Nothing
R/traitSmooth.r
In growthPheno: Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits
Defines functions
predict.pSpline
pSpline
predict.ncsSpline
ncsSpline
plotDeviationsBoxes
Documented in
plotDeviationsBoxes
#Function to produce a single plot of deviations boxplots
plotDeviationsBoxes <- function(data, observed, smoothed, x.factor,
x.title = NULL, y.titles = NULL,
facet.x = ".", facet.y = ".",
facet.labeller = NULL,
facet.scales = "fixed",
angle.x = 0,
deviations.plots = "absolute",
ggplotFuncs = NULL, printPlot = TRUE, ...)
{
options <- c("none", "profiles", "absolute.boxplots", "relative.boxplots",
"medians.deviations", "compare.medians") #needed for probeSmoothing only
devnplots <- options[unlist(lapply(deviations.plots, check.arg.values,
options=options))]
if ("none" %in% devnplots)
devnplots <- "none"
else
{
devnplots <- devnplots[grepl("boxplots", devnplots, fixed = TRUE)]
if (length(devnplots) == 0)
devnplots <- "none"
}
plts <- list()
#only do deviations boxplots if requested
if (all(devnplots != "none"))
{
dat <- data
if (!all(c(observed, smoothed) %in% names(dat)))
stop(paste("One or more of", observed, "and", smoothed, "is missing from ",
deparse(substitute(data))))
if (is.null(x.title))
x.title <- x.factor
strip.text.size <- 10
if (is.null(y.titles) && !("none" %in% devnplots))
{
y.titles <- c(paste("Absolute", observed, "deviations", sep = " "),
paste("Relative", observed, "deviations", sep = " "))
names(y.titles ) <- c("absolute.boxplots", "relative.boxplots")
y.titles <- y.titles[c("absolute.boxplots", "relative.boxplots") %in% devnplots]
}
if (length(y.titles) != length(devnplots))
stop("y.titles does not have a title for each plot in deviations.plot")
names(y.titles) <- devnplots
ggfacet <- list()
#Set up facet if have any
if (all(facet.x != ".") | all(facet.y != "."))
{
facet.form <- facet.char2formula(facet.x,facet.y)
if (is.null(facet.labeller))
ggfacet <- list(facet_grid(facet.form, scales = facet.scales))
else
ggfacet <- list(facet_grid(facet.form, labeller = facet.labeller, scales = facet.scales))
ggfacet <- c(ggfacet,
list(theme(strip.text = element_text(size=strip.text.size, face="bold"))))
}
plotDevnBox <- function(dat, y.ytitle, ggplotFuncs = NULL, printPlot = TRUE)
{
plt <- ggplot(data = dat,
aes(x = .data[[!!x.factor]], y = .data[["deviations"]]), ...) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "grey60", linewidth = 0.5),
panel.grid.minor = element_line(colour = "grey80", linewidth = 0.5),
axis.title = element_text(face="bold"),
axis.text.x = element_text(angle = angle.x)) +
geom_boxplot() +
geom_hline(yintercept=0, colour="blue") +
ylab(y.ytitle) + xlab(x.title) +
ggfacet
if (!is.null(ggplotFuncs))
{
for(f in ggplotFuncs)
plt <- plt + f
}
if (printPlot)
print(plt)
return(plt)
}
if ("absolute.boxplots" %in% devnplots)
{
dat$deviations <- dat[[observed]] - dat[[smoothed]]
plts[["absolute"]] <- plotDevnBox(dat, y.ytitle = y.titles["absolute.boxplots"],
ggplotFuncs = ggplotFuncs, printPlot = printPlot)
}
if ("relative.boxplots" %in% devnplots)
{
dat$deviations <- (dat[[observed]] - dat[[smoothed]])/dat[[smoothed]]
plts[["relative"]] <- plotDevnBox(dat, y.ytitle = y.titles["relative.boxplots"],
ggplotFuncs = ggplotFuncs, printPlot = printPlot)
}
}
invisible(plts)
}
"plotSmoothsDevnBoxplots" <- function(data, response, response.smoothed = NULL,
individuals = "Snapshot.ID.Tag", times = "DAP",
trait.types = c("response", "AGR", "RGR"),
which.plots = "absolute.boxplots",
x.title = NULL, y.titles = NULL,
devnboxes.plot.args =
args4devnboxes_plot(plots.by = NULL,
facet.x = ".", facet.y = "."),
printPlot = TRUE, ...)
{
devnboxes.plot.args <- devnboxes.plot.args
inargs <- list(...)
