# rms.trans

##### rms Special Transformation Functions

This is a series of functions (`asis`

, `pol`

, `lsp`

,
`rcs`

, `catg`

, `scored`

, `strat`

, `matrx`

, and
`%ia%`

) that set up special attributes (such as
knots and nonlinear term indicators) that are carried through to fits
(using for example `lrm`

,`cph`

, `ols`

,
`psm`

). `anova.rms`

, `summary.rms`

, `Predict`

,
`survplot`

, `fastbw`

, `validate`

, `specs`

,
`which.influence`

, `nomogram`

and `latex.rms`

use these
attributes to automate certain analyses (e.g., automatic tests of linearity
for each predictor are done by `anova.rms`

). Many of the functions
are called implicitly. Some S functions such as `ns`

derive data-dependent
transformations that are not always "remembered" when predicted values are
later computed, so the predictions may be incorrect. The functions listed
here solve that problem when used in the `rms`

context.

`asis`

is the identity transformation, `pol`

is an ordinary
(non-orthogonal) polynomial, `rcs`

is a linear tail-restricted
cubic spline function (natural spline, for which the
`rcspline.eval`

function generates the design matrix, the
presence of system option `rcspc`

causes `rcspline.eval`

to be
invoked with `pc=TRUE`

, and the presence of system option `fractied`

causes this value to be passed to `rcspline.eval`

as the `fractied`

argument), `catg`

is for a categorical variable,
`scored`

is for an ordered categorical variable, `strat`

is
for a stratification factor in a Cox model, `matrx`

is for a matrix
predictor, and `%ia%`

represents restricted interactions in which
products involving nonlinear effects on both variables are not included
in the model. `asis, catg, scored, matrx`

are seldom invoked
explicitly by the user (only to specify `label`

or `name`

,
usually).

In the list below, functions `asis`

through `strat`

can have
arguments `x, parms, label, name`

except that `parms`

does not
apply to `asis, matrx, strat`

.

##### Usage

```
asis(…)
matrx(…)
pol(…)
lsp(…)
rcs(…)
catg(…)
scored(…)
strat(…)
x1 %ia% x2
```

##### Arguments

- …
The arguments … above contain the following.

`x`

a predictor variable (or a function of one). If you specify e.g.

`pol(pmin(age,10),3)`

, a cubic polynomial will be fitted in`pmin(age,10)`

(`pmin`

is the S vector element--by--element function). The predictor will be labeled`age`

in the output, and plots with have`age`

in its original units on the axes. If you use a function such as`pmin`

, the predictor is taken as the first argument, and other arguments must be defined in the frame in effect when predicted values, etc., are computed.`parms`

parameters of transformation (e.g. number or location of knots). For

`pol`

the argument is the order of the polynomial, e.g.`2`

for quadratic (the usual default). For`lsp`

it is a vector of knot locations (`lsp`

will not estimate knot locations). For`rcs`

it is the number of knots (if scalar), or vector of knot locations (if`>2`

elements). The default number is the`nknots`

system option if`parms`

is not given. If the number of knots is given, locations are computed for that number of knots. If system option`rcspc`

is`TRUE`

the`parms`

vector has an attribute defining the principal components transformation parameters. For`catg`

,`parms`

is the category labels (not needed if variable is an S category or factor variable). If omitted,`catg`

will use`unique(x)`

, or`levels(x)`

if`x`

is a`category`

or a`factor`

. For`scored`

,`parms`

is a vector of unique values of variable (uses`unique(x)`

by default). This is not needed if`x`

is an S`ordered`

variable. For`strat`

,`parms`

is the category labels (not needed if variable is an S category variable). If omitted, will use`unique(x)`

, or`levels(x)`

if`x`

is`category`

or`factor`

.`parms`

is not used for`matrix`

.`label`

label of predictor for plotting (default =

`"label"`

attribute or variable name)`name`

Name to use for predictor in model. Default is name of argument to function.

- x1,x2
two continuous variables for which to form a non-doubly-nonlinear interaction

##### See Also

`rcspline.eval`

, `rcspline.restate`

, `rms`

, `cph`

, `lrm`

, `ols`

, `datadist`

##### Examples

```
# NOT RUN {
options(knots=4, poly.degree=2)
# To get the old behavior of rcspline.eval knot placement (which didnt' handle
# clumping at the lowest or highest value of the predictor very well):
# options(fractied = 1.0) # see rcspline.eval for details
country <- factor(country.codes)
blood.pressure <- cbind(sbp=systolic.bp, dbp=diastolic.bp)
fit <- lrm(Y ~ sqrt(x1)*rcs(x2) + rcs(x3,c(5,10,15)) +
lsp(x4,c(10,20)) + country + blood.pressure + poly(age,2))
# sqrt(x1) is an implicit asis variable, but limits of x1, not sqrt(x1)
# are used for later plotting and effect estimation
# x2 fitted with restricted cubic spline with 4 default knots
# x3 fitted with r.c.s. with 3 specified knots
# x4 fitted with linear spline with 2 specified knots
# country is an implied catg variable
# blood.pressure is an implied matrx variable
# since poly is not an rms function (pol is), it creates a
# matrx type variable with no automatic linearity testing
# or plotting
f1 <- lrm(y ~ rcs(x1) + rcs(x2) + rcs(x1) %ia% rcs(x2))
# %ia% restricts interactions. Here it removes terms nonlinear in
# both x1 and x2
f2 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 %ia% rcs(x2))
# interaction linear in x1
f3 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 %ia% x2)
# simple product interaction (doubly linear)
# Use x1 %ia% x2 instead of x1:x2 because x1 %ia% x2 triggers
# anova to pool x1*x2 term into x1 terms to test total effect
# of x1
# }
```

*Documentation reproduced from package rms, version 5.1-4, License: GPL (>= 2)*