IQR_outliers.RdOutlier detection with interquartile range (IQR) based distances, defined as:
lower limit \(Q1 - k * IQR\) and
upper limit \(Q3 + k * IQR\). Here Q1 and Q3 is the first and the
third quantile respectively.
Inner lower/upper limit for probable outlier detection has k = k_inn and
outer lower/upper limit for (extreme) outlier detection has k = k_out,
(k_out > k_inn).
IQR_outliers(x, k_out = 3, k_inn = 1.5)Matrix-like data or hyperSpec object.
Number of IQR distances from Q1 and Q3 for extreme
outlier identification. Default value is 3.
Number of IQR diustances from Q1 and Q3 for probable
outlier idientification. Default value is 1.5.
A list with these variables
outer / inner - matrices with values of outer / inner limits respectively
Logical matrices indicating TRUE for points that are:
is_out - (extreme) outliers (excluding probable outliers);
is_pout - probable outliers (excluding extreme outliers);
is_bothOut - either probable or extreme outliers;
Logical vectors indicating spectra / rows with at least one:
row_out - (extreme) outlier point;
row_pout - probable outlier point (excluding extreme outliers);
row_bothOut - either probable or extreme outlier point.
If x is a hyperSpec object, then the listed variables are returned as
hyperSpec objects.
Note that probable outliers are points between corresponding lower as
well as corresponding upper inner and outer limits.
Extreme outliers are points outside lower and upper outer limits.
Read about boxplots and outliers (Box Plot: Display of Distribution)
Other spHelper utilities:
expr2text(),
mad_outliers(),
rmExpr(),
sd_outliers(),
subt(),
unGroup(),
uncall()
Other spHelper functions for spectroscopy and hyperSpec:
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(),
sp_class_perform()
Other outlier detection functions in spHelper:
mad_outliers(),
sd_outliers()
outl <- IQR_outliers(Spectra2)
#> Warning: Function 'chk.hy' is deprecated.
#> Use function 'assert_hyperSpec' instead.
#> Error in .expand(e1, dim(e2)[c(1, 3)]): Dimension mismatch.
plot(outl$outer, col = NA)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'outl' not found
plot(Spectra2, add = TRUE, spc.nmax = nrow(Spectra2))
#> Error in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...): plot.new has not been called yet
plot(outl$outer, add = TRUE, col = "red", lines.args = list(lwd = 3))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'outl' not found
plot(outl$inner, add = TRUE, col = "orange", lines.args = list(lwd = 3))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'outl' not found
legend("topright",legend=c("Outer limit", "Inner limit", "Spectra"),
col = c("red", "orange", "black"), lwd = c(3,3,1), lty = 1)
#> Error in (function (s, units = "user", cex = NULL, font = NULL, vfont = NULL, ...) { if (!is.null(vfont)) vfont <- c(typeface = pmatch(vfont[1L], Hershey$typeface), fontindex = pmatch(vfont[2L], Hershey$fontindex)) .External.graphics(C_strWidth, as.graphicsAnnot(s), pmatch(units, c("user", "figure", "inches")), cex, font, vfont, ...)})(dots[[1L]][[1L]], cex = dots[[2L]][[1L]], font = dots[[3L]][[1L]], units = "user"): plot.new has not been called yet
title("IQR based distances")
#> Error in title("IQR based distances"): plot.new has not been called yet