate our point with the trichoptera data set, a full description of which can be found in the corresponding vignette. Data preparation is also detailed in the specific vignette.

```
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
```

The `trichoptera`

data frame stores a matrix of counts
(`trichoptera$Abundance`

), a matrix of offsets
(`trichoptera$Offset`

) and some vectors of covariates
(`trichoptera$Wind`

, `trichoptera$Temperature`

,
etc.)

The network model for multivariate count data that we introduce in Chiquet, Robin, and Mariadassou (2019) is a variant of the Poisson Lognormal model of Aitchison and Ho (1989), see the PLN vignette as a reminder. Compare to the standard PLN model we add a sparsity constraint on the inverse covariance matrix \({\boldsymbol\Sigma}^{-1}\triangleq \boldsymbol\Omega\) by means of the \(\ell_1\)-norm, such that \(\|\boldsymbol\Omega\|_1 < c\). PLN-network is the equivalent of the sparse multivariate Gaussian model (Banerjee, Ghaoui, and d’Aspremont 2008) in the PLN framework. It relates some \(p\)-dimensional observation vectors \(\mathbf{Y}_i\) to some \(p\)-dimensional vectors of Gaussian latent variables \(\mathbf{Z}_i\) as follows \[\begin{equation} \begin{array}{rcl} \text{latent space } & \mathbf{Z}_i \sim \mathcal{N}\left({\boldsymbol\mu},\boldsymbol\Omega^{-1}\right) & \|\boldsymbol\Omega\|_1 < c \\ \text{observation space } & Y_{ij} | Z_{ij} \quad \text{indep.} & Y_{ij} | Z_{ij} \sim \mathcal{P}\left(\exp\{Z_{ij}\}\right) \end{array} \end{equation}\]

The parameter \({\boldsymbol\mu}\) corresponds to the main effects and the latent covariance matrix \(\boldsymbol\Sigma\) describes the underlying structure of dependence between the \(p\) variables.

The \(\ell_1\)-penalty on \(\boldsymbol\Omega\) induces sparsity and selection of important direct relationships between entities. Hence, the support of \(\boldsymbol\Omega\) correspond to a network of underlying interactions. The sparsity level (\(c\) in the above mathematical model), which corresponds to the number of edges in the network, is controlled by a penalty parameter in the optimization process sometimes referred to as \(\lambda\). All mathematical details can be found in Chiquet, Robin, and Mariadassou (2019).

Just like PLN, PLN-network generalizes to a formulation close to a multivariate generalized linear model where the main effect is due to a linear combination of \(d\) covariates \(\mathbf{x}_i\) and to a vector \(\mathbf{o}_i\) of \(p\) offsets in sample \(i\). The latent layer then reads \[\begin{equation} \mathbf{Z}_i \sim \mathcal{N}\left({\mathbf{o}_i + \mathbf{x}_i^\top\mathbf{B}},\boldsymbol\Omega^{-1}\right), \qquad \|\boldsymbol\Omega\|_1 < c , \end{equation}\] where \(\mathbf{B}\) is a \(d\times p\) matrix of regression parameters.

Regularization via sparsification of \(\boldsymbol\Omega\) and visualization of the consecutive network is the main objective in PLN-network. To reach this goal, we need to first estimate the model parameters. Inference in PLN-network focuses on the regression parameters \(\mathbf{B}\) and the inverse covariance \(\boldsymbol\Omega\). Technically speaking, we adopt a variational strategy to approximate the \(\ell_1\)-penalized log-likelihood function and optimize the consecutive sparse variational surrogate with an optimization scheme that alternates between two step

- a gradient-ascent-step, performed with the CCSA algorithm of Svanberg (2002) implemented in the C++ library (Johnson 2011), which we link to the package.
- a penalized log-likelihood step, performed with the graphical-Lasso
of Friedman, Hastie, and Tibshirani (2008), implemented in the package
**fastglasso**(Sustik and Calderhead 2012).

More technical details can be found in Chiquet, Robin, and Mariadassou (2019)

In the package, the sparse PLN-network model is adjusted with the
function `PLNnetwork`

, which we review in this section. This
function adjusts the model for a series of value of the penalty
parameter controlling the number of edges in the network. It then
provides a collection of objects with class `PLNnetworkfit`

,
corresponding to networks with different levels of density, all stored
in an object with class `PLNnetworkfamily`

.

