---
title: "Array operations with the gRbase package"
author: "Søren Højsgaard"
output: 
  html_document:
    toc: true
    number_sections: true
vignette: >
  %\VignetteIndexEntry{arrays: Array operations in gRbase}
  %\VignettePackage{gRbase}
  %\VignetteEngine{knitr::knitr}
  %\VignetteEncoding{UTF-8}
---




```{r include=FALSE}
library(knitr)
opts_chunk$set(
tidy=FALSE,
size="small"
)
```


```{r echo=FALSE}
#require( gRbase )
prettyVersion <- packageDescription("gRbase")$Version
prettyDate <- format(Sys.Date())
```



```{r echo=F}
library(gRbase)
options("width"=100, "digits"=4)
options(useFancyQuotes="UTF-8")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  chk = "markup"
)
chk = "markup"
options("digits"=3)
```


# Introduction

This note describes some operations on arrays in R. These operations
have been implemented to facilitate implementation of graphical models
and Bayesian networks in R.

## Arrays/tables in R 
(\#sec:arrays)



The documentation of R states the following about arrays:

\begin{quote}
  \em An array in R can have one, two or more dimensions. It is simply
  a vector which is stored with additional attributes giving the
  dimensions (attribute "dim") and optionally names for those
  dimensions (attribute "dimnames").  A two-dimensional array is the
  same thing as a matrix.  One-dimensional arrays often look like
  vectors, but may be handled differently by some functions.
\end{quote}


## Cross classified data - contingency tables
(\#sec:new)

Arrays appear for example in connection with cross classified data. The array
\code{hec} below is an excerpt of the \code{HairEyeColor} array in R:

```{r  } 
hec <- c(32, 53, 11, 50, 10, 25, 36, 66, 9, 34, 5, 29) 
dim(hec) <- c(2, 3, 2)
dimnames(hec) <- list(Hair = c("Black", "Brown"), 
                      Eye = c("Brown", "Blue", "Hazel"), 
                      Sex = c("Male", "Female"))
hec
```

Above, \code{hec} is an array because it has a \code{dim} attribute. Moreover,
\code{hec} also has a \code{dimnames} attribute naming the levels of each
dimension. Notice that each dimension is given a name.

Printing arrays can take up a lot of space.  Alternative views on an
array can be obtained with \code{ftable()} or by converting the array
to a dataframe with \code{as.data.frame.table()}. We shall do so in the following.

```{r }
##flat <- function(x) {ftable(x, row.vars=1)}
flat <- function(x, n=4) {as.data.frame.table(x) |> head(n)}
hec |> flat()
```

An array with named dimensions is in this package called a *named array*.
The functionality described below relies heavily on arrays having named dimensions.
A check for an object being a named array is provided by
\rr{is.named.array()}
```{r }
is.named.array(hec)
``` 


## Defining arrays


Another way is to use \rr{tabNew()} from \grbase. This function is flexible wrt the input; for example:
```{r }
dn <- list(Hair=c("Black", "Brown"), Eye=~Brown:Blue:Hazel, Sex=~Male:Female)
counts <- c(32, 53, 11, 50, 10, 25, 36, 66, 9, 34, 5, 29)
z3 <- tabNew(~Hair:Eye:Sex, levels=dn, value=counts) 
z4 <- tabNew(c("Hair", "Eye", "Sex"), levels=dn, values=counts)
``` 

Notice that the levels list (\code{dn} above) when used in \rr{tabNew()} 
is allowed to contain superfluous elements. Default
\code{dimnames} are generated with
```{r }
z5 <- tabNew(~Hair:Eye:Sex, levels=c(2, 3, 2), values = counts)
dimnames(z5) |> str()
``` 


Using \rr{tabNew}, arrays can be normalized to sum to one in two ways:
1) Normalization can be over the first variable for *each*
configuration of all other variables and 2) over all configurations. For
example:
```{r }
z6 <- tabNew(~Hair:Eye:Sex, levels=c(2, 3, 2), values=counts, normalize="first")
z6 |> flat()
``` 


# Operations on arrays
{#sec:operations-arrays}

In the following we shall denote the dimnames 
(or variables) of the array \code{hec} by $H$, $E$ and $S$ and we let $(h,e,s)$
denote a configuration of these variables. The contingency table above
shall be denoted by $T_{HES}$ and we shall refer to the
$(h,e,s)$-entry of $T_{HES}$ as $T_{HES}(h,e,s)$. 

