bind_metrics: Metric's data preparation

In phuais/multilandR: 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

bind_metrics(
  x,
  data,
  raster = NULL,
  classes = NULL,
  radii = NULL,
  c_level = NULL,
  l_level = NULL,
  ext_raster = NULL,
  classnames = FALSE
)

Arguments

x	An object of class 'MultiLandMetrics' generated with 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.
classnames	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

The function simply merge data.frame with metrics values, contained in an object of class 'MultiLandMetrics' (returned by 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 rasterlayers to be included/excluded. For example: c(1, 2, 4) to include rasterlayers 1, 2 and 4; c(-2, -3) to exclude rasterlayers 2 and 3.

• classes: must be a list with as many elements as defined rasterlayers 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, bind_metrics() assumes that classes names are provided. For example, for the case with 2 rasterlayers: list(c(3, 20, 35), c("Forest", "Crops")) would include classes 3, 20 and 35 from rasterlayer 1 and classes "Forest" and "Crops" for rasterlayer 2. For the case of a unique rasterlayer, there is no need to input a list. For example, for the case of a unique rasterlayer and the exclusion of some classes: c(-5, -10, -15) to exclude classes 5, 10 and 15 of the unique rasterlayer; 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 rasterlayers 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

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 <- bind_metrics(ed_metrics, sampling_data)

# Subset for metrics of class "Forest", radius 5000 and metric "pland"
new_data <- bind_metrics(ed_metrics, sampling_data, classnames = 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)

phuais/multilandR documentation built on Feb. 11, 2024, 9:27 p.m.