metrics_bind: Metric's data preparation

In multilandr: Landscape Analysis at Multiple Spatial Scales

Metric's data preparation

Description

Merge data.frame with metric's values with a data.frame with other data.

Usage

metrics_bind(
  x,
  data,
  raster = NULL,
  classes = NULL,
  radii = NULL,
  c_level = NULL,
  l_level = NULL,
  ext_raster = NULL,
  show_class_names = FALSE
)

Arguments

x	An object of class 'MultiLandMetrics' generated with mland_metrics().
data	A data.frame with data from each sampling point/site. See Details.
raster, ext_raster, classes, radii, l_level, c_level	Parameters to subset data.frame containing the metrics values. See Details.
show_class_names	Logical. If TRUE, classes names will be returned as the names of the classes previously provided (if so) when x was generated. Default FALSE.

Details

Merges data.frame with metrics values, contained in an object of class 'MultiLandMetrics' (returned by mland_metrics()) with a data.frame with other data for each site. In this way, the returned data.frame will be prepared for later statistical or visual analyses. The data.frame provided in data must have a column named "site" or "point_id", containing unique identifiers for each sampling site, which must match with the identifiers present in the data.frame contained in x (i.e. data.frame with metrics values for each site). If "site", the function will assume that the site names are provided as identifiers. If "point_id", the function will assume that point ids are being provided. In any case, these identifiers must match the site identifiers in x.

Argument raster, ext_raster, classes, radii, l_level and c_level can be defined to subset the data.frame contained in x. In each one of these, an all-positive or an all-negative vector can be passed, whether to include (all-postive) or exclude (all-negative) the elements to be taken into account for the subsetting:

• rasterlayers: a numeric vector with the number of the raster layers to be included/excluded. For example: c(1, 2, 4) to include raster layers 1, 2 and 4; c(-2, -3) to exclude raster layers 2 and 3.

• classes: must be a list with as many elements as defined raster layers in argument rasterlayers. Each element of the list must be a numeric vector (classes identities) with the classes to be included/excluded. If provided a character vector, metrics_bind() assumes that classes names are provided. For example, for the case with 2 raster layers: list(c(3, 20, 35), c("Forest", "Crops")) would include classes 3, 20 and 35 from rasterl ayer 1 and classes "Forest" and "Crops" for raster layer 2. For the case of a unique rasterlayer, there is no need to input a list. For example, for the case of a unique raster layer and the exclusion of some classes: c(-5, -10, -15) to exclude classes 5, 10 and 15 of the unique raster layer; c("-Forest", "-Grassland") to exclude classes "Forest" and "Grassland". Note the "-" before each class name to indicate the exclusion of the classes.

• radii: a numeric vector to include/exclude particular radii. For example: c(1000, 2000) to include only radii of 1000 and 2000 m; c(-500, -1500) to exclude radii of 500 and 1500 m.

• c_level: character vector with the class-level metrics to be included/excluded from the analysis. For example: c("np", "pland") will include only the metrics "number of patches" ("np") and "percentage of the landscape" ("pland") in the analysis, whereas c("-np", "-pland") will exclude them. Note the "-" before each metric name to indicate the exclusion of the metrics.

• l_level: character vector with the landscape-level metrics to be included/excluded from the analysis. Extra calculations for extra raster layers are considered as landscape-level metrics, and must be provided as "fun_" + the name of the function.

Value

A data.frame equal to sampling data provided in data but with additional columns containing the values of the metrics for each sampling site.

See Also

mland_metrics()

Examples

# Get sites names from ed_metrics and creates ad-hoc data.frame with random values of
# "richness" (the response variable). Only for the purpose of this example
sites <- ed_metrics@points$name
sampling_data <- data.frame(site = rep(sites, each = 10),
                            richness = sample(1:500, 150))

# With no filters, all columns with all metrics at all spatial scales are added to
# the sampling data
new_data <- metrics_bind(ed_metrics, sampling_data)

# Subset for metrics of class "Forest", radius 5000 and metric "pland"
new_data <- metrics_bind(ed_metrics, sampling_data, show_class_names = TRUE,
                         classes = "Forest", radii = 3000, c_level = "pland")

# In this format, the data.frame can be passed to a fitting model
fit <- lm(richness ~ r1_Forest_pland_3000, data = new_data)

multilandr documentation built on April 3, 2025, 7:39 p.m.