predict-methods: Predicted values from an mle2 fit

Description Usage Arguments Methods Note Examples

Description

Given an mle2 fit and an optional list of new data, return predictions (more generally, summary statistics of the predicted distribution)

Usage

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    ## S4 method for signature 'mle2'
predict(object, newdata=NULL,
                         location="mean", newparams=NULL, ...)
    ## S4 method for signature 'mle2'
simulate(object, nsim,
                         seed, newdata=NULL, newparams=NULL, ...)
    ## S4 method for signature 'mle2'
residuals(object,type=c("pearson","response"),
                   location="mean",...)

Arguments

object

an mle2 object

newdata

optional list of new data

newparams

optional vector of new parameters

location

name of the summary statistic to return

nsim

number of simulations

seed

random number seed

type

residuals type

...

additional arguments (for generic compatibility)

Methods

x = "mle2"

an mle2 fit

Note

For some models (e.g. constant models), predict may return a single value rather than a vector of the appropriate length.

Examples

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set.seed(1002)
lymax <- c(0,2)
lhalf <- 0
x <- runif(200)
g <- factor(rep(c("a","b"),each=100))
y <- rnbinom(200,mu=exp(lymax[g])/(1+x/exp(lhalf)),size=2)
dat <- data.frame(y,g,x)

fit3 <- mle2(y~dnbinom(mu=exp(lymax)/(1+x/exp(lhalf)),size=exp(logk)),
    parameters=list(lymax~g),
    start=list(lymax=0,lhalf=0,logk=0),
data=dat)

plot(y~x,col=g)
## true curves
curve(exp(0)/(1+x/exp(0)),add=TRUE)
curve(exp(2)/(1+x/exp(0)),col=2,add=TRUE)
## model predictions
xvec = seq(0,1,length=100)
lines(xvec,predict(fit3,newdata=list(g=factor(rep("a",100),levels=c("a","b")),
                                x = xvec)),col=1,lty=2)
lines(xvec,predict(fit3,newdata=list(g=factor(rep("b",100),levels=c("a","b")),
                                x = xvec)),col=2,lty=2)


## comparing automatic and manual predictions
p1 = predict(fit3)
p2A =
with(as.list(coef(fit3)),exp(`lymax.(Intercept)`)/(1+x[1:100]/exp(lhalf)))
p2B =
with(as.list(coef(fit3)),exp(`lymax.(Intercept)`+lymax.gb)/(1+x[101:200]/exp(lhalf)))
all(p1==c(p2A,p2B))
##
simulate(fit3)


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