palaeodataMemory: Output of 'computeMemory'

In BlasBenito/memoria: Quantifying Ecological Memory in Palaeoecological Datasets and Other Long Time-Series

Description

List containing the output of computeMemory applied to palaeodataLagged. Its slots are:

Usage

data(palaeodataMemory)

Format

List with four slots.

Details

• 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.

Author(s)

Blas M. Benito <[e-mail]>

BlasBenito/memoria documentation built on Feb. 20, 2022, 1:45 a.m.