## ----setup, cache=FALSE, echo=FALSE-----------------------------------
library(knitr)
options(replace.assign=FALSE,width=72)
opts_chunk$set(fig.path='figs/gam-', cache.path='cache/gam-',
               fig.align='center', dev='pdf', fig.width=3.5,
               fig.height=3.5, fig.show='hold', pars=TRUE,
               tidy=FALSE,  comment=NA)
knit_hooks$set(pars=function(before, options, envir){
if (before && options$fig.show!='none') par(mar=c(4,4,1.6,.1),
              font.main=1, cex.lab=.95,cex.axis=.9,mgp=c(2,.7,0),
              tcl=-.3)
              par(options$pars)
}, crop=hook_pdfcrop)
pdf.options(pointsize=12)
oldopt <- options(digits=4)

## ----figControl-------------------------------------------------------
# To include the figures, change `showFigs <- FALSE`  
# to `showFigs <- TRUE` in the source `.Rnw` file,
# and regenerate the PDF.
#
showFigs <- FALSE

## ----fig6_1, eval=TRUE, echo=TRUE-------------------------------------
fig6.1 <- function(plotit=TRUE){
    matohms <- data.frame(model.matrix(with(fruitohms, ~ poly(juice, 4))))
    names(matohms) <- c("Intercept", paste("poly4",1:4, sep=""))
    form <- formula(paste(paste(names(matohms), collapse="+"), "~ juice"))
    matohms$juice <- fruitohms$juice
    gph1 <- xyplot(form, data=matohms, layout=c(1,5), scales=list(tck=0.5),
                   ylab="Basis terms",
                   strip=strip.custom(strip.names=TRUE,
                   var.name="",
                   sep=expression(""),
                   factor.levels=c("Constant","Linear","Quadratic",
                                   "Cubic","Quartic")),
                   panel=function(x,y,...){
                       llines(smooth.spline(x,y))},
                   outer=TRUE,
                   legend=list(top=list(fun=grid::textGrob,
                               args=list(label="A: Basis functions",
                               just="left", x=0))))
    b <- coef(lm(I(ohms/1000) ~ poly(juice,4), data=fruitohms))
    matohms <- sweep(model.matrix(with(fruitohms, ~ poly(juice, 4))),
                     2, b, "*")
    matohms <- data.frame(matohms)
    names(matohms) <- c("Intercept", paste("poly4",1:4, sep=""))
    form <- formula(paste(paste(names(matohms), collapse="+"), "~ juice"))
    matohms$juice <- fruitohms$juice
    matohms$Kohms <- fruitohms$ohms/1000
    nam <- lapply(1:5, function(x)substitute(A %*% B,
                                             list(A=round(b[x],2),
                                                  B=c("Constant","Linear",
                                                  "Quadratic","Cubic",
                                                  "Quartic")[x])))
    gph2 <- xyplot(form, data=matohms, layout=c(1,5),, scales=list(tck=0.5),
                   ylab="Add the contributions from these curves",
                   strip=strip.custom(strip.names=TRUE,
                   var.name="",
                   sep=expression(""),
                   factor.levels=as.expression(nam)),
                   panel=function(x,y,...){
                       llines(smooth.spline(x,y))},
                   outer=TRUE,
                   legend=list(top=list(fun=grid::textGrob,
                         args=list(label="B: Contribution to fitted curve",
                                   just="left", x=0))))
    if(plotit){
        print(gph1, position=c(0,0,.5,1))
        print(gph2, position=c(.5,0,1,1), newpage=FALSE)
}
    invisible(list(gph1, gph2))
}

