plot.modTempEff {modTempEff} | R Documentation |
Plots distributed lags curves from the modTempEff
fit.
## S3 method for class 'modTempEff': plot(x, which = c("cold", "heat"), add=FALSE, new=TRUE, var.bayes = FALSE, delta.rr = TRUE, level = 0.95, ...)
x |
object of class "modTempEff" . |
which |
Which DL curve should be plotted? for cold, heat or both of them (default). |
add |
logical; if TRUE the fitted DL curve for cold or heat is added
to an existing plot. |
new |
logical indicating if a new device should be opened. If add=TRUE , new
is set to FALSE . |
var.bayes |
logical indicating if the 'Bayesian' rather than the frequentist standard errors should be employed to compute the pointwise confidence intervals to be plotted |
delta.rr |
logical indicating if the DL curves should be plotted on the log scale or as per cent change in relative risk, i.e. 100*(exp(.)-1). |
level |
the selected confidence level of the pointwise confidence intervals to be plotted |
... |
additional arguments.. |
Takes a fitted "modTempEff"
object produced by tempeff()
and plots the
DL curves for cold and heat effect with relevant pointwise confidence intervals.
plot.modTempEff
also works with objects with fixed (not estimated) breakpoint, namely
fits returned by
tempeff(.., fcontrol=fit.control(it.max=0))
.
Note add=TRUE
makes sense (and works) only for a single (cold or heat) DL curve to be superimposed to an existing plot.
The function simply plots the required estimated DL curve. If the fitted model includes only a smooth term for
the long term trend, plot.modTempEff
draws it.
Vito Muggeo
## Not run: #obj is an object returned by tempeff() #plots DL curves for cold and heat with 95% pointwise CI # using frequentist standard errors plot(obj) #plots the estimated DL curve only for heat with 90% pointwise CI # using bayesian standard errors plot(obj, "heat", var.bayes=TRUE, level=.90) ## End(Not run)