interpolate_classes: Interpolate data between different classes.

In coffeemuggler/grainsize: Grain-size data analysis functions

Description

Interpolate grain-size data for different classes, either to higher or to lower resolution.

Usage

interpolate_classes(data, boundaries_in, boundaries_out, method = "natural",
  fixed = TRUE)

Arguments

data	Numeric matrix, input data set with m samples (rows) and n variables (columns).
boundaries_in	Numeric vector, class boundaries of the input data.
boundaries_out	Numeric vector, class boundaries of the output data.
method	Logical scalar, interpolation method, one out of "linear" (linear interpolation), "fmm" (cubic spline), "natural" (natural spline), "periodic" (periodic spline). Default is "natural".
fixed	Logical value, specifying if the outer boundaries should be set to the same values as in the original matrix, default is TRUE. This may become necessary to avoid interpolation errors, see example.

Value

Numeric matrix, interpolated class values.

Author(s)

Michael Dietze

Examples

## load example data
data(LA950, envir = environment())

## convert units to phi-scale
classes_in <- convert_units(mu = LA950$x)

## Example 1 - decrease the class numbers
## define number of output classes
classes_out <- seq(from = max(classes_in), 
                   to = min(classes_in), 
                   length.out = 20)
                   
## interpolate the data set
Y <- interpolate_classes(data = LA950$y, 
                         boundaries_in = classes_in, 
                         boundaries_out = classes_out,
                         method = "linear")
                         
## show original vs. interpolation for first 10 samples
plot(NA, xlim = range(classes_in), ylim = range(LA950$y))
lines(classes_in, LA950$y)
lines(classes_out, Y, col = 2)

## Example 2 - increase the class numbers
## define number of output classes
classes_out <- seq(from = max(classes_in), 
                   to = min(classes_in), 
                   length.out = 200)

## interpolate the data set
Y <- interpolate_classes(data = LA950$y, 
                         boundaries_in = classes_in, 
                         boundaries_out = classes_out,
                         method = "linear")

## show original vs. interpolation for first 10 samples
plot(NA, xlim = range(classes_in), ylim = range(LA950$y))
lines(classes_in, LA950$y)
lines(classes_out, Y, col = 2)

coffeemuggler/grainsize documentation built on May 24, 2019, 3:06 a.m.