## ----fig6_2, eval=TRUE, echo=TRUE-------------------------------------
fig6.2 <- function(){
    plot(ohms ~ juice, data=fruitohms, ylim=c(0, max(ohms)*1.02))
    ## 3 (=2+1) degrees of freedom natural spline
    fitns2 <- fitted(lm(ohms ~ splines::ns(juice, df=2), data=fruitohms))
    lines(fitns2 ~ juice, data=fruitohms, col="gray40")
    ## 4 (=3+1) degrees of freedom natural spline
    fitns3 <- fitted(lm(ohms ~ splines::ns(juice, df=3), data=fruitohms))
    lines(fitns3 ~ juice, data=fruitohms, lty=2, lwd=2, col="gray40")
    legend("topright", title="D.f. for cubic regression natural spline",
           legend=c("3  [ns(juice, 2)]",
           "4  [ns(juice, 3)]"),
           lty=c(1,2), lwd=c(1,2), cex=0.8)
    plot(ohms ~ juice, data=fruitohms, ylim=c(0, max(ohms)*1.02))
    ## 3 (=2+1) degrees of freedom natural spline
    fitns2 <- fitted(lm(ohms ~ splines::ns(juice, df=2), data=fruitohms))
    lines(fitns2 ~ juice, data=fruitohms, col="gray40")
    ## 4 (=3+1) degrees of freedom natural spline
    fitns3 <- fitted(lm(ohms ~ splines::ns(juice, df=3), data=fruitohms))
    lines(fitns3 ~ juice, data=fruitohms, lty=2, lwd=2, col="gray40")
    legend("topright", title="D.f. for cubic regression natural spline",
           legend=c("3  [ns(juice, 2)]",
           "4  [ns(juice, 3)]"),
           lty=c(1,2), lwd=c(1,2), cex=0.8)
}

## ----fig6_3, eval=TRUE, echo=TRUE-------------------------------------
fig6.3 <- function(){
    ohms.lm <- lm(ohms ~ ns(juice, df=3), data=fruitohms)
    termplot(ohms.lm, partial=TRUE, se=TRUE)
}

## ----fig6_4, eval=TRUE, echo=TRUE-------------------------------------
fig6.4 <- function(plotit=TRUE){
    matohms2 <- model.matrix(with(fruitohms, ~ splines::ns(juice, 2)))
    matohms3 <- model.matrix(with(fruitohms, ~ splines::ns(juice, 3)))
    m <- dim(matohms3)[1]
    longdf1 <- data.frame(juice=rep(fruitohms$juice,4),
                         basis2 = c(as.vector(matohms2),rep(NA,m)),
                         basis3 = as.vector(matohms3),
                         gp = factor(rep(c("Intercept",
                         paste("spline",1:3, sep="")),
                         rep(m,4))))
    gph1 <- xyplot(basis3 ~ juice | gp, data=longdf1, layout=c(1,4),
                  scales=list(tck=0.5),
                  ylab="Basis terms", strip=FALSE,
                  strip.left=strip.custom(strip.names=TRUE,
                  var.name="",
                  sep=expression(""),
                  factor.levels=c("Constant","Basis 1","Basis 2",
                  "Basis 3")),
                  par.settings=simpleTheme(lty=c(2,2,1,1)),
                  panel=function(x,y,subscripts){
                      llines(smooth.spline(x,y))
                      y2 <- longdf1$basis2[subscripts]
                      if(!any(is.na(y2))) llines(smooth.spline(x,y2),lty=1)},
                  outer=TRUE,
                  legend=list(top=list(fun=grid::textGrob,
                              args=list(label="A:Basis functions",
                              just="left", x=0))))
    b2 <- coef(lm(I(ohms/1000) ~ splines::ns(juice,2), data=fruitohms))
    b3 <- coef(lm(I(ohms/1000) ~ splines::ns(juice,3), data=fruitohms))
    spline2 <- as.vector(sweep(matohms2, 2, b2, "*"))
    spline3 <- as.vector(sweep(matohms3, 2, b3, "*"))
    longdf2 <- data.frame(juice=rep(fruitohms$juice,4),
                         spline2 = c(spline2, rep(NA,m)), spline3=spline3,
                         gp = factor(rep(c("Intercept",
                                     paste("spline",1:3, sep="")),
                         rep(m,4))))
    yran <- range(c(spline2, spline3))
    yran <- c(-6,8.5)
    gph2 <- xyplot(spline3 ~ juice | gp, data=longdf2, layout=c(1,4),
                  scales=list(tck=0.5, y=list(at=c(-4, 0, 4,8))), ylim=yran,
                  ylab="Add these contributions (ohms x 1000)", strip=FALSE,
                  strip.left=strip.custom(strip.names=TRUE,
                  var.name="",
                  sep=expression(""),
                  factor.levels=c("Const","Add 1","Add 2","Add 3")),
                  par.settings=simpleTheme(lty=c(2,2,1,1)),
                  panel=function(x,y,subscripts){
                      llines(smooth.spline(x,y))
                      y2 <- longdf2$spline2[subscripts]
                      if(!any(is.na(y2))) llines(smooth.spline(x,y2),lty=1)},
                  outer=TRUE,
                  legend=list(top=list(fun=grid::textGrob,
                              args=list(label="B: Contribution fo fitted curve",
                              just="left", x=0))))
    if(plotit){
        print(gph1, position=c(0,0,.5,1))
        print(gph2, position=c(.5,0,1,1), newpage=FALSE)
    }
    invisible(list(gph1, gph2))
}

