statforbiology (version 0.9.9)

gnlht: Linear/nonlinear contrasts of model parameters

Description

This function calculates linear/nonlinear contrasts of model parameters and returns their estimates with delta standard errors.

Usage

## S3 method for class 'numeric'
gnlht(object, func, const, vcov.,
            parameterNames, dfr, ...)
## S3 method for class 'lm'
gnlht(object, func, const, vcov.,
            parameterNames, dfr, ...)
## S3 method for class 'nls'
gnlht(object, func, const, vcov.,
            parameterNames, dfr, ...)
## S3 method for class 'lme'
gnlht(object, func, const, vcov.,
            parameterNames, dfr, ...)
## S3 method for class 'nlme'
gnlht(object, func, const, vcov.,
            parameterNames, dfr, ...)
## S3 method for class 'drc'
gnlht(object, func, const, vcov.,
            parameterNames, dfr, ...)

Value

Returns a data.frame

Arguments

object

a named vector of parameter estimates, or a model object for which there are coef and vcov methods. The estimates are assumed as asymptotically normally distributed with covariance matrix returned by vcov. or passed as an argument

func

a list of functions or quoted strings that are the functions of the parameter estimates to be evaluated

const

If necessary, a dataframe whose columns are the constants to be used in the calculations above. For each row of this dataframe, the functions above are evaluated

vcov.

a variance-covariance matrix, or a function to calculate it from the model object

parameterNames

a character vector with namings for the parameters to be combined

dfr

number of degrees of freedom

...

Additional arguments

Author

Andrea Onofri

Details

Methods are given for several types of model fitting objects (lm, nls, lme, nlme, drc), from where model coefficients and a variance-covariance matrix are automatically retrieved. For other cases, a named vector of model parameters and a variance-covariance matrix can be provided as arguments to the 'gnlht()' function.

Examples

data(metamitron)
#Fit nls grouped model
modNlin <- nls(Conc ~ A[Herbicide] * exp(-k[Herbicide] * Time),
               start=list(A=rep(100, 4), k=rep(0.06, 4)),
               data=metamitron)
summary(modNlin)


# Compare parameters
funList <- list(~k1 - k2, ~k1 - k3, ~k1 - k4)
gnlht(modNlin, funList)

# Combine parameters
funList <- list(~ -log(0.5)/k1, ~ -log(0.5)/k2,
                ~ -log(0.5)/k3, ~ -log(0.5)/k4)
gnlht(modNlin, funList)

# Combine more flexibly
funList <- list(~ -log(prop)/k1, ~ -log(prop)/k2,
                ~ -log(prop)/k3, ~ -log(prop)/k4)
gnlht(modNlin, funList, const = data.frame(prop = 0.5))

funList <- list(~ -log(prop)/k1, ~ -log(prop)/k2,
                ~ -log(prop)/k3, ~ -log(prop)/k4)
gnlht(modNlin, funList, const = data.frame(prop = c(0.7, 0.5, 0.3)))

# Other possibilities
funList <- list(~ (k2 - k1)/(k1 * k2) * log(prop),
                ~ (k3 - k1)/(k1 * k3) * log(prop),
                ~ (k4 - k1)/(k1 * k4) * log(prop))
gnlht(modNlin, funList, const = data.frame(prop = c(0.7, 0.5, 0.3)))

# Predictions
funList <- list(~ A1 * exp (- k1 * Time), ~ A2 * exp (- k2 * Time),
                ~ A3 * exp (- k3 * Time), ~ A4 * exp (- k4 * Time))
propdF <- data.frame(Time = seq(0, 67, 1))
func <- funList
const <- propdF
pred <- gnlht(modNlin, funList, const = propdF)
head(pred)
tail(pred)