Extract the coefficients and confidence intervals of a fitted causal model.

coef() returns the path coefficients as a named vector, and confint() the bounds of their confidence intervals. Both name each path as "from -> to" and return them in the same order, so they can be combined with cbind(). Paths that are absent from the causal model are not included. For a data frame with the standard errors alongside the coefficients, see as.data.frame.fitted_DAG().

Usage

# S3 method for class 'fitted_DAG'
coef(object, ...)

# S3 method for class 'fitted_DAG'
confint(object, parm, level = 0.95, ...)

Arguments

object: An object of class fitted_DAG.
...: Ignored, present for consistency with the generic.
parm: The paths for which to return intervals, given as names or indices. Defaults to all of them.
level: The confidence level, which has to be 0.95 for now.

Value

For coef(), a named numeric vector with one element per path. For confint(), a matrix with a row per path and the lower and upper bound in its two columns.

Details

Note that these are not the parametric intervals that confint() returns for most model objects, but the ones that were stored when the model was made: bootstrap intervals after fitting with boot, and normal intervals around the averaged estimate for a model from average().

Examples

  d <- DAG(LS ~ BM, NL ~ BM, DD ~ NL + LS)
  d_fitted <- est_DAG(d, rhino, rhino_tree, 'lambda')
  coef(d_fitted)
#>    BM -> NL    BM -> LS    NL -> DD    LS -> DD 
#>  0.43034428  0.49739369  0.63125366 -0.01185413 

  # Confidence intervals require the model to be fitted with bootstrapping.
  # \donttest{
    d_boot <- est_DAG(d, rhino, rhino_tree, 'lambda', boot = 100)
    confint(d_boot)
#>               2.5 %    97.5 %
#> BM -> NL  0.2882040 0.5875534
#> BM -> LS  0.3105099 0.6676275
#> NL -> DD  0.4704635 0.7977846
#> LS -> DD -0.2205770 0.1150009
    cbind(coef = coef(d_boot), confint(d_boot))
#>                 coef      2.5 %    97.5 %
#> BM -> NL  0.43034428  0.2882040 0.5875534
#> BM -> LS  0.49739369  0.3105099 0.6676275
#> NL -> DD  0.63125366  0.4704635 0.7977846
#> LS -> DD -0.01185413 -0.2205770 0.1150009
  # }