sp_class_perform: [.!] Compare spectra of groups at each wavelength

In GegznaV/spHelper: Extension of Package `hyperSpec` and Various Convenience Functions

[.!] Compare spectra of groups at each wavelength

Description

Compare spectra of each pair of indicated groups at each wavelength.

Usage

sp_class_perform(
  sp,
  by = stop("Parameter 'by' is missing."),
  measure = c("auc", "bac", "j", "sesp", "tpr")
)

sp_class_perform_cv(
  sp,
  by = stop("Parameter 'by' is not specified."),
  measure = c("sesp", "j", "tpr")[1],
  cvo = cvo_create_folds(sp, by, seeds),
  seeds = NULL,
  sp_test = NULL
)

print.sp_class_perform_cv(obj)

print.sp_classif_performance(obj)

## S3 method for class 'sp_classif_performance'
predict(object, newdata, what = c("values", "performance"), ...)

sp_classification_performance(
  sp,
  by = stop("Parameter 'by' is not specified."),
  measure = c("sesp", "j", "tpr")[1],
  cvo = cvo_create_folds(sp, by, seeds),
  seeds = NULL,
  ...
)

sp_compare_gr_wl(
  sp,
  by = stop("Parameter 'by' is missing."),
  measure = c("auc", "j", "sesp", "tpr"),
  ...
)

class_perform_sp(
  sp,
  by = stop("Parameter 'by' is missing."),
  measure = c("auc", "j", "sesp", "tpr"),
  ...
)

Arguments

sp	hyperSpec object.
by	A vector (factor variable) with indicated groups for each case: either variable name inside the object sp, or a vector of length nrow(sp).
measure	string with measure of classification performance. Currently available options: "auc" - AUC (default) area under the ROC curve; "j" - Youden's index; "bac" - Balanced accuracy (mean of Sensitivity (Se) and Specificity (Sp)). "sesp" - [DEPRECATED!] The same as "bac"; "tpr" - [DEPRECATED!] The same as "bac"; "kappa" - [SLOW, DO NOT USE IT] Cohen's kappa.
cvo	a cross-validation object (cvo), created with function cvo_create_folds,createFolds or similar.

Value

Fields of sp_classif_performance object:

type type of data used ("Training data");
performance - hyperSepc object with performance estimates;
cutoffs - hyperSepc object with estimates of critical values (cut-off points);
means - hyperSepc object with means of each compared group;
means.description - type of those means ("10% trimmed mean (of each group)");
compared_by_var - variable name, that was used for grouping;
measure- measure of performance.

Fields of sp_class_perform_cv object:

data - a hyperSpec object with data used in calculations;
cvo - cross-validation object used for analysis;
train_performance - performance estimates of training datasets for each repetition and fold;
test_performance - performance estimates of testing datasets for each repetition and fold;
cutoffs - estimates of cut-off values for each repetition and fold;
obj - a list of sp_classif_performance objects for each repetition and fold;

Author(s)

Vilmantas Gegzna

See Also

Other spHelper functions for spectroscopy and hyperSpec: IQR_outliers(), binning(), file, gapDer(), hy2mat(), hyAdd_Label_wl(), hyAdd_Labels_PAP_PD_2014(), hyAdd_Labels_TD2009(), hyAdd(), hyDrop_NA(), hyGet_palette(), hyRm_palette(), mad_outliers(), mean_Nsd(), median_Nmad(), plot_hyPalette(), prepare_PAP_RK_2014__MATLAB_failui(), read.OOIBase32(), read.OceanView.header(), read.OceanView(), read.sp.csv2(), read3csv2hy(), replace_spc(), sd_outliers(), spStat()

Examples

library(spHelper)
library(spPlot)
library(ROCR)

sp <- sp_filter(Spectra2)

# Default measure of performace is AUC:
sp_compared <- sp_class_perform(sp, "class")

sp_compared <- sp_class_perform(sp, "class", measure = "bac")

names(sp_compared)
  ##  [1] "type"   "performance"  "cutoffs"  "means"
  ##  [5] "means.description" "compared_by_var"   "measure"

sp_compared$performance

  ##  hyperSpec object
  ##     6 spectra
  ##     2 data columns
  ##     501 data points / spectrum
  ##  wavelength: lambda/nm [integer] 300 301 ... 800
  ##  data:  (6 rows x 2 columns)
  ##     1. spc: Mean of Se and Sp [matrix501] 0.6266667 0.7310526 ... 0.5763674
  ##     2. Compared: Compared groups [character] K vs. l K vs. N ... N vs. S1

sp_compared$cutoffs

  ##  hyperSpec object
  ##     6 spectra
  ##     2 data columns
  ##     501 data points / spectrum
  ##  wavelength: lambda/nm [integer] 300 301 ... 800
  ##  data:  (6 rows x 2 columns)
  ##     1. spc: Cut-offs [matrix501] 162.3499 151.5054 ... 19.60151
  ##     2. Compared: Compared groups [character] K vs. l K vs. N ... N vs. S1


theme_set(theme_bw())
ggplot(sp_compared$performance, aes(color = Compared)) + geom_line()

ggplot(sp_compared$cutoffs, aes(color = Compared)) + geom_line()

qplot_sp(sp_compared$performance, by = "Compared") + set_ggLims(c(.45,1),"y")


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sp2 <- Spectra2[,,500~510]
sp_compared_cv <- sp_class_perform_cv(Sp2, "class")

names(sp_compared_cv)
  ## [1] "data"              "cvo"      "train_performance"
  ## [4] "test_performance"  "cutoffs"  "obj"

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Not run: \donttest{
rez <- sp_class_perform_cv(sp = Spectra2, by = "gr")

rez
}
## End(Not run)

GegznaV/spHelper documentation built on April 16, 2023, 1:42 p.m.