`PLNnetwork`

finds an hopefully appropriate set of
penalties on its own. This set can be controlled by the user, but use it
with care and check details in `?PLNnetwork`

. The collection
of models is fitted as follows:

`network_models <- PLNnetwork(Abundance ~ 1 + offset(log(Offset)), data = trichoptera)`

```
##
## Initialization...
## Adjusting 30 PLN with sparse inverse covariance estimation
## Joint optimization alternating gradient descent and graphical-lasso
## sparsifying penalty = 3.643028 sparsifying penalty = 3.364959 sparsifying penalty = 3.108114 sparsifying penalty = 2.870875 sparsifying penalty = 2.651743 sparsifying penalty = 2.449338 sparsifying penalty = 2.262382 sparsifying penalty = 2.089696 sparsifying penalty = 1.930192 sparsifying penalty = 1.782862 sparsifying penalty = 1.646778 sparsifying penalty = 1.52108 sparsifying penalty = 1.404978 sparsifying penalty = 1.297737 sparsifying penalty = 1.198682 sparsifying penalty = 1.107187 sparsifying penalty = 1.022677 sparsifying penalty = 0.9446167 sparsifying penalty = 0.8725149 sparsifying penalty = 0.8059166 sparsifying penalty = 0.7444017 sparsifying penalty = 0.6875821 sparsifying penalty = 0.6350996 sparsifying penalty = 0.586623 sparsifying penalty = 0.5418465 sparsifying penalty = 0.5004879 sparsifying penalty = 0.4622861 sparsifying penalty = 0.4270002 sparsifying penalty = 0.3944076 sparsifying penalty = 0.3643028
## Post-treatments
## DONE!
```

Note the use of the `formula`

object to specify the model,
similar to the one used in the function `PLN`

.

`PLNnetworkfamily`

The `network_models`

variable is an `R6`

object
with class `PLNnetworkfamily`

, which comes with a couple of
methods. The most basic is the `show/print`

method, which
sends a very basic summary of the estimation process:

`network_models`

```
## --------------------------------------------------------
## COLLECTION OF 30 POISSON LOGNORMAL MODELS
## --------------------------------------------------------
## Task: Network Inference
## ========================================================
## - 30 penalties considered: from 0.3643028 to 3.643028
## - Best model (greater BIC): lambda = 0.462
## - Best model (greater EBIC): lambda = 0.744
```

One can also easily access the successive values of the criteria in the collection

param | nb_param | loglik | BIC | ICL | n_edges | EBIC | pen_loglik | density | stability |
---|---|---|---|---|---|---|---|---|---|

3.643028 | 34 | -1286.143 | -1352.304 | -2786.264 | 0 | -1352.304 | -1292.767 | 0.0000000 | NA |

3.364959 | 34 | -1280.104 | -1346.265 | -2783.682 | 0 | -1346.265 | -1286.439 | 0.0000000 | NA |

3.108114 | 34 | -1271.429 | -1337.590 | -2762.833 | 0 | -1337.590 | -1277.650 | 0.0000000 | NA |

2.870875 | 34 | -1261.099 | -1327.260 | -2727.954 | 0 | -1327.260 | -1267.330 | 0.0000000 | NA |

2.651743 | 34 | -1257.046 | -1323.207 | -2735.816 | 0 | -1323.207 | -1262.917 | 0.0000000 | NA |

2.449338 | 35 | -1249.456 | -1317.563 | -2719.589 | 1 | -1320.019 | -1255.195 | 0.0069204 | NA |

A diagnostic of the optimization process is available via the
`convergence`

field:

param | nb_param | status | backend | objective | iterations | convergence | |
---|---|---|---|---|---|---|---|

out | 3.643028 | 34 | 3 | nlopt | 1286.142854 | 6.000000 | 0.000005 |

elt | 3.364959 | 34 | 4 | nlopt | 1280.103846 | 7.000000 | 0.000001 |

elt.1 | 3.108114 | 34 | 4 | nlopt | 1271.428998 | 7.000000 | 0.000001 |

elt.2 | 2.870875 | 34 | 3 | nlopt | 1261.098935 | 6.000000 | 0.000008 |

elt.3 | 2.651743 | 34 | 3 | nlopt | 1257.045656 | 8.000000 | 0.000003 |

elt.4 | 2.449338 | 35 | 4 | nlopt | 1249.456251 | 8.000000 | 0.000008 |

An nicer view of this output comes with the option “diagnostic” in
the `plot`

method:

`plot(network_models, "diagnostic")`

By default, the `plot`

method of
`PLNnetworkfamily`

displays evolution of the criteria
mentioned above, and is a good starting point for model selection:

`plot(network_models)`

Note that we use the original definition of the BIC/ICL criterion
(\(\texttt{loglik} -
\frac{1}{2}\texttt{pen}\)), which is on the same scale as the
log-likelihood. A popular
alternative consists in using \(-2\texttt{loglik} + \texttt{pen}\) instead.
You can do so by specifying `reverse = TRUE`

:

`plot(network_models, reverse = TRUE)`

In this case, the variational lower bound of the log-likelihood is
hopefully strictly increasing (or rather decreasing if using
`reverse = TRUE`

) with a lower level of penalty (meaning more
edges in the network). The same holds true for the penalized counterpart
of the variational surrogate. Generally, smoothness of these criteria is
a good sanity check of optimization process. BIC and its
extended-version high-dimensional version EBIC are classically used for
selecting the correct amount of penalization with sparse estimator like
the one used by PLN-network. However, we will consider later a more
robust albeit more computationally intensive strategy to chose the
appropriate number of edges in the network.

To pursue the analysis, we can represent the coefficient path (i.e.,
value of the edges in the network according to the penalty level) to see
if some edges clearly come off. An alternative and more intuitive view
consists in plotting the values of the partial correlations along the
path, which can be obtained with the options `corr = TRUE`

.
To this end, we provide the S3 function
`coefficient_path`

:

```
coefficient_path(network_models, corr = TRUE) %>%
ggplot(aes(x = Penalty, y = Coeff, group = Edge, colour = Edge)) +
geom_line(show.legend = FALSE) + coord_trans(x="log10") + theme_bw()
```

To select a network with a specific level of penalty, one uses the
`getModel(lambda)`

S3 method. We can also extract the best
model according to the BIC or EBIC with the method
`getBestModel()`

.

```
model_pen <- getModel(network_models, network_models$penalties[20]) # give some sparsity
model_BIC <- getBestModel(network_models, "BIC") # if no criteria is specified, the best BIC is used
```

An alternative strategy is to use StARS (Liu, Roeder, and Wasserman 2010), which performs resampling to evaluate the robustness of the network along the path of solutions in a similar fashion as the stability selection approach of Meinshausen and Bühlmann (2010), but in a network inference context.

Resampling can be computationally demanding but is easily
parallelized: the function `stability_selection`

integrates
some features of the **future** package to perform parallel
computing. We set our plan to speed the process by relying on 2
workers:

We first invoke `stability_selection`

explicitly for
pedagogical purpose. In this case, we need to build our sub-samples
manually:

```
n <- nrow(trichoptera)
subs <- replicate(10, sample.int(n, size = n/2), simplify = FALSE)
stability_selection(network_models, subsamples = subs)
```

```
##
## Stability Selection for PLNnetwork:
## subsampling: ++++++++++
```

Requesting ‘StARS’ in `gestBestmodel`

automatically
invokes `stability_selection`

with 20 sub-samples, if it has
not yet been run.

`model_StARS <- getBestModel(network_models, "StARS")`

When “StARS” is requested for the first time,
`getBestModel`

automatically calls the method
`stability_selection`

with the default parameters. After the
first call, the stability path is available from the `plot`

function:

`plot(network_models, "stability")`

When you are done, do not forget to get back to the standard
sequential plan with *future*.

