EMMAgeo (version 0.9.4)

interpolate.classes: Function to interpolate classes.

Description

This function interpolates grain-size classes, either to higher or to lower resolution.

Usage

interpolate.classes(X, boundaries.in, boundaries.out, method = "natural",
  fixed.start = TRUE)

Arguments

X

Numeric matrix with m samples (rows) and n variables (columns).

boundaries.in

Numeric vector with class boundaries of the input data.

boundaries.out

Numeric vector with 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.start

Logical scalar, 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 with interpolated class values.

References

Dietze E, Hartmann K, Diekmann B, IJmker J, Lehmkuhl F, Opitz S, Stauch G, Wuennemann B, Borchers A. 2012. An end-member algorithm for deciphering modern detrital processes from lake sediments of Lake Donggi Cona, NE Tibetan Plateau, China. Sedimentary Geology 243-244: 169-180.

See Also

EMMA, approx, spline

Examples

# NOT RUN {
## load example data
data(X, envir = environment())
classes.in <- seq(from = 1, to = 10, length.out = ncol(X))

## Example 1 - decrease the class numbers
## define number of output classes
classes.out <- seq(1, 10, length.out = 20)

## interpolate the data set
Y <- interpolate.classes(X = X,
                         boundaries.in = classes.in,
                         boundaries.out = classes.out,
                         method = "linear")

## show original vs. interpolation for first 10 samples
plot(NA, xlim = c(1, 10), ylim = c(0, 11))
for(i in 1:10) {
  lines(classes.in, X[i,] * 20 + i)
  lines(classes.out, Y[i,] * 20 + i, col = 2)
}

## Example 2 - increase the class numbers
## define number of output classes
classes.out <- seq(1, 10, length.out = 200)

## interpolate the data set
Y <- interpolate.classes(X = X,
                         boundaries.in = classes.in,
                         boundaries.out = classes.out)

## show original vs. interpolation for first 10 samples
plot(NA, xlim = c(1, 10), ylim = c(0, 11))
for(i in 1:10) {
  lines(classes.in, X[i,] * 20 + i)
  lines(classes.out, Y[i,] * 20 + i, col = 2)
}
# }