## Normalizing an array
{#sec:numarlizing-an-array}

Normalize an array with  \rr{tabNormalize()}
Entries of an array can be normalized to sum to one in two ways:
1) Normalization can be over the first variable for *each*
configuration of all other variables and 2) over all configurations. For
example:
```{r }
tabNormalize(z5, "first") |> flat()
``` 


## Subsetting an array -- slicing
{#sec:subsetting-an-array}

We can subset arrays (this will also be called ``slicing'') in
different ways. Notice that the result is not necessarily an
array. Slicing can be done using standard R code or using \rr{tabSlice}.
The virtue of \rr{tabSlice} comes from the flexibility when
specifying the slice:


The following leads from the original $2\times 3 \times 2$
array to a $2 \times 2$
array by cutting away the \code{Sex=Male} and \code{Eye=Brown} slice of the array:

```{r results=chk}
tabSlice(hec, slice=list(Eye=c("Blue", "Hazel"), Sex="Female"))
## Notice: levels can be written as numerics
## tabSlice(hec, slice=list(Eye=2:3, Sex="Female"))
``` 

We may also regard the result above as a $2 \times 2 \times 1$ array:
```{r }
tabSlice(hec, slice=list(Eye=c("Blue", "Hazel"), Sex="Female"), drop=FALSE)
``` 

If slicing leads to a one dimensional array, the output will by
default not be an array but a vector (without a dim attribute). However, 
the result can be forced to be a 1-dimensional array:

```{r results=chk}
## A vector:
t1 <- tabSlice(hec, slice=list(Hair=1, Sex="Female")); t1
## A 1-dimensional array:
t2 <- tabSlice(hec, slice=list(Hair=1, Sex="Female"), as.array=TRUE); t2 
## A higher dimensional array (in which some dimensions only have one level)
t3 <- tabSlice(hec, slice=list(Hair=1, Sex="Female"), drop=FALSE); t3
``` 

The difference between the last two forms can be clarified:
```{r }
t2 |> flat()
t3 |> flat()
``` 



## Collapsing  and inflating arrays
{#sec:collapsing-arrays}

Collapsing: The $HE$--marginal array $T_{HE}$ of $T_{HES}$ is  the array with
values
\begin{displaymath}
  T_{HE}(h,e) = \sum_s T_{HES}(h,e,s)
\end{displaymath}
Inflating: The ``opposite'' operation is to extend an array. For example, we can
extend $T_{HE}$ to have a third dimension, e.g.\ \code{Sex}. That is
\begin{displaymath}
  \tilde T_{SHE}(s,h,e) = T_{HE}(h,e)
\end{displaymath}
so $\tilde T_{SHE}(s,h,e)$ is constant as a function of $s$. 

With \grbase\ we can collapse arrays with\footnote{FIXME: Should allow for abbreviations in
  formula and character vector specifications.}:
```{r }
he <- tabMarg(hec, c("Hair", "Eye"))
he
```

```{r results=chk}
## Alternatives
tabMarg(hec, ~Hair:Eye)
tabMarg(hec, c(1, 2))
hec %a_% ~Hair:Eye
```

Notice that collapsing is a projection in the sense that applying the
operation again does not change anything:
```{r }
he1 <- tabMarg(hec, c("Hair", "Eye"))
he2 <- tabMarg(he1, c("Hair", "Eye"))
tabEqual(he1, he2)
``` 

Expand an array by adding additional dimensions with \rr{tabExpand()}:
```{r }
extra.dim <- list(Sex=c("Male", "Female"))
tabExpand(he, extra.dim) 
``` 

```{r results=chk}
## Alternatives
he %a^% extra.dim
```

Notice that expanding and collapsing brings us back to where we started:
```{r }
(he %a^% extra.dim) %a_% c("Hair", "Eye")
``` 


## Permuting an array
{#sec:permuting-an-array}

A reorganization of the table can be made with \rr{tabPerm} (similar
to \code{aperm()}), but \rr{tabPerm} allows for a formula and for variable abbreviation:
```{r }
tabPerm(hec, ~Eye:Sex:Hair) |> flat()
``` 

