fnenvir: Check Covariates and Parameters of a Function

In rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models

Check Covariates and Parameters of a Function

Description

fnenvir finds the covariates and parameters in a function and can modify it so that the covariates used in it are found in the data object specified by .envir.

If the data object has class, repeated, the key word times in a function will use the response times from the data object as a covariate.

Usage

fnenvir(.z, ...)
## Default S3 method:
fnenvir(.z, .envir=parent.frame(), .name=NULL, .expand=TRUE,
	.response=FALSE, ...)

Arguments

.z	A function.
.envir	The environment or data object of class, repeated, tccov, or tvcov, in which the function is to be interpreted.
.name	Character string giving the name of the data object specified by .envir. Ignored unless the latter is such an object and only necessary when fnenvir is called within other functions.
.expand	If TRUE, expand functions with only time-constant covariates to return one value per observation instead of one value per individual. Ignored unless .envir is an object of class, repeated.
.response	If TRUE, any response variable can be used in the function. If FALSE, checks are made that the response is not also used as a covariate.
...	Arguments passed to other functions.

Value

The (modified) function, of class formulafn, is returned with its attributes giving the (new) model function, the covariate names, and the parameter names.

Author(s)

J.K. Lindsey

See Also

FormulaMethods,covariates, finterp, model, parameters

Examples

fn <- function(p) a+b*x
fnenvir(fn)
fn <- function(p) a+p*x
fnenvir(fn)
x <- 1:4
fnenvir(fn)
fn <- function(p) p[1]+exp(p[2]*x)
fnenvir(fn)
#
y <- matrix(rnorm(20),ncol=5)
y[3,3] <- y[2,2] <- NA
resp <- restovec(y)
xx <- tcctomat(x)
z1 <- matrix(rnorm(20),ncol=5)
z2 <- matrix(rnorm(20),ncol=5)
z3 <- matrix(rnorm(20),ncol=5)
zz <- tvctomat(z1)
zz <- tvctomat(z2,old=zz)
reps <- rmna(resp, ccov=xx, tvcov=zz)
rm(y, x, z1, z2)
#
# repeated objects
func1 <- function(p) p[1]+p[2]*x+p[3]*z2
print(fn1 <- fnenvir(func1, .envir=reps))
fn1(2:4)
#
# time-constant covariates
func2 <- function(p) p[1]+p[2]*x
print(fn2 <- fnenvir(func2, .envir=reps))
fn2(2:3)
print(fn2a <- fnenvir(func2, .envir=xx))
fn2a(2:3)
#
# time-varying covariates
func3 <- function(p) p[1]+p[2]*z1+p[3]*z2
print(fn3 <- fnenvir(func3, .envir=reps))
fn3(2:4)
print(fn3a <- fnenvir(func3, .envir=zz))
fn3a(2:4)
# including times
func3b <- function(p) p[1]+p[2]*z1+p[3]*z2+p[4]*times
print(fn3b <- fnenvir(func3b, .envir=reps))
fn3b(2:5)
#
# with typing error and a variable not in the data object
func4 <- function(p) p[1]+p2[2]*z1+p[3]*z2+p[4]*z3
print(fn4 <- fnenvir(func4, .envir=reps))
#
# first-order one-compartment model
# data objects for formulae
dose <- c(2,5)
dd <- tcctomat(dose)
times <- matrix(rep(1:20,2), nrow=2, byrow=TRUE)
tt <- tvctomat(times)
# vector covariates for functions
dose <- c(rep(2,20),rep(5,20))
times <- rep(1:20,2)
# functions
mu <- function(p) {
	absorption <- exp(p[1])
	elimination <- exp(p[2])
	absorption*exp(-p[3])*dose/(absorption-elimination)*
		(exp(-elimination*times)-exp(-absorption*times))}
shape <- function(p) exp(p[1]-p[2])*times*dose*exp(-exp(p[1])*times)
# response
conc <- matrix(rgamma(40,shape(log(c(0.1,0.4))),
	scale=mu(log(c(1,0.3,0.2))))/shape(log(c(0.1,0.4))),ncol=20,byrow=TRUE)
conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))),
	ncol=20,byrow=TRUE)[,1:19])
conc <- restovec(ifelse(conc>0,conc,0.01))
reps <- rmna(conc, ccov=dd, tvcov=tt)
#
print(fn5 <- fnenvir(mu,.envir=reps))
fn5(c(0,-1.2,-1.6))

rmutil documentation built on Oct. 29, 2022, 1:08 a.m.