ScatterDensity (version 0.1.1)

fast_table_num: fast_table_num

Description

Faster table computation in R compared to table() by omitting as.character and as.factor

Usage

fast_table_num(x, y, edges_x, edges_y, redefine = TRUE,
byrow = FALSE, all.inside = FALSE, rightmost.closed = FALSE,
sort = FALSE, na.rm = FALSE, names = FALSE,extendOutput=FALSE)

Value

extendOutput==FALSE: [1:\(k_2\),1:\(k_1\)] numerical matrix of counts

If extendOutput==TRUE, then list of named elements

count matrix: [1:\(k_2\),1:\(k_1\)] numerical matrix of counts

x_idx: [1:\(k_1\)] numerical vector of counts for x based on edges_x

y_idx: [1:\(k_2\)] numerical vector of counts for y based on edges_y

Arguments

x

[1:n] numerical vector

y

[1:n] numerical vector

edges_x

Optional, [1:(\(k_1\)+1)] numerical vector defining the specific borders in x default unique(x) for categorical scale

edges_y

Optional, [1:(\(k_2\)+1)] numerical vector defining the specific borders in y, default unique(y) for categorical scale

redefine

Optional, boolean TRUE: resets counts in y direction in order from 1:\(k_2\) to \(k_2\):1

byrow

Optional, boolean, If FALSE (the default) the count matrix is filled by columns, otherwise the matrix is filled by rows.

all.inside

Optional, boolean, if TRUE, the returned indices are coerced into 1,...,N-1, i.e., 0 is mapped to 1 and N to N-1

rightmost.closed

Optional, boolean, if TRUE, the rightmost interval, vec[N-1] .. vec[N] is treated as closed

sort

Optional, boolean, if TRUE, edges_x, edges_y are sorted non-decreasingly, NA/NaN are gthen ignored

na.rm

Optional, boolean, if TRUE, only complete observations are taken into account

names

Optional, boolean, if TRUE, output matrix is named by edges_x[1:\(k_1\)] and edges_y[1:\(k_2\)] (left-sided)

extendOutput

Optional, boolean, default FALSE, if TRUE, list is the output, otherwise numerical matrix, see below.

Author

Michael Thrun

Details

edges_x and edges_y must be sorted non-decreasingly. Beware that kernels are centers of bins, edges_x, edges_y are borders of bins. If edges are given, edges_x, edges_y can contain Inf,-Inf borders. In that case, edges always define n-1 bins lying within the edges. data outside first edge or last edge are ignored. Edges have either to be sorted non-decreasingly or set sort=T.

If edges are not given, set sort=T. In this case, they define the unique number of points. Then the number of edges internally sets the number of bins.

Beware that in matrix notation, count matrix would be expected be ordered [1:\(k_1\),1:\(k_2\)] instead of [1:\(k_2\),1:\(k_1\)]. Here we use the ordering that intuitively is given in plot(x,y), i.e. x are columns and y are rows.

See Also

table, findInterval

Examples

if(requireNamespace("FCPS")){
data(Hepta,package ="FCPS")
Cls=Hepta$Cls
Cls1=Cls+1
#k unqiue points define k bins
fast_table_num(Cls,Cls1,

redefine = FALSE,names=TRUE)==as.matrix(table(Cls,Cls1))
}
#k unqiue points define k bins
tab=fast_table_num(rnorm(100),rnorm(100),redefine=FALSE,sort=TRUE)

#set k+1 edges to get k bins
x=rnorm(100)
y=rnorm(100)
binsxy=5
edgex=seq(from=min(x),to=max(x),length.out=binsxy+1)
edgesy=seq(from=min(y),to=max(y),length.out=binsxy+1)
fast_table_num(x,y,edgex,edgesy,

redefine=FALSE,names=TRUE,rightmost.closed =TRUE)

#definition of counts analog to plotting
x = c(rnorm(1000, mean=-5), rnorm(1000, mean=5))
y = rnorm(2000)
edgesx = seq(min(x), max(x), length.out=512+1)
edgesy = seq(min(y), max(y), length.out=256+1)
joint_table = fast_table_num(x, y, edgesx, edgesy)
# \donttest{
plot(x,y)
plot(colSums(joint_table),xlab="x marginal",

ylab="sum of counts",main="x-values are stored in columns")

plot(rowSums(joint_table),xlab="y marginal",

ylab="sum of counts",main="y-values are stored in rows")
# }