This function returns a representation of the logistic model associated to a COVLMC context from its node in the associated context tree.

## Usage

`model(node, type = c("coef", "full"))`

## Arguments

- node
A

`ctx_node_covlmc`

object as returned by`find_sequence()`

or`contexts.covlmc()`

- type
specifies the model information to return, either the coefficients only (

`type="coef"`

default case) or the full model object (`type="full"`

)

## Value

if `node`

is a context, the coefficients of the logistic model (as a
vector or a matrix depending on the size of the state space) or a logistic
model as a R object. If `node`

is not a context, `NULL`

.

## Details

Full model extraction is only possible if the COVLMC model what not fully
trimmed (see `trim.covlmc()`

). Notice that `find_sequence.covlmc()`

can
produce node that are not context: in this case this function return `NULL`

.

## Examples

```
pc <- powerconsumption[powerconsumption$week == 5, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.5, 1))))
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
m_cov <- covlmc(dts, dts_cov, min_size = 10)
vals <- states(m_cov)
node <- find_sequence(m_cov, c(vals[1], vals[1]))
node
#> Context [T]: (0,1.34], (0,1.34]
#> followed by (0,1.34] (433), (1.34,7.54] (32)
model(node)
#> (Intercept) day_night_1TRUE day_night_2TRUE
#> -2.759353 2.784578 -2.524973
model(node, type = "full")
#>
#> Call: stats::glm(formula = target ~ ., family = stats::binomial(),
#> data = mm, control = stats::glm.control(maxit = options()[["mixvlmc.maxit"]]),
#> model = FALSE, x = FALSE, y = FALSE)
#>
#> Coefficients:
#> (Intercept) day_night_1TRUE day_night_2TRUE
#> -2.759 2.785 -2.525
#>
#> Degrees of Freedom: 464 Total (i.e. Null); 462 Residual
#> Null Deviance: 233
#> Residual Deviance: 226.4 AIC: 232.4
```