nonlin.function: Functions to Specify Nonlinear Terms in gnm Models

nonlin.functionR Documentation

Functions to Specify Nonlinear Terms in gnm Models

Description

Nonlinear terms maybe be specified in the formula argument to gnm by a call to a function of class "nonlin". A "nonlin" function takes a list of arguments and returns a list of arguments for the internal nonlinTerms function.

Arguments

...

arguments required to define the term, e.g. symbolic representations of predictors in the term.

inst

(optional) an integer specifying the instance number of the term - for compatibility with instances.

Value

The function should return a list with the following components:

predictors

a list of symbolic expressions or formulae with no left hand side which represent (possibly nonlinear) predictors that form part of the term. Intercepts will be added by default to predictors specified by formulae. If predictors are named, these names will be used as a prefix for parameter labels or the parameter label itself in the single parameter case (in either case, prefixed by the call if supplied.) Predictors that may include an intercept should always be named or matched to a call.

variables

an optional list of expressions representing variables in the term.

term

a function which takes the arguments predLabels and varLabels, which are vectors of labels defined by gnm that correspond to the specified predictors and variables, and returns a deparsed mathematical expression of the full term. Only functions recognised by deriv should be used in the expression, e.g. + rather than sum.

common

an optional numeric index of predictors with duplicated indices identifying single factor predictors for which homologous effects are to be estimated.

call

an optional call to be used as a prefix for parameter labels, specified as an R expression.

match

(if call is non-NULL) a numeric index of predictors specifying which arguments of call the predictors match to - zero indicating no match. If NULL, predictors will not be matched. It is recommended that matches are specified wherever possible, to ensure parameter labels are well-defined. Parameters in matched predictors are labelled using "dot-style" labelling, see examples.

start

an optional function which takes a named vector of parameters corresponding to the predictors and returns a vector of starting values for those parameters. This function is ignored if the term is nested within another nonlinear term.

Author(s)

Heather Turner

See Also

Const to specify a constant, Dref to specify a diagonal reference term, Exp to specify the exponential of a predictor, Inv to specify the reciprocal of a predictor, Mult to specify a multiplicative interaction, MultHomog to specify a homogeneous multiplicative interaction,

Examples

### Equivalent of weighted.MM function in ?nls
weighted.MM <- function(resp, conc){
    list(predictors = list(Vm = substitute(conc), K = 1),
         variables = list(substitute(resp), substitute(conc)),
         term = function(predictors, variables) {
             pred <- paste("(", predictors[1], "/(", predictors[2],
                           " + ", variables[2], "))", sep = "")
             pred <- paste("(", variables[1], " - ", pred, ")/sqrt(",
                           pred, ")", sep = "")
         })
}
class(weighted.MM) <- "nonlin"

## use to fitted weighted Michaelis-Menten model
Treated <- Puromycin[Puromycin$state == "treated", ]
Pur.wt.2 <- gnm( ~ -1 + weighted.MM(rate, conc), data = Treated,
                start = c(Vm = 200, K = 0.1), verbose = FALSE)
Pur.wt.2
## 
## Call:
## gnm(formula = ~-1 + weighted.MM(rate, conc), data = Treated, 
##     start = c(Vm = 200, K = 0.1), verbose = FALSE)
## 
## Coefficients:
##        Vm          K  
## 206.83477    0.05461  
## 
## Deviance:            14.59690 
## Pearson chi-squared: 14.59690 
## Residual df:         10

### The definition of MultHomog
MultHomog <- function(..., inst = NULL){
    dots <- match.call(expand.dots = FALSE)[["..."]]
    list(predictors = dots,
         common = rep(1, length(dots)),
         term = function(predictors, ...) {
             paste("(", paste(predictors, collapse = ")*("), ")", sep = "")
         },
         call = as.expression(match.call()))
}
class(MultHomog) <- "nonlin"
## use to fit homogeneous multiplicative interaction
set.seed(1)
RChomog <- gnm(Freq ~ origin + destination + Diag(origin, destination) +
               MultHomog(origin, destination), ofInterest = "MultHomog",
               family = poisson, data = occupationalStatus,
               verbose = FALSE)
RChomog
## 
## Call:
## 
## gnm(formula = Freq ~ origin + destination + Diag(origin, destination) + 
##     MultHomog(origin, destination), ofInterest = "MultHomog", family = poisson, 
##     data = occupationalStatus, verbose = FALSE)
## 
## Coefficients of interest:
## MultHomog(origin, destination)1  
##                              -1.50089  
## MultHomog(origin, destination)2  
##                              -1.28260  
## MultHomog(origin, destination)3  
##                              -0.68443  
## MultHomog(origin, destination)4  
##                              -0.10055  
## MultHomog(origin, destination)5  
##                              -0.08338  
## MultHomog(origin, destination)6  
##                               0.42838  
## MultHomog(origin, destination)7  
##                               0.84452  
## MultHomog(., .).`origin|destination`8  
##                               1.08809  
## 
## Deviance:            32.56098 
## Pearson chi-squared: 31.20716 
## Residual df:         34 
##

## the definition of Exp
Exp <- function(expression, inst = NULL){
    list(predictors = list(substitute(expression)),
         term = function(predictors, ...) {
             paste("exp(", predictors, ")", sep = "")
         },
         call = as.expression(match.call()),
         match = 1)
}
class(Exp) <- "nonlin"


## use to fit exponentional model
x <- 1:100
y <- exp(- x / 10)
set.seed(4)
exp1 <- gnm(y ~ Exp(1 + x), verbose = FALSE)
exp1
## 
## Call:
## gnm(formula = y ~ Exp(1 + x), verbose = FALSE)
## 
## Coefficients:
##            (Intercept)  Exp(. + x).(Intercept)
##              1.549e-11              -7.934e-11
##           Exp(1 + .).x  
##             -1.000e-01 
## 
## Deviance:            9.342418e-20 
## Pearson chi-squared: 9.342418e-20 
## Residual df:         97

gnm documentation built on April 29, 2022, 5:06 p.m.