Alternative forms (the first two also works for \code{aperm}):
```{r results=chk}
tabPerm(hec, c("Eye", "Sex", "Hair"))
tabPerm(hec, c(2, 3, 1)) 
tabPerm(hec, ~Ey:Se:Ha) 
tabPerm(hec, c("Ey", "Se", "Ha"))
``` 




## Equality
{#sec:equality}

Two arrays are defined to be identical 1) if they have the same dimnames
and 2) if, possibly after a permutation, all values are identical (up to
a small numerical difference):

```{r }
hec2 <- tabPerm(hec, 3:1)
tabEqual(hec, hec2)
``` 

```{r results=chk} 
## Alternative
hec %a==% hec2
```

## Aligning
{#sec:aligning}

We can align one array according to the ordering of another:
```{r results=chk} 
hec2 <- tabPerm(hec, 3:1)
tabAlign(hec2, hec)
```

```{r result=chk}
## Alternative:
tabAlign(hec2, dimnames(hec))
``` 



%## Operations on two or more arrays
%{#sec:oper-two-arrays}

## Multiplication, addition etc:  $+$, $-$, $*$, $/$
{#sec:mult-addt-etc}

The product of two arrays $T_{HE}$ and $T_{HS}$ is defined to be the array
$\tilde T_{HES}$ with entries
\begin{displaymath}
  \tilde T_{HES}(h,e,s)= T_{HE}(h,e) + T_{HS}(h,s)
\end{displaymath}

The sum, difference and quotient is defined similarly: This is done
with \rr{tabProd()}, \rr{tabAdd()}, \rr{tabDiff()} and \rr{tabDiv()}:

```{r }
hs <- tabMarg(hec, ~Hair:Eye)
tabMult(he, hs)
``` 

Available operations:
```{r results=chk}
tabAdd(he, hs) 
tabSubt(he, hs)
tabMult(he, hs)
tabDiv(he, hs) 
tabDiv0(he, hs) ## Convention 0/0 = 0
``` 

Shortcuts:
```{r results=chk} 
## Alternative
he %a+% hs
he %a-% hs
he %a*% hs
he %a/% hs
he %a/0% hs ## Convention 0/0 = 0
```

Multiplication and addition of (a list of) multiple arrays is
accomplished with \rr{tabProd()} and \rr{tabSum()} (much like
\rr{prod()} and \rr{sum()}):
```{r }
es <- tabMarg(hec, ~Eye:Sex)
tabSum(he, hs, es)  
## tabSum(list(he, hs, es))
``` 


%% Lists of arrays are processed with
%% ```{r results=chk} 
%% tabListAdd(list(he, hs, es))
%% tabListMult(list(he, hs, es))
%% @

## An array as a probability density
{#sec:an-array-as}

If an array consists of non--negative numbers then it may be regarded as an
(unnormalized) discrete multivariate density. With this view, the following
examples should be self explanatory:
```{r  } 
tabDist(hec, marg=~Hair:Eye)
tabDist(hec, cond=~Sex) 
tabDist(hec, marg=~Hair, cond=~Sex) 
```

## Miscellaneous
{#sec:miscellaneous-1}

Multiply values in a slice by some number and all other values by
another number:
```{r  } 
tabSliceMult(es, list(Sex="Female"), val=10, comp=0)
```



# Examples
{#sec:examples}


## A Bayesian network
{#sec:comp-with-arrays}

A classical example of a Bayesian network is the ``sprinkler
example'', see e.g.\
(https://en.wikipedia.org/wiki/Bayesian_network):
\begin{quote}
  \em
  Suppose that there are two events which could cause grass to be wet:
  either the sprinkler is on or it is raining. Also, suppose that the
  rain has a direct effect on the use of the sprinkler (namely that
  when it rains, the sprinkler is usually not turned on). Then the
  situation can be modeled with a Bayesian network.
\end{quote}

