Filter to retain only variables that have low collinearity
Source:R/filter_collinear.R
filter_collinear.RdThis 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.
Usage
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)
# Default S3 method
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)
# S3 method for class '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 class '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 class '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 class 'matrix'
filter_collinear(
x,
cutoff = NULL,
verbose = FALSE,
names = TRUE,
to_keep = NULL,
method = "cor_caret",
cor_type = "pearson",
max_cells = Inf,
...
)Arguments
- x
A
terra::SpatRasterorstarsobject, a data.frame (with only numeric variables)- cutoff
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
- verbose
A boolean whether additional information should be provided on the screen
- names
a logical; should the column names be returned
TRUEor the column indexFALSE)?- to_keep
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).
- method
character. One of "cor_caret", "vif_cor" or "vif_step".
- cor_type
character. For methods that use correlation, which type of correlation: "pearson", "kendall", or "spearman". Defaults to "pearson"
- max_cells
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
- exhaustive
boolean. Used only for
terra::SpatRasterwhen downsampling tomax_cells, if we require theexhaustiveapproach interra::spatSample(). This is only needed for rasters that are very sparse and not too large, see the help page ofterra::spatSample()for details.
Value
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).