Computes ecological memory patterns on simulated pollen curves produced by the virtualPollen library.

Applies computeMemory to assess ecological memory on a large set of virtual pollen curves.

runExperiment(
 simulations.file = NULL,
 selected.rows = 1,
 selected.columns = 1,
 parameters.file = NULL,
 parameters.names = NULL,
 sampling.names = NULL,
 driver.column = NULL,
 response.column = "Response_0",
 subset.response = "none",
 time.column = "Time",
 time.zoom = NULL,
 lags = NULL,
 repetitions = 10
 )

Arguments

simulations.file

list of dataframes, output of the function simulatePopulation of the virtualPollen library.
selected.rows

numeric vector, rows (virtual taxa) of simulations.file to be analyzed.
selected.columns

numeric.vector, columns (experiment treatments) of simulations.file to be analyzed.
parameters.file

dataframe of simulation parameters.
parameters.names

vector of character strings with names of traits and niche features from parameters.file to be included in the analysis (i.e. c("maximum.age", "fecundity", "niche.A.mean", "niche.A.sd"))
sampling.names

vector of character strings with the names of the columns of simulations.file.
driver.column

vector of character strings, names of the columns to be considered as drivers (generally, one of "Suitability", "Driver.A", "Driver.B).
response.column

character string defining the response variable, typically "Response_0".
subset.response

character string, one of "up", "down" or "none", triggers the subsetting of the input dataset. "up" only models ecological memory on cases where the response's trend is positive, "down" selectes cases with negative trends, and "none" selects all cases.
time.column

character string, name of the time/age column. Usually, "Time".
time.zoom

numeric vector with two numbers defining the time/age extremes of the time interval of interest.
lags

ags numeric vector of positive integers, lags to be used in the equation. Generally, a regular sequence of numbers, in the same units as time. The use seq to define it is highly recommended. If 0 is absent from lags, it is added automatically to allow the consideration of a concurrent effect. Lags should take into account the temporal resolution of the data, and be aligned to it. For example, if the interval between consecutive samples is 100 years, lags should be something like 0, 100, 200, 300. Lags can also be multiples of the time resolution, such as 0, 200, 400, 600 (in the case time resolution is 100 years).
repetitions

integer, number of random forest models to fit.

Value

A list with 2 slots:

  • names matrix of character strings, with as many rows and columns as simulations.file. Each cell holds a simulation name to be used afterwards, when plotting the results of the ecological memory analysis.

  • output a list with as many columns and columns as simulations.file. Each slot holds a an output of computeMemory.

    • memory dataframe with five columns:

      • Variable character, names and lags of the different variables used to model ecological memory.

      • median numeric, median importance across repetitions of the given Variable according to Random Forest.

      • sd numeric, standard deviation of the importance values of the given Variable across repetitions.

      • min and max numeric, percentiles 0.05 and 0.95 of importance values of the given Variable across repetitions.

    • R2 vector, values of pseudo R-squared value obtained for the Random Forest model fitted on each repetition. Pseudo R-squared is the Pearson correlation beteween the observed and predicted data.

    • prediction dataframe, with the same columns as the dataframe in the slot memory, with the median and confidence intervals of the predictions of all random forest models fitted.

    • multicollinearity multicollinearity analysis on the input data performed with vif. A vif value higher than 5 indicates that the given variable is highly correlated with other variables.

See also

computeMemory