checkEllipsisArgs(c("plotSmoothsDevnBoxplots","plotDeviationsBoxes"), inargs)
options <- c("none", "absolute.boxplots", "relative.boxplots")
plots <- options[unlist(lapply(which.plots, check.arg.values, options=options))]
if ("none" %in% plots && length(plots) > 1)
plots <- "none"
plts <- med.devn.dat <- NULL
#Check that plots are wanted
if (!("none" %in% plots) && !is.allnull(devnboxes.plot.args))
{
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
#Get the options for the deviations boxplots options from the list
plots.by.box <- devnboxes.plot.args$plots.by
facet.x.box <- devnboxes.plot.args$facet.x
facet.y.box <- devnboxes.plot.args$facet.y
collapse.facets.x.box <- devnboxes.plot.args$collapse.facets.x
collapse.facets.y.box <- devnboxes.plot.args$collapse.facets.y
facet.labeller <- devnboxes.plot.args$facet.labeller
scales.box <- devnboxes.plot.args$scales
angle.x.box <- devnboxes.plot.args$angle.x
ggplotFuncsDevnBoxes <- devnboxes.plot.args$ggplotFuncs
#Checking of the arguments that control the plots layout for boxplots
checkLayoutArgs(data = NULL, plots.by.box, plts.group = NULL, facet.x.box, facet.y.box)
plts.by <- plots.by.box
#Check have a valid smooths.frame
validsmoothsframe <- validSmoothsFrame(data)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
checkPlotsArgs(data, plts.by = plts.by, facet.x = facet.x.box, facet.y = facet.y.box)
options <- c("none", "absolute.boxplots", "relative.boxplots")
plots <- options[unlist(lapply(which.plots, check.arg.values, options=options))]
if ("none" %in% plots & length(plots) > 1)
plots <- "none"
if (is.null(x.title))
x.title <- times
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
response.smooth <- response.smoothed
#Check that responses, response.smoothed, individuals and times are in data
checkNamesInData(c(response, response.smoothed, individuals, times), data = data)
addRates <- function(traits, response, sep = ".")
{
unlist(lapply(traits,
function(trait, response)
{
if (!("response" %in% trait))
response <- paste(response, trait, sep = sep)
return(response)
}, response = response))
}
kresp <- addRates(traits, response = response)
if (!all(kresp %in% names(data)))
stop("The following traits are not in the smooths.frame: ",
paste0(kresp[!(kresp %in% names(data))], collapse = ", "),
"; perhaps, trait.types needs to be set differently")
kresp.sm <- addRates(traits, response = response.smoothed)
names(kresp.sm) <- kresp
if (is.null(y.titles))
{
y.titles <- addRates(traits, response = response, sep = " ")
names(y.titles) <- kresp
} else
{
if (length(y.titles) != length(kresp))
stop("y.titles should be the same length as trait.types")
else
names(y.titles) <- kresp
}
data[times] <- convertTimes2numeric(data[[times]])
times.factor <- ".Time.fac"
data[times.factor] <- data[times]
data[times.factor] <- with(data, eval(parse(text =times)))
data[times.factor] <- factor(unlist(data[times.factor]),
labels = unique(data[times.factor])[order(unique(data[[times.factor]])),])
#Determine whether there are any smooth.cols on the facets - if not must be in plots.by.box
smoothing.facets <- length(intersect(union(facet.x.box, facet.y.box), smooth.cols)) != 0
#Set up the facets
modfacet <- setupFacet(data = data, facet = facet.x.box, collapse.facets = collapse.facets.x.box,
combined.name = "Combined.x", smooth.cols = smooth.cols)
xfacet <- modfacet$newfacet
data <- modfacet$data
modfacet <- setupFacet(data = data, facet = facet.y.box, collapse.facets = collapse.facets.y.box,
combined.name = "Combined.y", smooth.cols = smooth.cols)
yfacet <- modfacet$newfacet
data <- modfacet$data
#Do the plots
plts <- list()
for (k in kresp)
{
plts[[k]] <- list()
if (is.allnull(plts.by)) #a single plot only
levs.by <- "all"
else
{
data$plots.by.box <- fac.mixcombine(data, plts.by, smooth.cols = smooth.cols)
levs.by <- levels(data$plots.by.box)
}
#Loop over plots.by.box
for (by in levs.by)
{
if (any(c("absolute.boxplots", "relative.boxplots") %in% plots))
{
y.titles.devn <- c(paste("Absolute", k, "deviations", sep = " "),
paste("Relative", k, "deviations", sep = " "))
names(y.titles.devn) <- c("absolute.boxplots", "relative.boxplots")
y.titles.devn <- y.titles.devn[c("absolute.boxplots", "relative.boxplots") %in% plots]
#Plot deviation plots for current plots.by.box
if (is.allnull(plts.by))
tmp1 <- data
else
{
tmp1 <- data[data$plots.by.box==by,]
ggplotFuncsDevnBoxes <- c(ggplotFuncsDevnBoxes, list(ggtitle(paste0("Plot for ", by))))
}
plt <- plotDeviationsBoxes(data = tmp1, x.factor = times.factor,
observed = k, smoothed = kresp.sm[k],
deviations.plots = plots,
x.title = x.title, y.titles = y.titles.devn,
facet.x=xfacet, facet.y=facet.y.box,
facet.labeller = facet.labeller,
facet.scales = scales.box,
angle.x = angle.x.box,
ggplotFuncs = ggplotFuncsDevnBoxes,
printPlot = printPlot)
plts[[k]][["absolute"]][[by]] <- plt[["absolute"]]
plts[[k]][["relative"]][[by]] <- plt[["relative"]]
}
}
}
}
invisible(plts)
}
"plotSmoothsMedianDevns" <- function(data, response, response.smoothed = NULL,
individuals = "Snapshot.ID.Tag", times = "DAP",
trait.types = c("response", "AGR", "RGR"),
x.title = NULL, y.titles = NULL,
meddevn.plot.args =
args4meddevn_plot(plots.by = NULL, plots.group = NULL,
facet.x = ".", facet.y = ".",
propn.note = TRUE,
propn.types = c(0.1, 0.5, 0.75)),
printPlot = TRUE, ...)
