predict-methods

0th

Percentile

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)

Keywords
methods
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",…)
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

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
## 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)
# }

Documentation reproduced from package bbmle, version 1.0.23.1, License: GPL

Community examples

Looks like there are no examples yet.