## ----fig6_5, eval=TRUE, echo=TRUE-------------------------------------
fig6.5 <- function(){
    res <- resid(lm(log(Time) ~ log(Distance), data=worldRecords))
    wr.gam <- gam(res ~ s(log(Distance)), data=worldRecords)
    plot(wr.gam, residuals=TRUE, pch=1, las=1, ylab="Fitted smooth")
}

## ----fig6_6, eval=TRUE, echo=TRUE-------------------------------------
fig6.6 <-
function () {
    res <- resid(lm(log(Time) ~ log(Distance), data=worldRecords))
    wr.gam <- gam(res ~ s(log(Distance)), data=worldRecords)
    gam.check(wr.gam)
}

## ----fig6_7, eval=TRUE, echo=TRUE-------------------------------------
fig6.7 <-
function (mf=3,nf=2)
{
  opar <- par(mfrow=c(mf,nf), mar=c(0.25, 4.1, 0.25, 1.1))
    set.seed(29)         # Ensure exact result is reproducible
    res <- resid(lm(log(Time) ~ log(Distance), data=worldRecords))
    npanels <- mf*nf
    for(i in 1:npanels){
        permres <- sample(res)  # Random permutation
                                        # 0 for left-handers;  1 for right
        perm.gam <- gam(permres ~ s(log(Distance)), data=worldRecords)
        plot(perm.gam, las=1, rug=if(i<5) FALSE else TRUE, ylab="Fit")
    }
  par(opar)
}

## ----fig6_8, eval=TRUE, echo=TRUE-------------------------------------
fig6.8 <- function(){
    meuse.gam <- gam(log(lead) ~ s(elev) + s(dist) + ffreq + soil,
                           data=meuse)
    plot(meuse.gam, residuals=TRUE, se=TRUE, pch=1)
    termplot(meuse.gam, terms="ffreq", se=TRUE)
    termplot(meuse.gam, terms="soil", se=TRUE)
}

