| fnenvir {rmutil} | R Documentation |
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.
fnenvir(.z, .envir=sys.frame(sys.parent()), .name=NULL, .expand=TRUE,
.response=FALSE)
.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. |
The (modified) function, of class formulafn, is returned with its
attributes giving the (new) model function, the covariate names, and
the parameter names.
J.K. Lindsey
FormulaMethods,covariates,
finterp, model,
parameters
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))