{
meddevn.plot.args <- meddevn.plot.args
inargs <- list(...)
checkEllipsisArgs("plotSmoothsMedianDevns", inargs)
plts <- med.devn.dat <- NULL
#Check that plots are wanted
if (!is.allnull(meddevn.plot.args))
{
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
#Check that responses, response.smoothed, individuals and times are in data
checkNamesInData(c(response, response.smoothed, individuals, times), data = data)
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
#Get the options for the median deviations plots options from the list
plots.by.med <- meddevn.plot.args$plots.by
plots.group.med <- meddevn.plot.args$plots.group
facet.x.med <- meddevn.plot.args$facet.x
facet.y.med <- meddevn.plot.args$facet.y
facet.labeller = meddevn.plot.args$facet.labeller
facet.scales.med <- meddevn.plot.args$facet.scales
breaks.spacing.x <- meddevn.plot.args$breaks.spacing.x
angle.x <- meddevn.plot.args$angle.x
colour.values.med <- meddevn.plot.args$colour.values
shape.values.med <- meddevn.plot.args$shape.values
alpha.med <- meddevn.plot.args$alpha
propn.note.med <- meddevn.plot.args$propn.note
propn.types.med <- meddevn.plot.args$propn.types
ggplotFuncsMedDevn <- meddevn.plot.args$ggplotFuncs
plts.by <- plots.by.med
plts.group <- plots.group.med
#Check have a valid smooths.frame
validsmoothsframe <- validSmoothsFrame(data)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
checkPlotsArgs(data, plts.by, plts.group, facet.x = facet.x.med, facet.y = facet.y.med)
strip.text.size <- 10
dat <- data
#Form data.frame with just columns needed
#Create a factor Times that has the plotted values of x for its labels
if (is.null(x.title))
x.title <- times
data[times] <- convertTimes2numeric(data[[times]])
id.cols <- c(individuals, times) #, colour.column)
times.factor <- ".Time.fac"
dat[times.factor] <- dat[times]
dat[times.factor] <- with(dat, eval(parse(text =times)))
dat[times.factor] <- factor(unlist(dat[times.factor]),
labels = unique(dat[times.factor])[order(unique(dat[[times.factor]])),])
fac.group <- NULL
if (!is.allnull(plts.group))
{
dat$SmoothParams <- fac.mixcombine(dat, plts.group, smooth.cols = smooth.cols)
fac.group <- "SmoothParams"
id.cols <- c(fac.group, id.cols)
}
#Determine xfacet and fac.by
xfacet <- facet.x.med
if (!is.allnull(plts.by))
{
dat$fac.by <- fac.mixcombine(dat, plts.by, smooth.cols = smooth.cols)
dat$fac.by <- factor(dat$fac.by)
id.cols <- c("fac.by", id.cols)
}
if (all(xfacet != "."))
id.cols <- c(id.cols, fac.getinFormula(xfacet))
if (all(facet.y.med != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.med))
#Set up facet
ggfacet <- list()
facet.cols <- NULL
if (all(xfacet != ".") || all(facet.y.med != "."))
{
facet.form <- facet.char2formula(xfacet, facet.y.med)
if (is.null(facet.labeller))
ggfacet <- list(facet_grid(facet.form, scales = facet.scales.med))
else
ggfacet <- list(facet_grid(facet.form, scales = facet.scales.med, labeller = facet.labeller))
facet.cols <- c(xfacet, facet.y.med)
facet.cols <- facet.cols[facet.cols != "."]
}
#Form raw and smoothed trait names
addRates <- function(traits, response, sep = ".")
{
unlist(lapply(traits,
function(trait, response)
{
if (!("response" %in% trait))
response <- paste(response, trait, sep = sep)
return(response)
}, response = response))
}
kresp <- addRates(traits, response = response)
kresp.sm <- addRates(traits, response = response.smoothed)
id.cols <- c(id.cols, kresp, kresp.sm)
if (!all(id.cols %in% names(dat)))
stop(paste("Do not have the following required columns in data: ",
paste(id.cols[!(id.cols %in% names(dat))],collapse=", "), "\n", sep=""))
kresp.devn <- paste(kresp, "devn", sep = ".")
