R: Kernel smooth data in time alone

kernel.smooth.in.time

R Documentation

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
)

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.

Value

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

Examples

n=1000;
synthesised.data<-data.frame(x=stats::runif(n),y=stats::runif(n),
    year=stats::runif(n,0,sqrt(2)));
synthesised.data$dependent.variable<-unlist(lapply(1:nrow(synthesised.data),
    function(X){
    stats::dist(as.matrix(synthesised.data[c(1,X),1:2]),method =
    "euclidean")<synthesised.data$year[X];
}))
result<-kernelPhil::kernel.smooth.in.time(dataset = synthesised.data,
    bandwidth = 0.05,sample.density.threshold = 100);
result$plot;