We specify conditional probabilities $p(r)$, $p(s|r)$ and $p(w|s,r)$
as follows
(notice that the vertical conditioning bar ($|$) is replaced by the
horizontal underscore:

```{r }
yn <- c("y","n")
lev <- list(rain=yn, sprinkler=yn, wet=yn)
r <- tabNew(~rain, levels=lev, values=c(.2, .8))
s_r <- tabNew(~sprinkler:rain, levels = lev, values = c(.01, .99, .4, .6))
w_sr <- tabNew( ~wet:sprinkler:rain, levels=lev, 
             values=c(.99, .01, .8, .2, .9, .1, 0, 1))
r 
s_r  |> flat()
w_sr |> flat()
``` 

The joint distribution $p(r,s,w)=p(r)p(s|r)p(w|s,r)$ can be obtained
with \rr{tabProd()}:
ways:
```{r }
joint <- tabProd(r, s_r, w_sr); joint |> flat()
```

What is the probability that it rains given that the grass is wet? We
find $p(r,w)=\sum_s p(r,s,w)$ and then $p(r|w)=p(r,w)/p(w)$. Can be done in various ways: with \rr{tabDist()}
```{r }
tabDist(joint, marg=~rain, cond=~wet)
```

```{r results='hide'}
## Alternative:
rw <- tabMarg(joint, ~rain + wet)
tabDiv(rw, tabMarg(rw, ~wet))
## or
rw %a/% (rw %a_% ~wet)
``` 

```{r }
## Alternative:
x <- tabSliceMult(rw, slice=list(wet="y")); x
tabDist(x, marg=~rain)
```

##  Iterative Proportional Scaling (IPS)
{#sec:ips}

We consider the $3$--way \code{lizard} data from \grbase:
```{r }
data(lizard, package="gRbase")
lizard |> flat()
```

Consider the two factor log--linear model for the \verb'lizard'
data. Under the model the expected counts have the form
\begin{displaymath}
  \log m(d,h,s)= a_1(d,h)+a_2(d,s)+a_3(h,s)
\end{displaymath}
If we let $n(d,h,s)$ denote the observed counts, the likelihood
equations are: Find $m(d,h,s)$ such that
\begin{displaymath}
  m(d,h)=n(d,h), \quad
  m(d,s)=n(d,s), \quad
  m(h,s)=n(h,s)
\end{displaymath}
where $m(d,h)=\sum_s m(d,h.s)$ etc. 
The updates are as follows: For the first term we have

\begin{displaymath}
  m(d,h,s) \leftarrow m(d,h,s) \frac{n(d,h)}{m(d,h)}
%  , \mbox{ where }
%  m(d,h) = \sum_s m(d,h,s)
\end{displaymath}
After iterating the updates will not change and we will have equality:
$  m(d,h,s) = m(d,h,s) \frac{n(d,h)}{m(d,h)}$ and summing over $s$
shows that the equation $m(d,h)=n(d,h)$ is satisfied. 

A rudimentary implementation of iterative proportional scaling for
log--linear models is straight forward:
```{r }
myips <- function(indata, glist){
    fit   <- indata
    fit[] <-  1
    ## List of sufficient marginal tables
    md    <- lapply(glist, function(g) tabMarg(indata, g))

    for (i in 1:4){
        for (j in seq_along(glist)){
            mf  <- tabMarg(fit, glist[[j]])
            # adj <- tabDiv( md[[ j ]], mf)
            # fit <- tabMult( fit, adj )
            ## or
            adj <- md[[ j ]] %a/% mf
            fit <- fit %a*% adj
        }
    }
    pearson <- sum((fit - indata)^2 / fit)
    list(pearson=pearson, fit=fit)
}

glist <- list(c("species", "diam"),c("species", "height"),c("diam", "height"))

fm1 <- myips(lizard, glist)
fm1$pearson
fm1$fit |> flat()

fm2 <- loglin(lizard, glist, fit=T)
fm2$pearson
fm2$fit |> flat()
```


# Some low level functions
{#sec:some-low-level}

For e.g.\ a $2\times 3 \times 2$ array, the entries are such that the first
variable varies fastest so the ordering of the cells are $(1,1,1)$,
$(2,1,1)$, $(1,2,1)$, $(2,2,1)$,$(1,3,1)$ and so on. To find the value
of such a cell, say,
$(j,k,l)$ in the array (which is really just a vector), the cell is
mapped into an entry of a vector. 

