sse.sample | R Documentation |
Calculate Sample Specific Errors
sse.sample( modern_taxa, modern_climate, fossil_taxa, trainfun, predictfun, nboot, nPLS, nsig, usefx = FALSE, fx_method = "bin", bin = NA, cpus = 4, seed = NULL, test_mode = FALSE, test_it = 5 )
modern_taxa |
The modern taxa abundance data, each row represents a sampling site, each column represents a taxon. |
modern_climate |
The modern climate value at each sampling site |
fossil_taxa |
Fossil taxa abundance data to reconstruct past climates, each row represents a site to be reconstructed, each column represents a taxon. |
trainfun |
Training function you want to use, either
|
predictfun |
Predict function you want to use: if |
nboot |
The number of bootstrap cycles you want to use. |
nPLS |
The number of components to be extracted. |
nsig |
The significant number of components to use to reconstruct past climates, this can be obtained from the cross-validation results. |
usefx |
Boolean flag on whether or not use |
fx_method |
Binned or p-spline smoothed |
bin |
Binwidth to get fx, needed for both binned and p-splined method.
if |
cpus |
Number of CPUs for simultaneous iterations to execute, check
|
seed |
Seed for reproducibility. |
test_mode |
Boolean flag to execute the function with a limited number
of iterations, |
test_it |
Number of iterations to use in the test mode. |
The bootstrapped standard error for each site.
fx
, TWAPLS.w
,
TWAPLS.predict.w
, WAPLS.w
, and
WAPLS.predict.w
## Not run: # Load modern pollen data modern_pollen <- read.csv("/path/to/modern_pollen.csv") # Extract taxa taxaColMin <- which(colnames(modern_pollen) == "taxa0") taxaColMax <- which(colnames(modern_pollen) == "taxaN") taxa <- modern_pollen[, taxaColMin:taxaColMax] # Load reconstruction data Holocene <- read.csv("/path/to/Holocene.csv") taxaColMin <- which(colnames(Holocene) == "taxa0") taxaColMax <- which(colnames(Holocene) == "taxaN") core <- Holocene[, taxaColMin:taxaColMax] ## SSE nboot <- 5 # Recommended 1000 nsig <- 3 # This should be got from the random t-test of the cross validation sse_tf_Tmin2 <- fxTWAPLS::sse.sample( modern_taxa = taxa, modern_climate = modern_pollen$Tmin, fossil_taxa = core, trainfun = fxTWAPLS::TWAPLS.w2, predictfun = fxTWAPLS::TWAPLS.predict.w, nboot = nboot, nPLS = 5, nsig = nsig, usefx = TRUE, fx_method = "bin", bin = 0.02, cpus = 2, seed = 1 ) # Run with progress bar `%>%` <- magrittr::`%>%` sse_tf_Tmin2 <- fxTWAPLS::sse.sample( modern_taxa = taxa, modern_climate = modern_pollen$Tmin, fossil_taxa = core, trainfun = fxTWAPLS::TWAPLS.w2, predictfun = fxTWAPLS::TWAPLS.predict.w, nboot = nboot, nPLS = 5, nsig = nsig, usefx = TRUE, fx_method = "bin", bin = 0.02, cpus = 2, seed = 1 ) %>% fxTWAPLS::pb() ## End(Not run)
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