R: Kernel smooth data in space and time

kernel.smooth.in.space.and.time

R Documentation

Kernel smooth data in space and time

Description

This function performs kernel smoothing on a dataset in time and space. A static temporal kernel is applied first, and then an (optionally) adaptive spatial kernel on this weighted data.

Usage

kernel.smooth.in.space.and.time(
  dataset,
  dependent.variable = "dependent.variable",
  x = "x",
  y = "y",
  time = "year",
  weight = "weight",
  normalise.by,
  data.type = "factor",
  alpha = 0.05,
  margin = 0.1,
  kernel.function = gaussian.kernel,
  adaptive.spatial.bw = TRUE,
  temporal.bandwidth,
  measure.points,
  measure.times,
  projection = NA,
  explicit = TRUE
)

Arguments

`dataset`	The dataset to be smoothed as a data.frame.
`dependent.variable`	String name of the single column in dataset with the factor dependent variable (if data.type=="factor") or a vector of column names with numeric counts (if data.type=="count") (defaults to "dependent.variable").
`x`	String name of column containing numeric x co-ordinate (defaults to "x").
`y`	String name of column containing numeric y co-ordinate (defaults to "y").
`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").
`normalise.by`	String name of column by which data should be normalised (typically factor with document, speaker or writer ids).
`data.type`	The type of the dependent variable as a string: either "factor", if each row is a token, or "count", if each row is a document, speaker or writer with token counts in separate columns (defaults to "factor").
`alpha`	Numeric alpha for calculating error margins (defaults to 0.05).
`margin`	Numeric desired error margin for calculating spatial bandwidths (defaults to 0.1).
`kernel.function`	The kernel function, one of gaussian.kernel, gaussian.square.kernel, triangular.kernel, square.kernel, or a custom function (defaults to gaussian.kernel).
`adaptive.spatial.bw`	Boolean indicating whether the spatial bandwidth is adaptive (set to achieve margin at every point) or static (set to the average of bandwidths needed to achieve margin at every point).
`temporal.bandwidth`	Numeric bandwidth of the (gaussian) temporal kernel.
`measure.points`	A data.frame of spatial points at which estimates are to be made, with two columns with the same names as x,y in dataset; if not supplied, estimates are at the same locations as dataset.
`measure.times`	A numeric vector of specific times at which to make estimates; if not given, will default to seq(from=min(time),to=max(time),length.out=5).
`projection`	Spatial projection as a proj4 string - if given, data will be projected before smoothing and results will be deprojected before returning.
`explicit`	If TRUE, progress will be reported with a progress bar (defaults to TRUE).

Value

A list containing the parameters and a data.frame with the smoothed estimates.

Examples

n=200;
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.space.and.time(dataset =
    synthesised.data,temporal.bandwidth = 0.25,measure.times =
    seq(from=-0.05,to=1.15,length.out=4),alpha = 0.15,margin = 0.2);
gridExtra::grid.arrange(ggplot2::ggplot(result$results[[1]],
    ggplot2::aes(x=x,y=y,colour=relative_density_TRUE))+
    ggplot2::geom_point(),ggplot2::ggplot(result$results[[2]],
    ggplot2::aes(x=x,y=y,colour=relative_density_TRUE))+
    ggplot2::geom_point(),ggplot2::ggplot(result$results[[3]],
    ggplot2::aes(x=x,y=y,colour=relative_density_TRUE))+
    ggplot2::geom_point(),ggplot2::ggplot(result$results[[4]],
    ggplot2::aes(x=x,y=y,colour=relative_density_TRUE))+
    ggplot2::geom_point());