This method finds a subset of variables that have low collinearity. It
provides three methods: cor_caret, a stepwise approach to remove variables
with a pairwise correlation above a given cutoff, choosing the variable with
the greatest mean correlation (based on the algorithm in
caret::findCorrelation); vif_step, a stepwise approach to remove
variables with an variance inflation factor above a given cutoff (based on
the algorithm in usdm::vifstep), and vif_cor, a stepwise approach that,
at each step, find the pair of variables with the highest correlation above
the cutoff and removes the one with the largest vif. such that all have a
correlation below a certain cutoff. There are methods for
terra::SpatRaster, data.frame and matrix. For
terra::SpatRaster and data.frame, only numeric variables will be
considered.
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)# S3 method for default
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)
# S3 method for stars
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
exhaustive = FALSE,
...
)
# S3 method for SpatRaster
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
exhaustive = FALSE,
...
)
# S3 method for data.frame
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)
# S3 method for matrix
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)
A vector of names of columns that are below the correlation
threshold (when names = TRUE), otherwise a vector of indices. Note
that the indices are only for numeric variables (i.e. if factors are
present, the indices do not take them into account).
A terra::SpatRaster or stars object, a data.frame (with only
numeric variables)
A numeric value used as a threshold to remove variables. For, "cor_caret" and "vif_cor", it is the pair-wise absolute correlation cutoff, which defaults to 0.7. For "vif_step", it is the variable inflation factor, which defaults to 10
A boolean whether additional information should be provided on the screen
a logical; should the column names be returned TRUE or the
column index FALSE)?
A vector of variable names that we want to force in the set (note that the function will return an error if the correlation among any of those variables is higher than the cutoff).
character. One of "cor_caret", "vif_cor" or "vif_step".
character. For methods that use correlation, which type of correlation: "pearson", "kendall", or "spearman". Defaults to "pearson"
positive integer. The maximum number of cells to be used. If this is smaller than ncell(x), a regular sample of x is used
additional arguments specific to a given object type
boolean. Used only for terra::SpatRaster when
downsampling to max_cells, if we require the exhaustive approach in
terra::spatSample(). This is only needed for rasters that are very sparse
and not too large, see the help page of terra::spatSample() for details.
for cor_caret: Original R code by Dong Li, modified by Max Kuhn and
Andrea Manica; for vif_step and vif_cor, original algorithm by Babak
Naimi, rewritten by Andrea Manica for tidysdm
Naimi, B., Hamm, N.A.S., Groen, T.A., Skidmore, A.K., and Toxopeus, A.G. 2014. Where is positional uncertainty a problem for species distribution modelling?, Ecography 37 (2): 191-203.