Function to find strong correlations within environmental predictors
Source:R/correlation_finder.R
correlation_finder.RdThis function identifies variables with strong correlations based on a specified threshold.
Arguments
- environmental_data
A matrix or data.frame containing environmental data.
- method
Method used to estimate the correlation matrix. Possible options include "spearman" (Spearman's rank correlation), "pearson" (Pearson's correlation), or "kendall" (Kendall's tau correlation).
- threshold
Correlation threshold value. Variables with absolute correlation values greater than or equal to this threshold are considered strongly correlated.
- verbose
Logical. If
TRUE, prints verbose output detailing correlations.
Value
A list with two elements:
not_correlated_vars: A vector containing names of variables that are not strongly correlated.correlation_values: A list with correlation values for all pairs of variables.
Examples
# \donttest{
library(tenm)
data("abronia")
tempora_layers_dir <- system.file("extdata/bio",package = "tenm")
abt <- tenm::sp_temporal_data(occs = abronia,
longitude = "decimalLongitude",
latitude = "decimalLatitude",
sp_date_var = "year",
occ_date_format="y",
layers_date_format= "y",
layers_by_date_dir = tempora_layers_dir,
layers_ext="*.tif$")
abtc <- tenm::clean_dup_by_date(abt,threshold = 10/60)
future::plan("multisession",workers=2)
abex <- tenm::ex_by_date(abtc,train_prop=0.7)
future::plan("sequential")
envdata <- abex$env_data[,-ncol(abex$env_data)]
ecors <- tenm::correlation_finder(environmental_data =envdata,
method="spearman",
threshold = 0.7 )
#> Warning: the standard deviation is zero
#> *****************************************************************
#>
#> Here is a list of variables that can summarize your niche
#> information, according to the threshold of 0.7 :
#>
#> bio_01 bio_02 bio_03 bio_04 bio_12 bio_14 bio_15
#>
#> *****************************************************************
#>
#> ----------------------------------------------------------------
#>
#> Correlation list:
#>
#> Variable bio_01 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 1.0000000 0.9720096 0.8622147 0.9762799 0.9389854 0.9884480 0.9708369
#> ----------------------------------------------------------------
#>
#> Variable bio_02 is strongly correlated with:
#>
#> bio_02 bio_07
#> 1.000000 0.750235
#> ----------------------------------------------------------------
#>
#> Variable bio_03 is strongly correlated with:
#>
#> bio_03
#> 1
#> ----------------------------------------------------------------
#>
#> Variable bio_04 is strongly correlated with:
#>
#> bio_04
#> 1
#> ----------------------------------------------------------------
#>
#> Variable bio_05 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 0.9720096 1.0000000 0.8287244 0.9468420 0.9218126 0.9528169 0.9538006
#> ----------------------------------------------------------------
#>
#> Variable bio_06 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 0.8622147 0.8287244 1.0000000 0.8314238 0.8774418 0.8339515 0.8896244
#> ----------------------------------------------------------------
#>
#> Variable bio_07 is strongly correlated with:
#>
#> bio_02 bio_07
#> 0.750235 1.000000
#> ----------------------------------------------------------------
#>
#> Variable bio_08 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 0.9762799 0.9468420 0.8314238 1.0000000 0.9187488 0.9767573 0.9329921
#> ----------------------------------------------------------------
#>
#> Variable bio_09 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 0.9389854 0.9218126 0.8774418 0.9187488 1.0000000 0.9263277 0.9321939
#> ----------------------------------------------------------------
#>
#> Variable bio_10 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 0.9884480 0.9528169 0.8339515 0.9767573 0.9263277 1.0000000 0.9473264
#> ----------------------------------------------------------------
#>
#> Variable bio_11 is strongly correlated with:
#>
#> bio_01 bio_05 bio_06 bio_08 bio_09 bio_10 bio_11
#> 0.9708369 0.9538006 0.8896244 0.9329921 0.9321939 0.9473264 1.0000000
#> ----------------------------------------------------------------
#>
#> Variable bio_12 is strongly correlated with:
#>
#> bio_12 bio_13 bio_16 bio_18 bio_19
#> 1.0000000 0.8849691 0.9095685 0.8344122 0.7131398
#> ----------------------------------------------------------------
#>
#> Variable bio_13 is strongly correlated with:
#>
#> bio_12 bio_13 bio_16 bio_18
#> 0.8849691 1.0000000 0.9462376 0.7578118
#> ----------------------------------------------------------------
#>
#> Variable bio_14 is strongly correlated with:
#>
#> bio_14 bio_17
#> 1.0000000 0.7848596
#> ----------------------------------------------------------------
#>
#> Variable bio_15 is strongly correlated with:
#>
#> bio_15
#> 1
#> ----------------------------------------------------------------
#>
#> Variable bio_16 is strongly correlated with:
#>
#> bio_12 bio_13 bio_16 bio_18
#> 0.9095685 0.9462376 1.0000000 0.7283047
#> ----------------------------------------------------------------
#>
#> Variable bio_17 is strongly correlated with:
#>
#> bio_14 bio_17 bio_19
#> 0.7848596 1.0000000 0.8555739
#> ----------------------------------------------------------------
#>
#> Variable bio_18 is strongly correlated with:
#>
#> bio_12 bio_13 bio_16 bio_18
#> 0.8344122 0.7578118 0.7283047 1.0000000
#> ----------------------------------------------------------------
#>
#> Variable bio_19 is strongly correlated with:
#>
#> bio_12 bio_17 bio_19
#> 0.7131398 0.8555739 1.0000000
#> ----------------------------------------------------------------
#>
# }