Description Usage Arguments Value Examples
This function performs kernel smoothing on a dataset in space alone. It is the same as kernel.smooth.in.space(), except that the results include the error margins for the estimates at every point. Note that it is much slower than kernel.smooth.in.space().
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | kernel.smooth.in.space.with.margins(
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
)
|
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"). |
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 |
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). |
A data.frame with the smoothed estimates.
1 2 3 4 5 6 7 8 9 10 11 | 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.with.margins(dataset = synthesised.data);
ggplot2::ggplot(result,ggplot2::aes(x=x,y=y,colour=relative_density_TRUE))+
ggplot2::geom_point();
|
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