> Reference > > dyneval :: Evaluating methods > Running an evaluation of a method on a set of datasets with a set of parameters

Running an evaluation of a method on a set of datasets with a set of parameters

evaluate_ti_method(dataset, method, parameters, metrics,
  give_priors = NULL, output_model = TRUE, seed = function()
  random_seed(), map_fun = map, verbose = FALSE)

Arguments

dataset	The first trajectory, in most cases a gold standard trajectory
method	One or more methods. Must be one of: an object or list of ti_... objects (eg. `dynmethods::ti_comp1()`), a character vector containing the names of methods to execute (e.g. `"scorpius"`), a character vector containing dockerhub repositories (e.g. `dynverse/paga`), or a dynguidelines data frame.
parameters	A set of parameters to be used during trajectory inference. A parameter set must be a named list of parameters. If multiple methods were provided in the `method` parameter, `parameters` must be an unnamed list of the same length.
metrics	Which metrics to evaluate. Check `dyneval::metrics` for a list of possible metrics. Passing a custom metric function with format `function(dataset, model) { 1 }` is also supported. The name of this function within the list will be used as the name of the metric.
give_priors	All the priors a method is allowed to receive. Must be a subset of all available priors (dynwrap::priors).
output_model	Whether or not the model will be outputted.
seed	A seed to be passed to the TI method.
map_fun	A map function to use when inferring trajectories with multiple datasets or methods. Allows to parallellise the execution in an arbitrary way.
verbose	Whether or not to print information output.