names(kresp.sm) <- kresp
names(kresp.devn) <- kresp
if (is.null(y.titles))
{
y.titles <- paste("median", addRates(traits, response = response, sep = " "), "deviations")
names(y.titles) <- kresp
} else
{
if (length(y.titles) != length(kresp))
stop("y.titles should be the same length as trait.types")
else
names(y.titles) <- kresp
}
#Calculate the deviations
dat[kresp.devn] <- dat[kresp] - dat[kresp.sm]
#Calculate the median deviations
split.facs <- c(if (!is.allnull(plts.by)) "fac.by",
facet.cols, fac.group, times.factor) #NULL objects will be ignored
dat <- dat[c(split.facs, setdiff(names(dat), split.facs))]
dat <- dat[do.call(order, dat),]
tmp <- dat
tmp$split.fac <- dae::fac.combine(as.list(dat[split.facs]), combine.levels = TRUE)
dat.split <- split(tmp, f = tmp$split.fac)
med.devn.dat <- lapply(dat.split,
function(data, kresp.devn)
{
med <- unlist(lapply(data[kresp.devn], median, na.rm = TRUE))
krow <- cbind(data[1,split.facs], rbind(med))
return(krow)
},
kresp.devn = kresp.devn)
med.devn.dat <- as.data.frame(do.call(rbind, med.devn.dat))
med.devn.dat[times] <- dae::as.numfac(unlist(med.devn.dat[times.factor]))
#Remove the times.factor
med.devn.dat <- med.devn.dat[,-match(times.factor, names(med.devn.dat))]
#Remove any missing values
med.devn.dat <- med.devn.dat[which(!is.na(med.devn.dat[kresp.devn[1]])), ]
#Calculate the median responses
if (propn.note.med && !is.null(propn.types.med))
{
if (length(propn.types.med) != length(kresp))
stop("Length of propn.types.med is not the same as the number of trait.types")
names(propn.types.med) <- kresp
med.resp.dat <- lapply(dat.split,
function(data, kresp)
{
med <- unlist(lapply(data[kresp], median, na.rm = TRUE))
krow <- cbind(data[1,split.facs], rbind(med))
return(krow)
},
kresp = kresp)
med.resp.dat <- as.data.frame(do.call(rbind, med.resp.dat))
med.resp.dat <- rbind(med.resp.dat, med.resp.dat)
med.resp.dat <- cbind(sign = rep(c(1,-1), each = nrow(med.resp.dat)/2),
med.resp.dat)
med.resp.dat[kresp] <- as.data.frame(mapply(function(var, propn)
{
var <- propn * rep(c(1, -1), each = length(var)/2) * var
},
med.resp.dat[kresp], propn.types.med))
med.resp.dat[times] <- dae::as.numfac(unlist(med.resp.dat[times.factor]))
#Remove the times.factor
med.resp.dat <- med.resp.dat[,-match(times.factor, names(med.resp.dat))]
#Remove any missing values
med.resp.dat <- med.resp.dat[which(!is.na(med.resp.dat[kresp[1]])), ]
}
#Plot the median deviations for each trait
if (is.null(shape.values.med))
shape.values.med <- c(21:24,7,9,10,11,3,4)
plts <- list()
for (k in kresp)
{
plts[[k]] <- list()
if (is.allnull(plts.by))
levs.by <- "all"
else
levs.by <- levels(med.devn.dat$fac.by)
for (p in levs.by)
{
if (is.allnull(plts.by))
tmp <- med.devn.dat
else
tmp <- med.devn.dat[med.devn.dat$fac.by == p,]
if ("Method" %in% names(tmp))
tmp$Method <- with(tmp, dae::fac.recast(Method,
newlevels = substring(levels(Method),1,3)))
plts[[k]][[p]] <- ggplot(tmp, aes(x = .data[[!!times]], .data[[!!kresp.devn[k]]]),
...) +
ggfacet +
geom_hline(yintercept=0, linetype="solid", linewidth=0.5, colour = "maroon", alpha=0.7) +
setScaleTime(tmp[[times]], breaks.spacing.x = breaks.spacing.x) +
xlab(x.title) + ylab(y.titles[k]) + theme_bw() +
theme(strip.text = element_text(size=strip.text.size, face="bold"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(angle = angle.x),
panel.grid.major = element_line(colour = "grey60", linewidth = 0.5),
panel.grid.minor = element_line(colour = "grey80", linewidth = 0.5))
if (is.null(fac.group))
{
if (is.allnull(colour.values.med))
{
plts[[k]][[p]] <- plts[[k]][[p]] + geom_line (linewidth=0.4, alpha=alpha.med)
if (is.allnull(shape.values.med))
plts[[k]][[p]] <- plts[[k]][[p]] + geom_point(alpha=alpha.med, size=1.5)
else
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_point(shape=shape.values.med[1], alpha=alpha.med, size=1.5)
}
else
{
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_line (colour=colour.values.med[1], linewidth=0.4, alpha=alpha.med)
if (is.allnull(shape.values.med))
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_point(colour=colour.values.med[1],
fill=colour.values.med[1], alpha=alpha.med, size=1.5)
else
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_point(colour=colour.values.med[1], shape=shape.values.med[1],
fill=colour.values.med[1], alpha=alpha.med, size=1.5)
}
}
else
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_line (aes(colour = .data[[!!fac.group]]), linewidth=0.4, alpha=alpha.med) +
geom_point(aes(colour = .data[[!!fac.group]],
shape = .data[[!!fac.group]],
fill = .data[[!!fac.group]]),
alpha=alpha.med, size=1.5)
if (!(is.null(colour.values.med)))
plts[[k]][[p]] <- plts[[k]][[p]] + scale_colour_manual(values = colour.values.med)
if (!(is.null(shape.values.med)))
plts[[k]][[p]] <- plts[[k]][[p]] + scale_shape_manual(values = shape.values.med)
else
plts[[k]][[p]] <- plts[[k]][[p]] + scale_shape_manual(values = c(21:24,7,9,10,11))
if (!is.allnull(plts.group) && fac.group == "SmoothParams")
plts[[k]][[p]] <- plts[[k]][[p]] + guides(shape=guide_legend(title = "Smoothing\nparameters"),
fill=guide_legend(title = "Smoothing\nparameters"),
colour=guide_legend(title = "Smoothing\nparameters"))
if (!is.allnull(plts.by))
plts[[k]][[p]] <- plts[[k]][[p]] + ggtitle(paste("Plot for",p))
#Plot an envelope of the response median
if (propn.note.med && !is.null(propn.types.med))
{
if (is.allnull(plts.by))
med.resp.tmp <- med.resp.dat
else
med.resp.tmp <- med.resp.dat[med.resp.dat$fac.by == p, ]
if ("Method" %in% names(med.resp.tmp))
med.resp.tmp$Method <- with(med.resp.tmp,
dae::fac.recast(Method,
newlevels = substring(levels(Method),1,3)))
#Construct message to be plotted
if (propn.note.med)
{
xmin <- min(med.resp.tmp[times], na.rm = TRUE)
xrange <- max(med.resp.tmp[times], na.rm = TRUE) - xmin #not used at present
ymin <- min(med.resp.tmp[k], na.rm = TRUE)
envel <- data.frame(rep(xmin, 2),
c(ymin+2.75, ymin))
names(envel) <- c(times, kresp.devn[k])
ncol.x <- 1
if (all(facet.x.med != "."))
