kernelPhil (version 0.1)

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

Description

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

Usage

kernel.smooth.in.space(
  dataset,
  dependent.variable = "dependent.variable",
  x = "x",
  y = "y",
  weight = "weight",
  normalise.by,
  data.type = "factor",
  alpha = 0.05,
  margin = 0.1,
  kernel.function = gaussian.kernel,
  adaptive.spatial.bw = TRUE,
  measure.points,
  projection = NA,
  round.up.low.variance = TRUE,
  explicit = TRUE
)

Arguments

dataset

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").

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: 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

A 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).

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.

projection

The spatial projection as a proj4 string - if given, data will be projected before smoothing and results will be deprojected before returning.

round.up.low.variance

Set to TRUE if there are periods of time with extremely low variance (defaults to TRUE).

explicit

If TRUE, progress will be reported with a progress bar (defaults to TRUE).

Value

A data.frame with the smoothed estimates.

Examples

# NOT RUN {
n=400;
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(dataset = synthesised.data);
ggplot2::ggplot(result,ggplot2::aes(x=x,y=y,colour=relative_density_TRUE))+
    ggplot2::geom_point();
# }