For example, cell $(2,3,1)$
(\verb|Hair=Brown|, \verb|Eye=Hazel|, \verb|Sex=Male|) must be mapped to
entry $4$ in
```{r }
hec
c(hec)
``` 

For illustration we do:
```{r }
cell2name <- function(cell, dimnames){
    unlist(lapply(1:length(cell), function(m) dimnames[[m]][cell[m]]))
}
cell2name(c(2,3,1), dimnames(hec))
``` 



\subsection{\code{cell2entry()}, \code{entry2cell()} and \code{next\_cell()} }


The map from a cell to the corresponding
entry is provided by \rr{cell2entry()}. The reverse operation, going
from an entry to a cell (which is much less needed) is provided by
\rr {entry2cell()}.

```{r }
cell2entry(c(2,3,1), dim=c(2, 3, 2))
entry2cell(6, dim=c(2, 3, 2))
``` 


Given a cell, say $i=(2,3,1)$ in a $2\times 3\times 2$ array we often want to find the next cell in
the table following the convention that the first factor varies
fastest, that is $(1,1,2)$. This is provided by
\rr{next\_cell()}.
```{r }
next_cell(c(2,3,1), dim=c(2, 3, 2))
```


\subsection{\code{next\_cell\_slice()} and \code{slice2entry()}}
%{#sec:x}

Given that we look at cells for which for which the index in dimension $2$ is at level $3$ (that is
\verb|Eye=Hazel|), i.e.\ cells of the form $(j,3,l)$. Given such a
cell, what is then the next cell that also satisfies this
constraint. This is provided by
\rr{next\_cell\_slice()}.\footnote{FIXME: sliceset should be called margin.}
```{r }
next_cell_slice(c(1,3,1), slice_marg=2, dim=c( 2, 3, 2 ))
next_cell_slice(c(2,3,1), slice_marg=2, dim=c( 2, 3, 2 ))
``` 


Given that in dimension $2$ we look at level $3$. We want to find
entries for the cells of the form $(j,3,l)$.\footnote{FIXME:slicecell and
  sliceset should be renamed}
```{r }
slice2entry(slice_cell=3, slice_marg=2, dim=c( 2, 3, 2 ))
```

To verify that we indeed get the right cells:

```{r }
r <- slice2entry(slice_cell=3, slice_marg=2, dim=c( 2, 3, 2 ))
lapply(lapply(r, entry2cell, c( 2, 3, 2 )),
       cell2name, dimnames(hec))
```

\subsection{\code{fact\_grid()} -- Factorial grid}
{#sec:factgrid}

Using the operations above we can obtain the combinations of the
factors as a matrix:

```{r }
head( fact_grid( c(2, 3, 2) ), 6 )
``` 

A similar dataframe can also be obtained with the standard R
function \code{expand.grid} (but \code{factGrid} is faster)
```{r }
head( expand.grid(list(1:2, 1:3, 1:2)), 6 )
``` 



\appendix

# More about slicing
{#sec:more-about-slicing}

Slicing using standard R code can be done as follows:

```{r }
hec[, 2:3, ]  |> flat()  ## A 2 x 2 x 2 array
hec[1, , 1]             ## A vector
hec[1, , 1, drop=FALSE] ## A 1 x 3 x 1 array
``` 

Programmatically we can do the above as
```{r results=chk}
do.call("[", c(list(hec), list(TRUE, 2:3, TRUE)))  |> flat()
do.call("[", c(list(hec), list(1, TRUE, 1))) 
do.call("[", c(list(hec), list(1, TRUE, 1), drop=FALSE)) 
``` 

\grbase\ provides two alterntives for each of these three cases above:
```{r results=chk}
tabSlicePrim(hec, slice=list(TRUE, 2:3, TRUE))  |> flat()
tabSlice(hec, slice=list(c(2, 3)), margin=2) |> flat()

tabSlicePrim(hec, slice=list(1, TRUE, 1))  
tabSlice(hec, slice=list(1, 1), margin=c(1, 3)) 

tabSlicePrim(hec, slice=list(1, TRUE, 1), drop=FALSE)  
tabSlice(hec, slice=list(1, 1), margin=c(1, 3), drop=FALSE) 
``` 