{
lastlevs <- lapply(facet.x.med,
function(fac, med.resp.tmp)
{
lastlev <- levels(med.resp.tmp[[fac]])
lastlev <- rep(factor(lastlev[length(lastlev)], levels = lastlev), 2)
}, med.resp.tmp = med.resp.tmp)
names(lastlevs) <- facet.x.med
envel <- cbind(envel, lastlevs)
}
if (all(facet.y.med != "."))
{
lastlevs <- lapply(facet.y.med,
function(fac, med.resp.tmp)
{
lastlev <- levels(med.resp.tmp[[fac]])
lastlev <- rep(factor(lastlev[length(lastlev)], levels = lastlev), 2)
}, med.resp.tmp = med.resp.tmp)
names(lastlevs) <- facet.y.med
envel <- cbind(envel, lastlevs)
}
if (ncol.x >3)
envel$lab <- c("Envelope:",
paste(propn.types.med[k],"of response median"))
else
{
envel <- envel[2,]
envel$lab <- paste("Envelope:", propn.types.med[k],"of response median")
}
}
#Plot the envelope
plts[[k]][[p]] <- plts[[k]][[p]] + geom_line(data = med.resp.tmp,
aes(y = .data[[!!k]],
group = .data[["sign"]]),
linetype="dashed")
if (propn.note.med)
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_text(data = envel,
mapping = aes(x = .data[[!!times]], y = .data[[!!kresp.devn[k]]],
label = .data[["lab"]]),
hjust = 0, vjust=-Inf,
fontface = "plain", size = 3)
}
if (!is.null(ggplotFuncsMedDevn))
{
for(f in ggplotFuncsMedDevn)
plts[[k]][[p]] <- plts[[k]][[p]] + f
}
if (printPlot)
print(plts[[k]][[p]])
}
}
}
invisible(list(plots = plts, med.devn.dat = med.devn.dat))
}
"plotSmoothsComparison" <- function(data, response, response.smoothed = NULL,
individuals = "Snapshot.ID.Tag", times = "DAP",
trait.types = c("response", "AGR", "RGR"),
x.title = NULL, y.titles = NULL,
profile.plot.args = args4profile_plot(plots.by = NULL,
facet.x = ".", facet.y = ".",
include.raw = "no"),
printPlot = TRUE, ...)
{
profile.plot.args <- profile.plot.args
inargs <- list(...)
checkEllipsisArgs(c("plotSmoothsComparison","plotProfiles"), inargs)
plts <- NULL
#Check that plots are wanted
if (!is.allnull(profile.plot.args))
{
#Find out if any plotProfiles arguments that args4profile_plot handles have been supplied in '...'
if (length(inargs))
{
usedProfile.args <- formalArgs(args4profile_plot)
doubleargs <- intersect(names(inargs), usedProfile.args)
if (length(doubleargs))
stop("the 'plotProfiles' arguments ",paste0(doubleargs, collapse = ", "),
" conflict with 'args4profile_plot' arguments")
}
pltProfile.args <- NULL
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
#Get the options for the profile plots options from the list
plots.by.pf <- profile.plot.args$plots.by
facet.x.pf <- profile.plot.args$facet.x
facet.y.pf <- profile.plot.args$facet.y
include.raw.pf <- profile.plot.args$include.raw
collapse.facets.x.pf <- profile.plot.args$collapse.facets.x
collapse.facets.y.pf <- profile.plot.args$collapse.facets.y
facet.labeller <- profile.plot.args$facet.labeller
facet.scales.pf <- profile.plot.args$facet.scales
breaks.spacing.x <- profile.plot.args$breaks.spacing.x
angle.x <- profile.plot.args$angle.x
colour.pf <- profile.plot.args$colour
colour.column.pf <- profile.plot.args$colour.column
colour.values.pf <- profile.plot.args$colour.values
alpha.pf <- profile.plot.args$alpha
addMediansWhiskers.pf <- profile.plot.args$addMediansWhiskers
ggplotFuncsProfile <- profile.plot.args$ggplotFuncs
#Check include.raw.pf value
incl.raw.opt <- c("no", "alone", "facet.x", "facet.y")
incl.raw.opt <- incl.raw.opt[check.arg.values(include.raw.pf, options=incl.raw.opt)]
incl.raw <- incl.raw.opt != "no"
#Checking of the arguments that control the plots layout
checkLayoutArgs(data = data, plots.by.pf, plts.group = NULL, facet.x.pf, facet.y.pf)
plts.by <- plots.by.pf
#Check have a valid smooths.frame
validsmoothsframe <- validSmoothsFrame(data)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
checkPlotsArgs(data, plts.by = plts.by, facet.x = facet.x.pf, facet.y = facet.y.pf)
if (is.null(x.title))
x.title <- times
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
response.smooth <- response.smoothed
#Check that responses, response.smoothed, individuals and times are in data
checkNamesInData(c(response, response.smoothed, individuals, times), data = data)
addRates <- function(traits, response, sep = ".")