## ----fig6_9, eval=TRUE, echo=TRUE-------------------------------------
fig6.9 <- function(nsim=1000, caption=NULL){
  opar <- par(mfrow=c(1,2), oma=c(0,0,1.6,0.6))
  if(missing(caption))captCol <- "black" else captCol <- "blue"
  if(is.null(caption))caption <- paste("Graphs are from", nsim, "simulations")
    if(!exists("meuseML.gam"))
    meuseML.gam <- gam(log(lead) ~ s(elev) + s(dist) + ffreq + soil,
                       data=meuse, method="ML")
    if(!exists("meusexML.gam"))
    meusexML.gam <- gam(log(lead) ~ s(elev, dist) + ffreq + soil,
                        data=meuse, method="ML")
    ## Now simulate from meuseML.gam
    simY <- simulate(meuseML.gam, nsim=nsim)
    simResults <- matrix(0, nrow=nsim, ncol=3)
    colnames(simResults) <- c( "deltaDf", "fsim", "psim")
    for(i in 1:nsim){
        mML.gam <- gam(simY[,i] ~ s(elev) + s(dist) + ffreq + soil,
                       data=meuse, method="ML")
        mxML.gam <- gam(simY[,i] ~ s(elev, dist) + ffreq + soil,
                        data=meuse, method="ML")
        aovcomp <- anova(mML.gam, mxML.gam, test="F")
        simResults[i,] <- unlist(aovcomp[2, c("Df", "F", "Pr(>F)")])
    }
    ## Now plot the $p$-statistics and $F$-statistics
    ## against the change in degrees of freedom:
    colcode <- c("gray", "black")[1+(simResults[,"deltaDf"]>=1)]
    simResults <- as.data.frame(simResults)
    plot(psim ~ deltaDf, log="y", xlab="Change in degrees of freedom",
         ylab=expression(italic(p)*"-value"), col=colcode, data=simResults)
    abline(v=1, lty=2, col="gray")
    mtext("A", side=3, line=0.25, adj=0)
    mtext("1", side=1, at=1, line=0, cex=0.75)
    plot(fsim ~ deltaDf, log="y", xlab="Change in degrees of freedom",
         ylab=expression(italic(F)*"-statistic"),  col=colcode,
         data=simResults)
    abline(v=1, lty=2, col="gray")
    mtext("1", side=1, at=1, line=0, cex=0.75)
    mtext("B", side=3, line=0.25, adj=0)
  mtext(side=3, line=0.5, adj=0.052, outer=TRUE, caption, col=captCol)
    invisible(simResults)
  par(opar)
}

## ----fig6_10A, eval=TRUE, echo=TRUE-----------------------------------
fig6.10A <- function(){
    if(!exists("meuseML.gam"))
    meuseML.gam <- gam(log(lead) ~ s(elev) + s(dist) + ffreq + soil,
                       data=meuse, method="ML")
    plot(meuseML.gam)
    termplot(meuseML.gam, terms="ffreq", se=TRUE)
    termplot(meuseML.gam, terms="soil", se=TRUE)
    mtext(side=3, line=0.65, "A: Add effects of dist and elev", outer=TRUE,
          cex=0.8, adj=0)
}

## ----fig6_10B, eval=TRUE, echo=TRUE-----------------------------------
fig6.10B <- function(){
    if(!exists("meusexML.gam"))
    meusexML.gam <- gam(log(lead) ~ s(elev, dist) + ffreq + soil,
                        data=meuse, method="ML")
    plot(meusexML.gam)
    termplot(meusexML.gam, terms="ffreq", se=TRUE)
    termplot(meusexML.gam, terms="soil", se=TRUE)
    mtext(side=3, line=0.65, "B: Fit surface to dist and elev", outer=TRUE,
      cex=0.8, adj=0)
}

## ----fig6_10, eval=TRUE, echo=TRUE------------------------------------
fig6.10 <- function()
print("Run fig6.10A() and fig6.10B() separately")

## ----fig6_11, eval=TRUE, echo=TRUE------------------------------------
fig6.11 <- function(){
    hand <- with(cricketer, as.vector(as.vector(unclass(left)-1)))
                                        # 0 for left-handers
                                        # 1 for right
    hand.gam <- gam(hand ~ s(year), data=cricketer, family=binomial)
    plot(hand.gam, las=1, xlab="", ylab="Pr(left-handed)",
         trans=function(x)exp(x)/(1+exp(x)),
         shift=mean(predict(hand.gam)))
}

## ----fig6_12, eval=TRUE, echo=TRUE------------------------------------
fig6.12 <- function(mf=3,nf=2){
    opar <- par(mfrow=c(mf,nf), mar=c(0.25, 4.1, 0.25, 1.1))
    npanel <- mf*nf
    for(i in 1:npanel){
        hand <- sample(c(0,1), size=nrow(cricketer), replace=TRUE,
                       prob=c(0.185, 0.815))
                                        # 0 for left-handers
                                        # 1 for right
        hand.gam <- gam(hand ~ s(year), data=cricketer, family=binomial)
        plot(hand.gam, las=1, xlab="",
             rug=if(i<4)FALSE else TRUE,
             trans=function(x)exp(x)/(1+exp(x)),
             shift=mean(predict(hand.gam)))
        }
        par(opar)
}

