# nrm

##### Fitting gHypEG regression models for multi-edge networks.

nrm is used to fit multi-edge network regression models.

- Keywords
- multivariate, models, regression, nonlinear, SNA

##### Usage

```
nrm(
w,
adj,
xi = NULL,
pval = 0.01,
directed = FALSE,
selfloops = FALSE,
regular = FALSE,
...
)
```# S3 method for default
nrm(
w,
adj,
xi = NULL,
pval = 0.01,
directed = FALSE,
selfloops = FALSE,
regular = FALSE,
ci = TRUE,
significance = FALSE,
null = FALSE,
init = NULL,
...
)

# S3 method for nrm
print(x, suppressCall = FALSE, ...)

##### Arguments

- w
an object of class

`'list'`

containing the predictors layers (explanatory variables/covariates) of the multiplex, passed as adjacency matrices. The entries of the list can be named.- adj
matrix. The adjacency matrix of the response network (dependent variable).

- xi
optional matrix. Passes a non-standard \(\Xi\) matrix.

- pval
the significance level used to compute confidence intervals of the parameters. Per default, set to 0.01.

- directed
logical. If

`TRUE`

the response variable is considered the adjacency matrix of directed graph. If`FALSE`

only the upper triangular of`adj`

is considered. Default set to FALSE.- selfloops
logical. Whether selfloops are allowed. Default set to FALSE.

- regular
logical. Whether the gHypEG regression should be performed with correction of combinatorial effects (

`TRUE`

) or without (`FALSE`

).- …
optional arguments to print or plot methods.

- ci
logical. Whether to compute confidences for the parameters. Defaults to

`TRUE`

.- significance
logical. Whether to test the model significance against the null by means of lr-test.

- null
logical. Is this a null model? Used for internal routines.

- init
numeric. Vector of initial values used for numerical MLE. If only a single value is passed, this is repeated to match the number of predictors in

`w`

.- x
object of class

`'nrm'`

- suppressCall
logical, indicating whether to print the call that generated x

##### Value

nrm returns an object of class 'nrm'.

The function summary is used to obtain and print a summary and analysis of the results. The generic accessory functions coefficients, etc, extract various useful features of the value returned by nrm.

An object of class 'nrm' is a list containing at least the following components:

a named vector of coefficients.

a named matrix with confidence intervals and standard deviation for each coefficient.

the estimated propensity matrix.

the matrix of possibilities.

log-likelihood of the estimated model.

AIC of the estimated model.

Mc Fadden pseudo R-squared

Cox and Snells pseudo R-squared

the p-value of the likelihood-ratio test for the estimated model against the null.

##### Methods (by class)

`default`

: Default method for nrm`nrm`

: Print method for elements of class`'nrm'`

.

##### References

Casiraghi, Giona. 'Multiplex Network Regression: How do relations drive interactions?.' arXiv preprint arXiv:1702.02048 (2017).

##### See Also

##### Examples

```
# NOT RUN {
## For a complete example see the vignette
data('highschool.predictors')
highschool.m <- nrm(w=highschool.predictors[1], adj=contacts.adj, directed=FALSE,
selfloops=FALSE)
highschool.m
# }
# NOT RUN {
data('highschool.predictors')
highschool.m <- nrm(w=highschool.predictors, adj=contacts.adj, directed=FALSE,
selfloops=FALSE)
highschool.m
# }
# NOT RUN {
# }
```

*Documentation reproduced from package ghypernet, version 1.0.0, License: AGPL-3*