predict-methods

Predicted values from an mle2 fit

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

Usage

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

Note

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

Methods

x = "mle2"

an mle2 fit

Aliases

• gfun
• predict-methods
• predict,mle2-method
• residuals,mle2-method
• simulate,mle2-method

Examples

# NOT RUN {
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)
# }