{
unlist(lapply(traits,
function(trait, response)
{
if (!("response" %in% trait))
response <- paste(response, trait, sep = sep)
return(response)
}, response = response))
}
kresp <- addRates(traits, response = response)
if (!all(kresp %in% names(data)))
stop("The following traits are not in the smooths.frame: ",
paste0(kresp[!(kresp %in% names(data))], collapse = ", "),
"; perhaps, trait.types needs to be set differently")
kresp.sm <- addRates(traits, response = response.smoothed)
names(kresp.sm) <- kresp
if (is.null(y.titles))
{
y.titles <- addRates(traits, response = response, sep = " ")
names(y.titles) <- kresp
} else
{
if (length(y.titles) != length(kresp))
stop("y.titles should be the same length as trait.types")
else
names(y.titles) <- kresp
}
data[times] <- convertTimes2numeric(data[[times]])
times.factor <- ".Time.fac"
data[times.factor] <- data[times]
data[times.factor] <- with(data, eval(parse(text =times)))
data[times.factor] <- factor(unlist(data[times.factor]),
labels = unique(data[times.factor])[order(unique(data[[times.factor]])),])
#Determine whether there are any smooth.cols on the facets - if not must be in plots.by.pf
smoothing.facets <- length(intersect(union(facet.x.pf, facet.y.pf), smooth.cols)) != 0
if (incl.raw.opt %in% c("facet.x", "facet.y") && all(c(facet.x.pf, facet.y.pf) == "."))
stop(paste0("The argument incl.raw is set to ", include.raw.pf,
", but ", include.raw.pf, " has not been set to include a variable"))
#Set up the facets
modfacet <- setupFacet(data = data, facet = facet.x.pf, collapse.facets = collapse.facets.x.pf,
combined.name = "Combined.x", smooth.cols = smooth.cols)
xfacet <- modfacet$newfacet
data <- modfacet$data
modfacet <- setupFacet(data = data, facet = facet.y.pf, collapse.facets = collapse.facets.y.pf,
combined.name = "Combined.y", smooth.cols = smooth.cols)
yfacet <- modfacet$newfacet
data <- modfacet$data
#Do the plots
plts <- list()
for (k in kresp)
{
plts[[k]] <- list()
if (incl.raw.opt == "alone")
{
#Get a single instance of the unsmoothed data
tmp <- split(data, data[smooth.cols])[[1]]
#Removing smoothing factors from facets
xfacet.tmp <- setdiff(xfacet, smooth.cols)
if (length(xfacet.tmp) == 0)
xfacet.tmp <- "."
yfacet.tmp <- setdiff(yfacet, smooth.cols)
if (length(yfacet.tmp) == 0)
yfacet.tmp <- "."
plts[[k]][["profiles"]][["Unsmoothed"]] <-
do.call(plotProfiles,
c(list(data = tmp, times = times, response = k,
individuals = individuals,
facet.x=xfacet.tmp, facet.y=yfacet,
labeller = facet.labeller, scales = facet.scales.pf,
breaks.spacing.x = breaks.spacing.x,
angle.x = angle.x,
colour = colour.pf,
colour.column = colour.column.pf,
colour.values = colour.values.pf,
alpha = alpha.pf,
title="Plot of unsmoothed response",
x.title = x.title, y.title = y.titles[k],
addMediansWhiskers = addMediansWhiskers.pf,
printPlot=FALSE,
ggplotFuncs = ggplotFuncsProfile),
pltProfile.args))
if (printPlot)
print(plts[[k]][["profiles"]][["Unsmoothed"]])
}
if (is.allnull(plts.by)) #all profiles in a single plot
levs.by <- "all"
else
{
data$plots.by.pf <- fac.mixcombine(data, plts.by, smooth.cols = smooth.cols)
data$plots.by.pf <- factor(data$plots.by.pf)
levs.by <- levels(data$plots.by.pf)
}
#Loop over plots.by.pf
for (by in levs.by)
{
{
if (is.allnull(plts.by))
{
title <- NULL
tmp1 <- data
} else
{
title <- paste0("Plot for ", by)
tmp1 <- data[data$plots.by.pf==by,]
if ("Combined.x" %in% names(tmp1))
tmp1["Combined.x"] <- factor(tmp1[["Combined.x"]])
if ("Combined.y" %in% names(tmp1))
tmp1["Combined.y"] <- factor(tmp1[["Combined.y"]])
}
xfacet.tmp <- xfacet
yfacet.tmp <- yfacet
if (incl.raw.opt %in% c("facet.x", "facet.y"))
{
if (incl.raw.opt == "facet.x") comb.name <- xfacet else comb.name <- yfacet
if (any(comb.name == "."))
