kernelPhil (version 0.2)

kernel.smooth.in.time: Kernel smooth data in time alone

Description

This function performs kernel smoothing on a dataset in time alone.

Usage

kernel.smooth.in.time(
  dataset,
  dependent.variable = "dependent.variable",
  time = "year",
  weight = "weight",
  bandwidth = 10,
  sample.density.threshold = 3,
  length.out = 1000,
  alpha = 0.05,
  xlabel = "year",
  ylabel,
  greyscale = "compatible",
  save.path = "",
  measure.times,
  kernel.function = gaussian.kernel
)

Value

A list containing a data.frame with the smoothed estimates, and a ggplot grob visualising them.

Arguments

dataset

The dataset to be smoothed as a data.frame.

dependent.variable

String name of the column in dataset with the dependent variable (defaults to "dependent.variable"); this column should be numeric or factor.

time

String name of the column in dataset with the time variable (defaults to "year").

weight

String name of column in the dataset with numeric weights (defaults to "weight").

bandwidth

Numeric bandwidth of the kernel function.

sample.density.threshold

Numeric local density of samples below which no estimates will be returned.

length.out

The number of measure points along the time axis (defaults to 1000).

alpha

Numeric alpha for calculating error margins (defaults to 0.05).

xlabel

String label for the x-axis in returned plot (defaults to "year").

ylabel

String label for the y-axis in returned plot.

greyscale

If TRUE, plot will be in greyscale; if "compatible", plot will use a colour spectrum which also goes light>dark; otherwise, will use a non-greyscale-compatible colour scale.

save.path

String path to save plot to (if not given, plot will not be saved).

measure.times

A numeric vector of specific times at which to make estimates; if given, sample.density.threshold and length.out will be ignored.

kernel.function

The kernel function, one of gaussian.kernel, gaussian.square.kernel, triangular.kernel, square.kernel, or a custom function.

Examples

n=1000;
synthesised.data<-data.frame(x=stats::runif(n),y=stats::runif(n),
    year=stats::runif(n,0,sqrt(2)));
synthesised.data$dependent.variable<-unlist(lapply(1:nrow(synthesised.data),
    function(X){
    stats::dist(as.matrix(synthesised.data[c(1,X),1:2]),method =
    "euclidean")