## ----fig6_13, eval=TRUE, echo=TRUE------------------------------------
fig6.13 <- function(){
    rtlef <- data.frame(with(cricketer, as(table(year, left), "matrix")))
    rtlef <- within(rtlef, year <- as.numeric(rownames(rtlef)))
    right.gam <-  gam(right ~ s(year), data=rtlef, family=poisson)
    left.gam <-  gam(left ~ s(year), data=rtlef, family=poisson)
    rtlef <- within(rtlef,
                {fitright <- predict(right.gam, type="response")
                 fitleft <- predict(left.gam, type="response")})
    key.list <- list(text=expression("Right-handers", "Left-handers",
        "Left-handers "%*%" 4"),
                     corner=c(0,1), x=0, y=0.985,
                     points=FALSE, lines=TRUE)
    parset <- simpleTheme(col=c("blue", "purple", "purple"),
                          lty=c(1,1,2), lwd=c(2,2, 1))
    gph <- xyplot(fitright+fitleft+I(fitleft*4) ~ year, data=rtlef,
                  auto.key=key.list, par.settings=parset,tck=-0.05,
                  xlab="",
                  ylab="Number of cricketers\nborn in given year",
                  type="l", ylim=c(0,70))
    print(gph)
}

## ----fig6_14, eval=TRUE, echo=TRUE------------------------------------
countAccs <- function(data=airAccs, dateCol="Date", 
                      fromDate = as.Date("2006-01-01"),
                      by="1 day", prefix="num"){
  dfCount <- eventCounts(data, dateCol=dateCol,
                       from= fromDate, by=by,
                       prefix=prefix)
  dfCount[, "day"] <- julian(dfCount[,"Date"], origin=fromDate)
  dfCount
}
##
fig6.14 <- function()
    print("Run the separate functions fig6.14A() and fig6.14B()")
fig6.14A <- function(fromDate=as.Date("2006-01-01"), basis.df=50){
  if(!exists('dfDay06'))dfDay06 <- countAccs(fromDate=fromDate)
year <- seq(from=fromDate, to=max(dfDay06$Date), by="1 year")
atyear=julian(year, origin=fromDate)
dfDay06.gam <-
  gam(formula = num ~ s(day, k=basis.df), family = quasipoisson,
      data = dfDay06)
av <- mean(predict(dfDay06.gam))
plot(dfDay06.gam, xaxt="n", shift=av, trans=exp, rug=FALSE, xlab="",
     ylab="Estimated rate per day")
axis(1, at=atyear, labels=format(year, "%Y"))
mtext(side=3, line=0.75, "A: Events per day, vs date", adj=0)
}
fig6.14B <- function(fromDate=as.Date("2006-01-01"), basis.df=50){
  if(!exists('dfWeek06'))dfWeek06 <- countAccs(fromDate=fromDate,
                                               by="1 week")
dfWeek06.gam <- gam(num~s(day, k=basis.df), data=dfWeek06,
                    family=quasipoisson)
av <- mean(predict(dfWeek06.gam))
year <- seq(from=fromDate, to=max(dfWeek06$Date), by="1 year")
atyear=julian(year, origin=fromDate)
plot(dfWeek06.gam, xaxt="n", shift=av, trans=exp, rug=FALSE, xlab="",
      ylab="Estimated rate per week")
axis(1, at=atyear, labels=format(year, "%Y"))
mtext(side=3, line=0.75, "B: Events per week, vs date", adj=0)
}

## ----pkgs-figs6, eval=showFigs, message=FALSE, warning=FALSE----------
#  pkgs <- c("DAAG","mgcv","splines","gamclass")
#  z <- sapply(pkgs, require, character.only=TRUE, warn.conflicts=FALSE)
#  if(any(!z)){
#    notAvail <- paste(names(z)[!z], collapse=", ")
#    print(paste("The following packages require to be installed:", notAvail))
#  }

