tempeff {modTempEff} | R Documentation |
Fits the constrained segmented distributed lag log-linear regression model to daily time series data of mortality and temperature and additional confounding factors.
tempeff(formula, z, data, tcontrol = temp.control(), pcontrol = p.control(), fcontrol = fit.control(), etastart = NULL, ndx.seas = 0, ...)
formula |
the model formula including the `response ~ parametric terms', see details. |
z |
the temperature variable. |
data |
the dataset where the variables are stored. |
tcontrol |
a list with components returned by temp.control() . |
pcontrol |
a list with components returned by p.control() . |
fcontrol |
a list with components returned by fit.control() . |
etastart |
possible starting values on the scale of the linear predictor. |
ndx.seas |
possible apparent dimension of the B-spline basis for seasonality
(actually the basis size is ndx.seas+3). If ndx.seas=0 no spline for seasonality is assumed. |
... |
additional arguments to be passed to tempeff.fit() ; currently unimplemented. |
This function fits a log-linear regression model to assess the effects of temperature on mortality.
It is assumed that the data are daily time series of mortality (or perhaps morbidity) and temperature.
The response and the confounders (such as influenza epidemics or day-of-week) have to be specified in the
formula
and the temperature itself in specified in the argument z
. Long-term trend and
seasonality may be modelled via P-splines by specifying the apparent dimension of the relevant basis via
the argument ndx.seas
.
The function returns an object of class "modTempEff"
. It is the list returned by gam.fit
of
package mgcv
with the additional components
psi |
The estimated breakpoint with corresponding standard error (bayesian and frequentist). |
betaCold |
The estimated DL coefficients for the cold effect. |
SE.c |
The frequentist standard errors of the cold DL estimates. |
SE.c.bayes |
The bayesian standard errors of the cold DL estimates. |
ToTcold |
Estimate and frequentist standard error of the total (net) effect of cold. |
ToTcold.bayes |
Estimate and bayesian standard error of the total (net) effect of cold. |
edf.cold |
The df associated at each spline coefficient of the DL curve of cold. |
rank.cold |
The apparent dimension of the B-spline basis of the DL for cold. |
betaHeat |
The estimated DL coefficients for the heat effect. |
SE.h |
The frequentist standard errors of the heat DL estimates. |
SE.h.bayes |
The bayesian standard errors of the heat DL estimates. |
ToTheat |
Estimate and frequentist standard error of the total (net) effect of heat. |
ToTheat.bayes |
Estimate and bayesian standard error of the total (net) effect of heat. |
edf.heat |
The df associated at each spline coefficient of the DL curve of heat. |
rank.heat |
The apparent dimension of the B-spline basis of the DL for heat. |
rank.seas |
When ndx.seas>0 , the apparent dimension of the B-spline basis for seasonality. |
edf.seas |
When ndx.seas>0 , the df associated at spline coefficients of seasonality. |
fit.seas |
When ndx.seas>0 , the fitted long-term trend (on the log scale). |
The first 'max(L)' observations are discarded before model fitting.
Vito Muggeo, vito.muggeo@unipa.it
Muggeo, V.M.R. (2008) Modeling temperature effects on mortality: multiple segmented relationships with common break points Biostatistics 9, 613–620.
modTempEff-package
, plot.modTempEff
, summary.modTempEff
,
gam.fit
in package mgcv
## Not run: library(modTempEff) data(dataset) o1<-tempeff(dec1~day+factor(dweek)+factor(year)+factor(month), data=dataset, tcontrol = temp.control(psi=20, L=c(60,60)), z=mtemp, pcontrol = p.control(ridge.formulas=NA), fcontrol = fit.control(display=TRUE)) o2<-update(o1, pcontrol=p.control(ridge.formulas=list(cold="xlag^2", heat="xlag^2"))) ## End(Not run)