finterp {rmutil} | R Documentation |
finterp
translates a model formula into a function of the
unknown parameters or of a vector of them. Such language formulae can
either be in Wilkinson and Rogers notation or be expressions
containing both known (existing) covariates and unknown (not existing)
parameters. In the latter, factor variables cannot be used and
parameters must be scalars.
The covariates in the formula are sought in the environment or in the
data object provided. If the data object has class, repeated,
times
will use the response times as a covariate,
individuals
will use the index for individuals as a factor
covariate, and nesting
the index for nesting as a factor
covariate.
Note that, in parameter displays, formulae in Wilkinson and Rogers notation use variable names whereas those with unknowns use the names of these parameters, as given in the formulae, and that the meaning of operators (*, /, :, etc.) is different in the two cases.
finterp(z, envir=sys.frame(sys.parent()), formula=FALSE, vector=TRUE, start=1, name=NULL, expand=TRUE)
z |
A model formula beginning with ~, either in Wilkinson and Rogers notation or containing unknown parameters. |
envir |
The environment in which the formula is to be interpreted or a data object of class, repeated, tccov, or tvcov. |
formula |
If TRUE and the formula is in Wilkinson and Rogers notation, just returns the formula. |
vector |
If FALSE and the formula contains unknown parameters,
the function returned has them as separate arguments, if TRUE, it has
one argument, the unknowns as a vector. Always true if envir is
a data object. |
start |
The starting index value of the parameter vector in the function returned. |
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 finterp 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. |
A function, of class formulafn, of the unknown parameters or of a
vector of them is returned. Its attributes give the formula supplied,
the model function produced, the covariate names, the parameter names,
and the range of values of the index of the parameter vector. If
formula
is TRUE and a Wilkinson and Rogers formula was
supplied, it is simply returned instead of creating a function.
J.K. Lindsey
x1 <- rpois(20,2) x2 <- rnorm(20) # # Wilkinson and Rogers formula with three parameters fn1 <- finterp(~x1+x2) fn1 fn1(rep(2,3)) # the same formula with unknowns fn2 <- finterp(~b0+b1*x1+b2*x2) fn2 fn2(rep(2,3)) # # nonlinear formulae with unknowns # log link fn2a <- finterp(~exp(b0+b1*x1+b2*x2)) fn2a fn2a(rep(0.2,3)) # compartment model times <- 1:20 # exp() parameters to ensure that they are positive fn3 <- finterp(~exp(volume)*exp(absorption)/(exp(absorption)- exp(elimination))*(exp(-exp(elimination)*times)- exp(-exp(absorption)*times))) fn3 fn3(log(c(3,0.3,0.2))) # # Poisson density y <- rpois(20,5) fn4 <- finterp(~mu^y*exp(-mu)/gamma(y+1)) fn4 fn4(5) dpois(y,5) # # Poisson likelihood # mean parameter fn5 <- finterp(~-y*log(mu)+mu+lgamma(y+1),vector=F) fn5 likefn1 <- function(p) sum(fn5(mu=p)) nlm(likefn1,p=1) mean(y) # canonical parameter fn5a <- finterp(~-y*theta+exp(theta)+lgamma(y+1),vector=F) fn5a likefn1a <- function(p) sum(fn5a(theta=p)) nlm(likefn1a,p=1) # # likelihood for Poisson log linear regression y <- rpois(20,fn2a(c(0.2,1,0.4))) nlm(likefn1,p=1) mean(y) likefn2 <- function(p) sum(fn5(mu=fn2a(p))) nlm(likefn2,p=c(1,0,0)) # or likefn2a <- function(p) sum(fn5a(theta=fn2(p))) nlm(likefn2a,p=c(1,0,0)) # # likelihood for Poisson nonlinear regression y <- rpois(20,fn3(log(c(3,0.3,0.2)))) nlm(likefn1,p=1) mean(y) likefn3 <- function(p) sum(fn5(mu=fn3(p))) nlm(likefn3,p=log(c(1,0.4,0.1))) # # envir as data objects y <- matrix(rnorm(20),ncol=5) y[3,3] <- y[2,2] <- NA x1 <- 1:4 x2 <- c("a","b","c","d") resp <- restovec(y) xx <- tcctomat(x1) xx2 <- tcctomat(data.frame(x1,x2)) 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) reps2 <- rmna(resp, ccov=xx2, tvcov=zz) rm(y, x1, x2 , z1, z2) # # repeated objects # # time-constant covariates # Wilkinson and Rogers notation form1 <- ~x1 print(fn1 <- finterp(form1, envir=reps)) fn1(2:3) print(fn1a <- finterp(form1, envir=xx)) fn1a(2:3) form1b <- ~x1+x2 print(fn1b <- finterp(form1b, envir=reps2)) fn1b(2:6) print(fn1c <- finterp(form1b, envir=xx2)) fn1c(2:6) # with unknown parameters form2 <- ~a+b*x1 print(fn2 <- finterp(form2, envir=reps)) fn2(2:3) print(fn2a <- finterp(form2, envir=xx)) fn2a(2:3) # # time-varying covariates # Wilkinson and Rogers notation form3 <- ~z1+z2 print(fn3 <- finterp(form3, envir=reps)) fn3(2:4) print(fn3a <- finterp(form3, envir=zz)) fn3a(2:4) # with unknown parameters form4 <- ~a+b*z1+d*z2 print(fn4 <- finterp(form4, envir=reps)) fn4(2:4) print(fn4a <- finterp(form4, envir=zz)) fn4a(2:4) # # note: lengths of x1 and z2 differ # Wilkinson and Rogers notation form5 <- ~x1+z2 print(fn5 <- finterp(form5, envir=reps)) fn5(2:4) # with unknown parameters form6 <- ~a+b*x1+d*z2 print(fn6 <- finterp(form6, envir=reps)) fn6(2:4) # # with times # Wilkinson and Rogers notation form7 <- ~x1+z2+times print(fn7 <- finterp(form7, envir=reps)) fn7(2:5) form7a <- ~x1+x2+z2+times print(fn7a <- finterp(form7a, envir=reps2)) fn7a(2:8) # with unknown parameters form8 <- ~a+b*x1+d*z2+e*times print(fn8 <- finterp(form8, envir=reps)) fn8(2:5) # # with a variable not in the data object form9 <- ~a+b*z1+d*z2+e*z3 print(fn9 <- finterp(form9, envir=reps)) fn9(2:5) # z3 assumed to be an unknown parameter: fn9(2:6)