finterp {gamlss.nl} | R Documentation |
This function is taken from Jim Lindsey's R package rmutil
.
What follows is taken from the help file of rmutil
.
Note that not all the functionalities of finterp
are implemented in nlgamlss
.
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' or 'response', then the key words, 'times' will use the response times from the data object as a covariate, 'individuals' will use the index for individuals as a factor covariate, and 'nesting' the index for nesting as a factor covariate. The latter two only work for W&R notation.
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.
The function fmobj
inspects a formula and returns a list containing the
objects referred to, with indicators as to which are unknown
parameters, covariates, factor variables, and functions.
finterp.default(.z, .envir = parent.frame(), .formula = FALSE, .vector = TRUE, .args = NULL, .start = 1, .name = NULL, .expand = TRUE, .intercept = TRUE, .old = NULL, .response = FALSE, ...) finterp(.z, ...) fmobj(z, envir = parent.frame())
.z |
A model formula beginning with ~, either in Wilkinson and Rogers notation or containing unknown parameters. If it contains unknown parameters, it can have several lines so that, for example, local variables can be assigned temporary values. In this case, enclose the formula in curly brackets |
.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, unless certain parameter names are specified in '.args'. Always TRUE if '.envir' is a data object. |
.args |
If '.vector' is TRUE, names of parameters that are to be function arguments and not included in the vector. |
.start |
The starting index value of the parameter vector in the function returned when '.vector' is TRUE. |
.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'. |
.intercept |
If W&R notation is supplied and '.intercept=F', a model function without intercept is returned. |
.old |
The name of an existing object of class 'formulafn' which has common parameters with the one being created, or a list of such objects. Only used if '.vector'=TRUE. The value of '.start' should ensure that there is no conflict in indexing the vector. |
.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. |
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. |
... |
for extra arguments |
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.
For fmobj
a list, of class 'fmobj', containing a character vector
('objects') with the names of the objects used in a formula, and
logical vectors indicating which are unknown parameters
('parameters'), covariates ('covariates'), factor variables
('factors'), and functions ('functions') is returned.
J.K. Lindsey
http://popgen.unimaas.nl/~jlindsey/index.html: Jim Lindsey web page
help
, ~~~
# From Jim 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)) # parameters common to two functions fn2b <- finterp(~c0+c1*exp(b0+b1*x1+b2*x2), .old=fn2a, .start=4) fn2b # function returned also depends on values of another function fn2c <- finterp(~fn2+c1*exp(b0+b1*x1+b2*x2), .old=fn2a, .start=4, .args="fn2") fn2c args(fn2c) fn2c(rep(0.2,4),fn2(rep(2,3))) # # compartment model times <- 1:20 # exp() parameters to ensure that they are positive fn3 <- finterp(~exp(absorption-volume)/(exp(absorption)- exp(elimination))*(exp(-exp(elimination)*times)- exp(-exp(absorption)*times))) fn3 fn3(log(c(0.3,3,0.2))) # a more efficient way # (note that parameters do not appear in the same order) form <- ~{ ka <- exp(absorption) ke <- exp(elimination) ka*exp(-volume)/(ka-ke)*(exp(-ke*times)-exp(-ka*times))} fn3a <- finterp(form) fn3a(log(c(0.3,0.2,3))) # # 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=FALSE) 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=FALSE) 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+c*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+c*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+c*z2+e*times #print(fn8 <- finterp(form8, .envir=reps)) #fn8(2:5) # # with a variable not in the data object #form9 <- ~a+b*z1+c*z2+e*z3 #print(fn9 <- finterp(form9, .envir=reps)) #fn9(2:5) # z3 assumed to be an unknown parameter: #fn9(2:6) # # multiline formula #form10 <- ~{ # tmp <- exp(b) # a+tmp*z1+c*z2+d*times} #print(fn10 <- finterp(form10, .envir=reps)) #fn10(2:5) # for fmobj x1 <- rpois(20,2) x2 <- rnorm(20) x3 <- gl(2,10) # # W&R formula fmobj(~x1+x2+x3) # # formula with unknowns fmobj(~b0+b1*x1+b2*x2) # # nonlinear formulae with unknowns # log link fmobj(~exp(b0+b1*x1+b2*x2))