IEAtools is an R package that provides supporting functions for conducting Integrated Ecosystem Assessments (IEA).
The package is in its early development and not yet on CRAN. To install the development version from GitHub directly use the remotes package:
# install.packages("remotes")
remotes::install_github("saskiaotto/IEAtools")If you encounter a clear bug, please file a minimal reproducible example on github. For questions email me any time.
Here is a short overview of functions and methods included in the package:
- Explore gaps in the multiple time series missing values:
explore_na()- → This function creates an image plot where available values for the different variables and years are indicated in gray and missing values in white. At the right side and bottom, two barplots are added showing the frequency of available variables per year and the available years per variable.
- Impute missing values by local mean:
impute()- → The function substitutes missing values in time series with a mean of the previous and following years (or less depending on the position of NAs, whether it is at the beginning or end of the time series, and the presence of further NAs in the selected time period).
- Calculate the variance inflation factor (VIF):
calc_vif() - Compute (partial) autocorrelation functions and test for
significance:
test_tac() - Create a traffic light plot or heatmap:
trafficlight()- → This function creates for multiple time series an image plot where the color code is based on selected quantiles or evenly spaced intervals.
Two approaches are included in IEAtools that are based on trajectories in state space to determine the current state of the system in comparison to an earlier period as reference using the selected IND suite (state space = n-dimensional space of possible locations of variables). The functions are imported from the INDperform package and will be in the future only available in IEAtools.
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Calculation of the Euclidean distance in state space of any dimensionality between each single year (or any other time step used) and a defined reference year.
statespace_ed()calculates the Euclidean distance over time.plot_statespace_ed()creates a ggplot2 object of the Euclidean distance trend.
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Given the identification of a reference domain in state space, more recent observations might lie within or outside this domain. The convex hull is a multivariate measure derived from computational geometry representing the smallest convex set containing all the reference points in Euclidean plane or space. For visualization, only 2 dimensions considered (dimension reduction through e.g. Principal Component Analysis suggested).
statespace_ch()calculates the convex hull for 2 defined periods (current and reference) in the x-y space (i.e. 2 IND or 2 Principal Components).plot_statespace_ch()creates a ggplot2 object showing all observed combinations in x-y space as well as the convex hull of both periods. The proportion of the recent time period within the reference space is additionally provided.