residuals.dfrr {dfrr} | R Documentation |
Obtain residuals for a dfrr model
Description
Returns the residuals of a fitted dfrr
model.
A dfrr
model is of the form:
Y_{i}(t)=I(W_{i}(t)>0),
in which I(.)
is the indicator function and W_{i}(t)=Z_{i}(t)+\epsilon_{i}(t)\times\sigma^2
, where Z_{i}(t)
is the functional part of the model and epsilon_{i}(t)\times\sigma^2
is the measurement error.
The functional part of the model, consisting a location and a residual function of the form:
Z_{i}(t)=\sum_{j=1}^{q}\beta_{j}(t)*x_{ji}+\varepsilon_{i}(t),
and \epsilon_{i}(t)
are iid standard normal for each i
and t
.
The residuals reported in the output of this functions is the estimation of the
measurement error of the model i.e. \epsilon_{i}(t)\times\sigma^2
, which is estimated by:
E(W_{i}(t)-Z_{i}(t)\mid Y_{i}(t)).
Usage
## S3 method for class 'dfrr'
residuals(object, standardized = NULL, unstandardized = !standardized, ...)
Arguments
object |
a fitted |
standardized , unstandardized |
a |
... |
dot argument, just for consistency with the generic function |
Value
This function returns either a matrix
or a data.frame
.
If the argument ydata is specified, the return value is 'ydata' with
a column added, namely 'residual'. Otherwise, the return value
is a matrix of residuals of dimension NxM where N is the number of sample curves,
and M is the length of argument 'yind' passed to the function dfrr
.
See Also
Examples
set.seed(2000)
N<-50;M<-24
X<-rnorm(N,mean=0)
time<-seq(0,1,length.out=M)
Y<-simulate_simple_dfrr(beta0=function(t){cos(pi*t+pi)},
beta1=function(t){2*t},
X=X,time=time)
#The argument T_E indicates the number of EM algorithm.
#T_E is set to 1 for the demonstration purpose only.
#Remove this argument for the purpose of converging the EM algorithm.
dfrr_fit<-dfrr(Y~X,yind=time,T_E=1)
resid<-residuals(dfrr_fit)
plot(resid)
# We can also use the qq function to draw the QQ-plot.