{
comb.name <- ".Response"
tmp1[comb.name] <- factor("Smoothed")
if (incl.raw.opt == "facet.x") xfacet.tmp <- comb.name else yfacet.tmp <- comb.name
}
comb.name <- comb.name[length(comb.name)]
tmp2 <- tmp1
tmp2[kresp.sm[k]] <- tmp2[k]
tmp2[comb.name] <- "Raw"
levs <- c("Raw", levels(factor(tmp1[[comb.name]])))
tmp1 <- rbind(tmp2,tmp1)
tmp1[comb.name] <- factor(tmp1[[comb.name]], levels = levs)
}
plts[[k]][["profiles"]][[by]] <-
do.call(plotProfiles,
c(list(data = tmp1, times = times,
response = kresp.sm[k],
individuals = individuals,
facet.x = xfacet.tmp, facet.y = yfacet.tmp,
labeller = facet.labeller,
scales = facet.scales.pf,
breaks.spacing.x = breaks.spacing.x,
angle.x = angle.x,
colour = colour.pf,
colour.column = colour.column.pf,
colour.values = colour.values.pf,
alpha = alpha.pf,
title = title,
x.title = x.title, y.title = y.titles[k],
addMediansWhiskers = addMediansWhiskers.pf,
printPlot=FALSE,
ggplotFuncs = ggplotFuncsProfile),
pltProfile.args))
if (printPlot)
print(plts[[k]][["profiles"]][[by]])
}
}
}
}
invisible(plts)
}
#Function to fit splines, including possible boundary correction from Huang (2001)
ncsSpline <- function(vars, correctBoundaries = FALSE,
df, lambda, cv = FALSE, ...)
{
if (ncol(vars) != 2)
stop("Must supply a two-column matrix or data.frame")
if (!correctBoundaries)
{
if (missing(df))
{
if (missing(lambda))
fity <- smooth.spline(vars, all.knots=TRUE, ...)
else
fity <- smooth.spline(vars, all.knots=TRUE, lambda = lambda, ...)
} else
{
if (missing(lambda))
fity <- smooth.spline(vars, all.knots=TRUE, df=df, ...)
else
stop("Only one of df and lambda can be specified")
}
fit.spline <- list(x = fity$x,
y = fity$y,
lev = fity$lev,
lambda = fity$lambda,
df = fity$df,
uncorrected.fit = fity)
} else
{
nval <- nrow(vars)
W <- matrix(NA, nrow = nval, ncol = 4)
W2 <- matrix(NA, nrow = nval, ncol = 4)
W3 <- matrix(NA, nrow = nval, ncol = 4)
x <- vars[,1]
y <- vars[,2]
# construct the four polynomials
W[,1] <- x^2/2-x^4/4+x^5/10
W[,2] <- x^3/6-x^4/6+x^5/20
W[,3] <- x^4/4-x^5/10
W[,4] <- -x^4/12+x^5/20
if (missing(df))
{
if (!missing(lambda))
stop("lambda must not be set for correctBoundaries = TRUE")
lam <- vector(mode = "numeric", length = nval)
rss <- vector(mode = "numeric", length = nval)
tr <- vector(mode = "numeric", length = nval)
gcv <- vector(mode = "numeric", length = nval)
# use GCV to search for best lambda
for(i in 1:nval)
{
u <- -7+7.0*(i-1)/(nval-1)
lam[i] <- 10^u
slam <- lam[i]
# get regular fit for y and four polynomials
fity <- smooth.spline(x,y,all.knots=TRUE,spar=slam, ...)
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,spar=slam, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,spar=slam, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,spar=slam, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,spar=slam, ...)$y
#Apply boundary correction to the fitted spline
W2 <- W-W2
h <- solve(t(W2)%*%W2,t(W2)%*%(y-fity$y))
# get the newfit, rss and calculate the trace of new S
newfit <- fity$y+W2%*%h
rss[i] <- sum((newfit-y)^2)
W3[,1] <- smooth.spline(x,W2[,1],all.knots=TRUE,spar=slam, ...)$y
W3[,2] <- smooth.spline(x,W2[,2],all.knots=TRUE,spar=slam, ...)$y
W3[,3] <- smooth.spline(x,W2[,3],all.knots=TRUE,spar=slam, ...)$y
W3[,4] <- smooth.spline(x,W2[,4],all.knots=TRUE,spar=slam, ...)$y
W3 <- W2-W3
K <- solve(t(W2)%*%W2,t(W3))
tr[i] <- sum(fity$lev)+sum(diag(K%*%W2))
gcv[i] <- nval*rss[i]/(nval-tr[i])^2
}
# get the optimal lambda and apply to y and the four polynomials
lam.opt<- lam[order(gcv)[1]]
slam <- lam.opt
fity <- smooth.spline(x,y,all.knots=TRUE,spar=slam)
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,spar=slam, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,spar=slam, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,spar=slam, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,spar=slam, ...)$y
} else #df is specified
{
# get regular fit for y and four polynomials for specified df
fity <- smooth.spline(x,y,all.knots=TRUE,df=df, ...)
