calcSmoothTrends: Smooth Basis Functions for a STdata Object
In SpatioTemporal: Spatio-Temporal Model Estimation

Description Usage Arguments Details Value Author(s) See Also Examples

A front end function for calling SVDsmooth (and SVDsmoothCV), with either a STdata object or vectors containing observations, dates and locations.

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calcSmoothTrends(STdata = NULL, obs = STdata$obs$obs,
  date = STdata$obs$date, ID = STdata$obs$ID, subset = NULL,
  extra.dates = NULL, n.basis = 2, cv = FALSE, ...)

`STdata`	A `STdata`/`STmodel` data structure containing observations, see `mesa.data.raw`. Use either this or the `obs`, `date`, and `ID` inputs.
`obs`	A vector of observations.
`date`	A vector of observation times.
`ID`	A vector of observation locations.
`subset`	A subset of locations to extract the data matrix for. A warning is given for each name not found in `ID`.
`extra.dates`	Additional dates for which smooth trends should be computed (any duplicates will be removed).
`n.basis`	Number of basis functions to compute, see `SVDsmooth`.
`cv`	Also compute smooth functions using leave one out cross-validation, see `SVDsmoothCV`.
`...`	Additional parameters passed to `SVDsmooth` and `SVDsmoothCV`; except `fnc`, which is always `TRUE`.

The function uses createDataMatrix to create a data matrix which is passed to SVDsmooth (and SVDsmoothCV). The output can be used as
STdata$trend = calcSmoothTrends(...)$trend, or
STdata$trend = calcSmoothTrends(...)$trend.cv[[i]]. However, it is recommended to use updateTrend.STdata.

Returns a list with

`trend`	A data.frame containing the smooth trends and the dates. This can be used as the `trend` in `STdata$trend`.
`trend.cv`	If `cv==TRUE` a list of data.frames; each one containing the smooth trend obtained when leaving one site out. Similar to `SVDsmoothCV(data)$smoothSVD[[1]]`).
`trend.fnc,trend.fnc.cv`	Functions that produce the content of the above data.frames, see `SVDsmooth`.

Johan Lindstrom and Paul D. Sampson

Other SVD for missing data: SVDmiss, SVDsmooth, plot.SVDcv, print.SVDcv, updateTrend.STdata

##let's load some data
data(mesa.model)

##let's compute two smooth trend functions
trend <- calcSmoothTrends(mesa.model, n.basis=2)

##or with some other parameters for the splines
trend.alt <- calcSmoothTrends(mesa.model, n.basis=2, df=100)

##and study the trends
par(mfrow=c(2,1), mar=c(2.5,2.5,.5,.5))
plot(trend$trend$date, trend$trend$V1, type="l", ylab="", xlab="",
     ylim=range(c(trend$trend$V1, trend$trend$V2)))
lines(trend$trend$date, trend$trend$V2, col=2)
plot(trend.alt$trend$date, trend.alt$trend$V1,
     type="l", ylab="", xlab="",
     ylim=range(c(trend.alt$trend$V1, trend.alt$trend$V2)))
lines(trend.alt$trend$date, trend.alt$trend$V2, col=2)

##Let's exclude locations with fewer than 100 observations
IND <- names(which(table(mesa.model$obs$ID) >= 100))
##now we also compute the CV trends.
trend2 <- calcSmoothTrends(mesa.model, n.basis=2, subset=IND, cv=TRUE)

##Let's compare to the previous result
lines(trend2$trend$date, trend2$trend$V1, lty=2)
lines(trend2$trend$date, trend2$trend$V2, lty=2, col=2)

##we can also study the cross validated results to examine the
##possible variation in the estimated trends.
plot(trend$trend$date, trend2$trend$V1, type="n", ylab="", xlab="",
     ylim=range(c(trend2$trend$V1, trend2$trend$V2)))
for(i in 1:length(trend2$trend.cv)){
  lines(trend2$trend.cv[[i]]$date, trend2$trend.cv[[i]]$V1, col=1)
  lines(trend2$trend.cv[[i]]$date, trend2$trend.cv[[i]]$V2, col=2)
}

##trend functions for every week (mesa.model$obs$date is every 2-weekss)
trend.more <- calcSmoothTrends(mesa.model, n.basis=2,
                               extra.dates=seq(min(mesa.model$obs$date),
                                 max(mesa.model$obs$date), by=7))
##This results in a message detailing how many times that
##have hade interpolated trends (i.e. no data)

##compare to the earlier
plot(trend$trend$date, trend$trend$V1, pch=19,
     ylim=range(c(trend$trend$V1, trend.more$trend$V1)))
points(trend.more$trend$date, trend.more$trend$V1, col=2, pch=3)