`Function`

is a class of functions for creating other S functions.
`Function.rms`

is the method for creating S functions to compute
X beta, based on a model fitted with `rms`

in effect.
Like `latexrms`

, `Function.rms`

simplifies restricted cubic
spline functions and factors out terms in second-order interactions.
`Function.rms`

will not work for models that have third-order
interactions involving restricted cubic splines.
`Function.cph`

is a particular method for handling fits from
`cph`

, for which an intercept (the negative of the centering
constant) is added to
the model. `sascode`

is a function that takes an S function such
as one created by `Function`

and does most of the editing
to turn the function definition into
a fragment of SAS code for computing X beta from the fitted model, along
with assignment statements that initialize predictors to reference
values.
`perlcode`

similarly creates Perl code to evaluate a fitted
regression model.

```
# S3 method for rms
Function(object, intercept=NULL,
digits=max(8, .Options$digits), posterior.summary=c('mean', 'median', 'mode'), …)
# S3 method for cph
Function(object, intercept=-object$center, …)
```# Use result as fun(predictor1=value1, predictor2=value2, \dots)

sascode(object, file='', append=FALSE)

perlcode(object)

object

a fit created with `rms`

in effect

intercept

an intercept value to use (not allowed to be specified to `Function.cph`

).
The intercept is usually retrieved from the regression coefficients
automatically.

digits

number of significant digits to use for coefficients and knot locations

posterior.summary

if using a Bayesian model fit such as from
`blrm`

, specifies whether to use posterior mode/mean/median parameter estimates in generating the function

file

name of a file in which to write the SAS code. Default is to write to standard output.

append

set to `TRUE`

to have `sascode`

append code to an existing file named
`file`

.

…

arguments to pass to `Function.rms`

from
`Function.cph`

`Function`

returns an S-Plus function that can be invoked in any
usual context. The function has one argument per predictor variable,
and the default values of the predictors are set to `adjust-to`

values
(see `datadist`

). Multiple predicted X beta values may be calculated
by specifying vectors as arguments to the created function.
All non-scalar argument values must have the same length.
`perlcode`

returns a character string with embedded newline characters.

# NOT RUN { suppressWarnings(RNGversion("3.5.0")) set.seed(1331) x1 <- exp(rnorm(100)) x2 <- factor(sample(c('a','b'),100,rep=TRUE)) dd <- datadist(x1, x2) options(datadist='dd') y <- log(x1)^2+log(x1)*(x2=='b')+rnorm(100)/4 f <- ols(y ~ pol(log(x1),2)*x2) f$coef g <- Function(f, digits=5) g sascode(g) cat(perlcode(g), '\n') g() g(x1=c(2,3), x2='b') #could omit x2 since b is default category predict(f, expand.grid(x1=c(2,3),x2='b')) g8 <- Function(f) # default is 8 sig. digits g8(x1=c(2,3), x2='b') options(datadist=NULL) # } # NOT RUN { # Make self-contained functions for computing survival probabilities # using a log-normal regression f <- psm(Surv(d.time, death) ~ rcs(age,4)*sex, dist='gaussian') g <- Function(f) surv <- Survival(f) # Compute 2 and 5-year survival estimates for 50 year old male surv(c(2,5), g(age=50, sex='male')) # }