`future::plan("sequential")`

`PLNnetworkfit`

The variables `model_BIC`

, `model_StARS`

and
`model_pen`

are other `R6Class`

objects with class
`PLNnetworkfit`

. They all inherits from the class
`PLNfit`

and thus own all its methods, with a couple of
specific one, mostly for network visualization purposes. Most fields and
methods are recalled when such an object is printed:

`model_StARS`

```
## Poisson Lognormal with sparse inverse covariance (penalty = 1.4)
## ==================================================================
## nb_param loglik BIC ICL n_edges EBIC pen_loglik density
## 35 -1203.858 -1271.965 -2608.229 1 -1274.422 -1208.642 0.007
## ==================================================================
## * Useful fields
## $model_par, $latent, $latent_pos, $var_par, $optim_par
## $loglik, $BIC, $ICL, $loglik_vec, $nb_param, $criteria
## * Useful S3 methods
## print(), coef(), sigma(), vcov(), fitted()
## predict(), predict_cond(), standard_error()
## * Additional fields for sparse network
## $EBIC, $density, $penalty
## * Additional S3 methods for network
## plot.PLNnetworkfit()
```

The `plot`

method provides a quick representation of the
inferred network, with various options (either as a matrix, a graph, and
always send back the plotted object invisibly if users needs to perform
additional analyses).

```
my_graph <- plot(model_StARS, plot = FALSE)
my_graph
```

```
## IGRAPH d18834a UNW- 17 1 --
## + attr: name (v/c), label (v/c), label.cex (v/n), size (v/n),
## | label.color (v/c), frame.color (v/l), weight (e/n), color (e/c),
## | width (e/n)
## + edge from d18834a (vertex names):
## [1] Hfo--Hsp
```

`plot(model_StARS)`

`plot(model_StARS, type = "support", output = "corrplot")`

We can finally check that the fitted value of the counts – even with sparse regularization of the covariance matrix – are close to the observed ones:

```
data.frame(
fitted = as.vector(fitted(model_StARS)),
observed = as.vector(trichoptera$Abundance)
) %>%
ggplot(aes(x = observed, y = fitted)) +
geom_point(size = .5, alpha =.25 ) +
scale_x_log10(limits = c(1,1000)) +
scale_y_log10(limits = c(1,1000)) +
theme_bw() + annotation_logticks()
```

Aitchison, J., and C. H. Ho. 1989. “The Multivariate Poisson-Log
Normal Distribution.” *Biometrika* 76 (4): 643–53.

Banerjee, Onureena, Laurent El Ghaoui, and Alexandre d’Aspremont. 2008.
“Model Selection Through Sparse Maximum Likelihood Estimation for
Multivariate Gaussian or Binary Data.” *Journal of Machine
Learning Research* 9 (Mar): 485–516.

Chiquet, Julien, Stephane Robin, and Mahendra Mariadassou. 2019.
“Variational Inference for Sparse Network Reconstruction from
Count Data.” In *Proceedings of the 36th International
Conference on Machine Learning*, edited by Kamalika Chaudhuri and
Ruslan Salakhutdinov, 97:1162–71. Proceedings of Machine Learning
Research. Long Beach, California, USA: PMLR. http://proceedings.mlr.press/v97/chiquet19a.html.

Friedman, J., T. Hastie, and R. Tibshirani. 2008. “Sparse Inverse
Covariance Estimation with the Graphical Lasso.”
*Biostatistics* 9 (3): 432–41.

Johnson, Steven G. 2011. *The NLopt Nonlinear-Optimization
Package*. https://nlopt.readthedocs.io/en/latest/.

Liu, Han, Kathryn Roeder, and Larry Wasserman. 2010. “Stability
Approach to Regularization Selection (StARS) for High Dimensional
Graphical Models.” In *Proceedings of the 23rd International
Conference on Neural Information Processing Systems - Volume 2*,
1432–40. USA.

Meinshausen, Nicolai, and Peter Bühlmann. 2010. “Stability
Selection.” *Journal of the Royal Statistical Society: Series
B (Statistical Methodology)* 72 (4): 417–73.

Sustik, Mátyás A, and Ben Calderhead. 2012. “GLASSOFAST: An
Efficient GLASSO Implementation.” *UTCS Technical Report
TR-12-29 2012*.

Svanberg, Krister. 2002. “A Class of Globally Convergent
Optimization Methods Based on Conservative Convex Separable
Approximations.” *SIAM Journal on Optimization* 12 (2):
555–73.