api-parray.RdGeneral representation of multidimensional arrays (with named dimnames, also called named arrays.)
parray(varNames, levels, values = 1, normalize = "none", smooth = 0)
as.parray(values, normalize = "none", smooth = 0)
data2parray(data, varNames = NULL, normalize = "none", smooth = 0)
makeDimNames(varNames, levels, sep = "")Names of variables defining table; can be a right hand sided formula.
Either 1) a vector with number of levels of the factors in varNames or 2) a list with specification of the levels of the factors in varNames. See 'examples' below.
Values to go into the array
Either "none", "first" or "all". Should result be normalized, see 'Details' below.
Should values be smoothed, see 'Details' below.
Data to be coerced to a parray; can be data.frame,
table, xtabs, matrix.
Desired separator in dim names; defaults to "".
A a named array.
A named array object represents a table defined by a set of variables and their levels, together with the values of the table. E.g. f(a,b,c) can be a table with a,b,c representing levels of binary variable
If normalize="first" then for each configuration of all
other variables than the first, the probabilities are normalized to
sum to one. Thus f(a,b,c) becomes a conditional probability table
of the form p(a|b,c).
If normalize="all" then the sum over all entries of f(a,b,c)
is one.
If smooth is positive then smooth is added to
values before normalization takes place.
t1 <- parray(c("gender","answer"), list(c('male','female'),c('yes','no')), values=1:4)
t1 <- parray(~gender:answer, list(c('male','female'),c('yes','no')), values=1:4)
t1 <- parray(~gender:answer, c(2,2), values=1:4)
t2 <- parray(c("answer","category"), list(c('yes','no'),c(1,2)), values=1:4+10)
t3 <- parray(c("category","foo"), c(2,2), values=1:4+100)
varNames(t1)
#> [1] "gender" "answer"
nLevels(t1)
#> gender answer
#> 2 2
valueLabels(t1)
#> $gender
#> [1] "gender1" "gender2"
#>
#> $answer
#> [1] "answer1" "answer2"
#>
## Create 1-dimensional vector with dim and dimnames
x1 <- 1:5
as.parray(x1)
#> V1
#> V11 V12 V13 V14 V15
#> 1 2 3 4 5
x2 <- parray("x", levels=length(x1), values=x1)
dim(x2)
#> x
#> 5
dimnames(x2)
#> $x
#> [1] "x1" "x2" "x3" "x4" "x5"
#>
## Matrix
x1 <- matrix(1:6, nrow=2)
as.parray(x1)
#> V2
#> V1 V21 V22 V23
#> V11 1 3 5
#> V12 2 4 6
parray(~a:b, levels=dim(x1), values=x1)
#> b
#> a b1 b2 b3
#> a1 1 3 5
#> a2 2 4 6
#> attr(,"class")
#> [1] "parray" "array"
## Extract parrays from data
## 1) a dataframe
data(cad1)
data2parray(cad1, ~Sex:AngPec:AMI)
#> , , AMI = Definite
#>
#> AngPec
#> Sex Atypical None Typical
#> Female 2 4 4
#> Male 4 10 39
#>
#> , , AMI = NotCertain
#>
#> AngPec
#> Sex Atypical None Typical
#> Female 4 19 14
#> Male 20 52 64
#>
data2parray(cad1, c("Sex","AngPec","AMI"))
#> , , AMI = Definite
#>
#> AngPec
#> Sex Atypical None Typical
#> Female 2 4 4
#> Male 4 10 39
#>
#> , , AMI = NotCertain
#>
#> AngPec
#> Sex Atypical None Typical
#> Female 4 19 14
#> Male 20 52 64
#>
data2parray(cad1, c(1,2,3))
#> , , AMI = Definite
#>
#> AngPec
#> Sex Atypical None Typical
#> Female 2 4 4
#> Male 4 10 39
#>
#> , , AMI = NotCertain
#>
#> AngPec
#> Sex Atypical None Typical
#> Female 4 19 14
#> Male 20 52 64
#>
## 2) a table
data2parray(UCBAdmissions,c(1,2), normalize="first")
#> Gender
#> Admit Male Female
#> Admitted 0.4451877 0.3035422
#> Rejected 0.5548123 0.6964578