apply.RdReturns a vector or array or list of values obtained by applying a function to margins of a sparse matrix.
apply.spam(X, MARGIN=NULL, FUN, ...)the spam matrix to be used.
a vector giving the subscripts which the function will be
applied over. 1 indicates rows, 2 indicates columns,
NULL or c(1,2) indicates rows and columns.
the function to be applied.
optional arguments to FUN.
This is a handy wrapper to apply a function to the (nonzero)
elements of a sparse matrix.
For example, it is possible to apply a covariance matrix to a distance
matrix obtained by nearest.dist, see Examples.
A call to apply only coerces the sparse matrix to a regular one.
The basic principle is applying the function to @entries, or to
the extracted columns or rows ([,i,drop=F] or
[i,,drop=F]). It is important to note that an empty column
contains at least one zero value and may lead to non intuitive
results.
This function may evolve over the next few releases.
Similar as a call to apply with a regular matrix. The most
important cases are as follows. The
result is a vector (MARGIN is length 1 and FUN is
scalar) or a matrix (MARGIN is length 1 and FUN returns
fixed length vectors, or MARGIN is length 2 and FUN is
scalar) or a list (if FUN returns vectors of different lengths).
base:apply for more details on Value.
S <- as.spam(dist(1:5))
S <- apply.spam(S/2, NULL, exp)
# instead of
# S@entries <- exp( S@entries/2)
# Technical detail, a null matrix consists
# of one zero element.
apply.spam(S,c(1,2),pmax)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.000000 0.000000 0.000000 0.000000 0
#> [2,] 1.648721 0.000000 0.000000 0.000000 0
#> [3,] 2.718282 1.648721 0.000000 0.000000 0
#> [4,] 4.481689 2.718282 1.648721 0.000000 0
#> [5,] 7.389056 4.481689 2.718282 1.648721 0
#> Class 'spam' (32-bit)
apply.spam(S,1,range)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 1.648721 1.648721 1.648721 1.648721
#> [2,] 0 1.648721 2.718282 4.481689 7.389056
# A similar example as for the base apply.
# However, no dimnames else we would get warnings.
x <- as.spam(cbind(x1 = 3, x2 = c(0,0,0, 5:2)))
apply.spam(x, 2, mean, trim = .2)
#> [1] 3.0 3.5
col.sums <- apply.spam(x, 2, sum)
row.sums <- apply.spam(x, 1, sum)
rbind(cbind(x, row.sums), c(col.sums, sum(col.sums)))
#> [,1] [,2] [,3]
#> [1,] 3 0 3
#> [2,] 3 0 3
#> [3,] 3 0 3
#> [4,] 3 5 8
#> [5,] 3 4 7
#> [6,] 3 3 6
#> [7,] 3 2 5
#> [8,] 21 14 35
#> Class 'spam' (32-bit)
apply.spam(x, 2, is.vector)
#> [1] TRUE TRUE
# Sort the columns of a matrix
# Notice that the result is a list due to the different
# lengths induced by the nonzero elements
apply.spam(x, 2, sort)
#> [[1]]
#> [1] 3 3 3 3 3 3 3
#>
#> [[2]]
#> [1] 2 3 4 5
#>
# Function with extra args:
cave <- function(x, c1, c2) c(mean(x[c1]), mean(x[c2]))
apply(x,1, cave, c1=1, c2=c(1,2))
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 3.0 3.0 3.0 3 3.0 3 3.0
#> [2,] 1.5 1.5 1.5 4 3.5 3 2.5
ma <- spam(c(1:4, 0, 0,0, 6), nrow = 2)
ma
#> [,1] [,2] [,3] [,4]
#> [1,] 1 3 0 0
#> [2,] 2 4 0 6
#> Class 'spam' (32-bit)
apply.spam(ma, 1, table) #--> a list of length 2
#> [[1]]
#>
#> 1 3
#> 1 1
#>
#> [[2]]
#>
#> 2 4 6
#> 1 1 1
#>
apply.spam(ma, 1, stats::quantile)# 5 x n matrix with rownames
#> [,1] [,2]
#> 0% 1.0 2
#> 25% 1.5 3
#> 50% 2.0 4
#> 75% 2.5 5
#> 100% 3.0 6