pmxNODE (version 0.1.0)

der_state_plot_nlmixr: Generate Derivative versus State Plot (nlmixr2)

Description

This functions allows to generate a derivative versus state plot for a neural network from a NODE in nlmixr2

Usage

der_state_plot_nlmixr(
  nn_name,
  min_state = NULL,
  max_state = NULL,
  inputs = NULL,
  est_parms = NULL,
  fit_obj = NULL,
  length_out = 100,
  time_nn = FALSE,
  act = "ReLU",
  plot_type = c("base", "ggplot"),
  beta = 20
)

Value

Displaying derivative versus state plot; returns ggplot-object if plot_type="ggplot"

Arguments

nn_name

(string) Name of the NN, e.g., “c” for NNc(...)

min_state

(numeric) Value of minimal state for which the derivative should be calculated (optional if inputs is given, ignored if inputs is defined)

max_state

(numeric) Value of maximal state for which the derivative should be calculated (optional if inputs is given, ignored if inputs is defined)

inputs

(numeric vector) Vector of input values for which derivatives should be calculated (optional if min_state and max_state is given)

est_parms

(named vector; semi-optional) Named vector of estimated parameters from the NN extracted through fit$fixef. For optionality, see Details.

fit_obj

(nlmixr fit object; semi-optional) The fit-object from nlmixr2(...). For optionality, see Details.

length_out

(numeric) Number of states between min_state and max_state for derivative calculations.

time_nn

(boolean) Whether the neural network to analyze is a time-dependent neural network or not. Default values is FALSE.

act

(string) Activation function used in the NN. Currently "ReLU" and "Softplus" available.

plot_type

(string) What plot type should be used; "base" or "ggplot"

beta

(numeric) Beta value for the Softplus activation function, only applicable if act="Softplus"; Default to 20.

Author

Dominic Bräm

Details

Either est_parms or fit_obj must be given. If both arguments are given, est_parms is prioritized.

Examples

if (FALSE) {
pop_fit <- nlmixr2(node_model_pop,data=data,est="bobyqa")
der_state_plot <- der_state_plot_nlmixr(nn="c",
                                        min_state=0,max_state=10,
                                        fit_obj=pop_fit,
                                        plot_type="ggplot")
}