Uses flexible parametric additive models (see `areg`

and its
use of regression splines) to
determine how well each variable can be predicted from the remaining
variables. Variables are dropped in a stepwise fashion, removing the
most predictable variable at each step. The remaining variables are used
to predict. The process continues until no variable still in the list
of predictors can be predicted with an \(R^2\) or adjusted \(R^2\)
of at least `r2`

or until dropping the variable with the highest
\(R^2\) (adjusted or ordinary) would cause a variable that was dropped
earlier to no longer be predicted at least at the `r2`

level from
the now smaller list of predictors.

```
redun(formula, data=NULL, subset=NULL, r2 = 0.9,
type = c("ordinary", "adjusted"), nk = 3, tlinear = TRUE,
allcat=FALSE, minfreq=0, iterms=FALSE, pc=FALSE, pr = FALSE, ...)
# S3 method for redun
print(x, digits=3, long=TRUE, ...)
```

formula

a formula. Enclose a variable in `I()`

to force
linearity.

data

a data frame

subset

usual subsetting expression

r2

ordinary or adjusted \(R^2\) cutoff for redundancy

type

specify `"adjusted"`

to use adjusted \(R^2\)

nk

number of knots to use for continuous variables. Use
`nk=0`

to force linearity for all variables.

tlinear

set to `FALSE`

to allow a variable to be automatically
nonlinearly transformed (see `areg`

) while being predicted. By
default, only continuous variables on the right hand side (i.e., while
they are being predictors) are automatically transformed, using
regression splines. Estimating transformations for target (dependent)
variables causes more overfitting than doing so for predictors.

allcat

set to `TRUE`

to ensure that all categories of
categorical variables having more than two categories are redundant
(see details below)

minfreq

For a binary or categorical variable, there must be at
least two categories with at least `minfreq`

observations or
the variable will be dropped and not checked for redundancy against
other variables. `minfreq`

also specifies the minimum
frequency of a category or its complement
before that category is considered when `allcat=TRUE`

.

iterms

set to `TRUE`

to consider derived terms (dummy
variables and nonlinear spline components) as separate variables.
This will perform a redundancy analysis on pieces of the variables.

pc

if `iterms=TRUE`

you can set `pc`

to `TRUE`

to replace the submatrix of terms corresponding to each variable
with the orthogonal principal components before doing the redundancy
analysis. The components are based on the correlation matrix.

pr

set to `TRUE`

to monitor progress of the stepwise algorithm

…

arguments to pass to `dataframeReduce`

to remove
"difficult" variables from `data`

if `formula`

is
`~.`

to use all variables in `data`

(`data`

must be
specified when these arguments are used). Ignored for `print`

.

x

an object created by `redun`

digits

number of digits to which to round \(R^2\) values when printing

long

set to `FALSE`

to prevent the `print`

method
from printing the \(R^2\) history and the original \(R^2\) with
which each variable can be predicted from ALL other variables.

an object of class `"redun"`

A categorical variable is deemed
redundant if a linear combination of dummy variables representing it can
be predicted from a linear combination of other variables. For example,
if there were 4 cities in the data and each city's rainfall was also
present as a variable, with virtually the same rainfall reported for all
observations for a city, city would be redundant given rainfall (or
vice-versa; the one declared redundant would be the first one in the
formula). If two cities had the same rainfall, `city`

might be
declared redundant even though tied cities might be deemed non-redundant
in another setting. To ensure that all categories may be predicted well
from other variables, use the `allcat`

option. To ignore
categories that are too infrequent or too frequent, set `minfreq`

to a nonzero integer. When the number of observations in the category
is below this number or the number of observations not in the category
is below this number, no attempt is made to predict observations being
in that category individually for the purpose of redundancy detection.

`areg`

, `dataframeReduce`

,
`transcan`

, `varclus`

,
`subselect::genetic`

# NOT RUN { set.seed(1) n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <- 1*(x5=='a' | x5=='c') redun(~x1+x2+x3+x4+x5+x6, r2=.8) redun(~x1+x2+x3+x4+x5+x6, r2=.8, minfreq=40) redun(~x1+x2+x3+x4+x5+x6, r2=.8, allcat=TRUE) # x5 is no longer redundant but x6 is # }