## ----provide-meuse, eval=showFigs-------------------------------------
#  if(!exists("meuse")){
#      msg <- "Cannot find package 'sp',"
#          if(!require("sp"))
#            return(paste(msg, "cannot do graph."))
#      data("meuse", package="sp", envir = environment())
#      }

## ----fig6_1x, eval=showFigs, echo=TRUE, fig.width=6, fig.height=4.75, out.width="0.725\\textwidth"----
#  fig6.1()

## ----fig6_2x, eval=showFigs, echo=TRUE, fig.width=5, fig.height=3.5, out.width="0.605\\textwidth"----
#  fig6.2()

## ----fig6_3x, eval=showFigs, echo=TRUE, fig.width=4.5, fig.height=3, out.width="0.605\\textwidth"----
#  fig6.3()

## ----fig6_4x, eval=showFigs, echo=TRUE, fig.width=6, fig.height=4.5, out.width="0.8\\textwidth"----
#  fig6.4()

## ----fig6_5x, eval=showFigs, echo=TRUE, fig.width=4.5, fig.height=2.5, out.width="0.625\\textwidth"----
#  fig6.5()

## ----fig6_6x, eval=showFigs, echo=TRUE, fig.width=6, fig.height=5.5, out.width="0.8\\textwidth"----
#  fig6.6()

## ----fig6_7x, eval=showFigs, echo=TRUE, fig.width=6, fig.height=3.75, out.width="0.75\\textwidth"----
#  fig6.7()

## ----fig6_8x, eval=showFigs, echo=TRUE, fig.width=6, fig.height=5.5, pars=list(mar=c(3.6,3.1,2.1, 1.6), mgp=c(2.25, 0.5, 0), oma=c(0,0,2.1,0), mfrow=c(2,2)), out.width="0.8\\textwidth"----
#  if(exists("meuse")) fig6.8() else
#    print("Cannot locate data set 'meuse', hence cannot plot")

## ----fig6_9x, eval=showFigs, echo=TRUE, fig.width=5.5, fig.height=3.25, out.width="0.97\\textwidth"----
#  {
#  caption <- paste("These are from 25 simulations.",
#                   "More usefully, try, eg: fig6.9(nsim=500)")
#  if(exists("meuse")) fig6.9(nsim=25, caption=caption) else
#    print("Cannot locate data set 'meuse', hence cannot plot")
#  }

## ----fig6_10Ax, eval=showFigs, echo=TRUE, fig.width=5, fig.height=4.5, pars=list(mar=c(4.1,4.1,2.1, 1.6), mex=0.8, oma=c(0,0,2.1,0), mfrow=c(2,2)), out.width="0.875\\textwidth"----
#  if(exists("meuse")) fig6.10A() else
#    print("Cannot locate data set 'meuse', hence cannot plot")

## ----fig6_10Bx, eval=showFigs, echo=TRUE, fig.width=6, fig.height=2.25, pars=list(mar=c(4.1,4.1,2.1, 1.6), mex=0.8, oma=c(0,0,2.1,0), mfrow=c(1,3)), out.width="0.98\\textwidth"----
#  if(exists("meuse")) fig6.10B() else
#    print("Cannot locate data set 'meuse', hence cannot plot")

## ----fig6_11x, eval=showFigs, echo=TRUE, fig.width=4.5, fig.height=2.75, out.width="0.75\\textwidth"----
#  fig6.11()

## ----fig6_12x, eval=showFigs, echo=TRUE, fig.width=6, fig.height=4.5, out.width="0.75\\textwidth"----
#  fig6.12()

## ----fig6_13x, eval=showFigs, echo=TRUE, fig.width=5, fig.height=3.25, out.width="0.75\\textwidth"----
#  fig6.13()

## ----fig6_14x, eval=showFigs, echo=TRUE, fig.width=3.0, fig.height=2.5, out.width="0.47\\textwidth"----
#  {
#    fig6.14A(fromDate=as.Date("2010-01-01"), basis.df=50)
#    fig6.14B(fromDate=as.Date("2010-01-01"), basis.df=50)
#  }

