predict-methods
From bbmle v1.0.23.1
by Ben Bolker
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
- …
additional arguments (for generic compatibility)
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
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)
# }
Community examples
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