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,df=df, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,df=df, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,df=df, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,df=df, ...)$y
# calculate the trace of new S
W3[,1] <- smooth.spline(x,W2[,1],all.knots=TRUE,df=df, ...)$y
W3[,2] <- smooth.spline(x,W2[,2],all.knots=TRUE,df=df, ...)$y
W3[,3] <- smooth.spline(x,W2[,3],all.knots=TRUE,df=df, ...)$y
W3[,4] <- smooth.spline(x,W2[,4],all.knots=TRUE,df=df, ...)$y
W3 <- W2-W3
K <- solve(t(W2)%*%W2,t(W3))
}
#Apply boundary correction to the fitted spline
W2 <- W-W2
h <- solve(t(W2)%*%W2,t(W2)%*%(y-fity$y))
newy <- as.vector(fity$y+W2%*%h)
# get the newfit leverage values of new S
lev <- as.vector(fity$lev+diag(W2%*%K))
tr <- sum(lev)
rss <- sum((y - newy)^2)
lambda <- nval*rss/(nval-tr)^2
# Set up list to return
fit.spline <- list(x = fity$x,
y = newy,
lev = lev,
lambda = lambda,
df = fity$df,
uncorrected.fit = fity)
if (!missing(df))
fit.spline$df <- df
}
class(fit.spline) <- "ncsSpline"
return(fit.spline)
}
predict.ncsSpline <- function(object, x, correctBoundaries = FALSE,
df, cv = FALSE, ...)
{
if (!inherits(object, what = "ncsSpline"))
stop("Must supply a an object of class ncsSpline")
fit <- predict(object$uncorrected.fit, x = x)
#Correct boundaries
if (correctBoundaries)
{
nval <- length(x)
W <- matrix(NA, nrow = nval, ncol = 4)
W2 <- matrix(NA, nrow = nval, ncol = 4)
vars <- data.frame(x = object$uncorrected.fit$x,
yin = object$uncorrected.fit$yin)
vars <- merge(as.data.frame(fit), vars, all.x = TRUE)
vars$yin[is.na(vars$yin)] <- vars$y[is.na(vars$yin)]
vars <- vars[order(vars$x), ]
x <- vars$x
# construct the four polynomials
W[,1] <- x^2/2-x^4/4+x^5/10
W[,2] <- x^3/6-x^4/6+x^5/20
W[,3] <- x^4/4-x^5/10
W[,4] <- -x^4/12+x^5/20
if (missing(df))
{
slam <- object$lambda
# get regular fit for y and four polynomials
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,spar=slam, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,spar=slam, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,spar=slam, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,spar=slam, ...)$y
} else #df is specified
{
# get regular fit for y and four polynomials for specified df
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,df=df, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,df=df, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,df=df, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,df=df, ...)$y
}
#Apply boundary correction to the fitted spline
W2 <- W-W2
h <- solve(t(W2)%*%W2,t(W2)%*%(vars$yin-vars$y))
fit <- list(x = vars$x,
y = as.vector(vars$y+W2%*%h))
}
return(fit)
}
#Functions to fit P-splines using JOPS
pSpline <- function(vars, npspline.segments, lambda = NULL, ...)
{
if (ncol(vars) != 2)
stop("Must supply a two-column matrix or data.frame")
fity <- JOPS::psNormal(x = vars[[1]], y = vars[[2]], nseg = npspline.segments, lambda = lambda,
xgrid = vars[[1]])
fit.spline <- list(x = fity$x,
y = as.vector(fity$muhat),
lev = NULL,
lambda = fity$lambda,
df = fity$effdim,
npspline.segments = npspline.segments,
uncorrected.fit = fity)
class(fit.spline) <- "PSpline"
class(fit.spline$uncorrected.fit) <- "pspfit"
return(fit.spline)
}
predict.pSpline <- function(object, x, npspline.segments, deriv = 0, ...)
{
fit.obj <- object$uncorrected.fit
bdeg <- fit.obj$bdeg
xmin <- min(x, na.rm = TRUE)
xmax <- max(x, na.rm = TRUE)
if (!inherits(object, what = "PSpline"))
stop("Must supply a an object of class PSpline")
if (deriv == 0)
{
preds <- predict(fit.obj, x = x, type = "mu")
fit <- list(x = x, y = preds)
} else #obtain a derivative of order deriv based on Eqn 2.15 from Eilers and Marx (2021) JOPS
{
if ((bdeg - deriv) <= 0)
stop("The degree of the spline (3) is insufficient to compute a derivative of order ", deriv)
alpha <- as.vector(fit.obj$pcoef)
alphaDeriv <- diff(alpha, differences = deriv) / (((xmax - xmin)/fit.obj$nseg)^deriv)
Bderiv <- JOPS::bbase(x, xl = xmin, xr = xmax, nseg = fit.obj$nseg, bdeg = (bdeg - deriv))
fit <- as.vector(Bderiv %*% alphaDeriv)
fit <- list(x = x, y = fit)
}
return(fit)
}
#Function to fit a spline using smooth.spline or JOPS
"smoothSpline" <- function(data, response, response.smoothed = NULL, x,
smoothing.method = "direct",
spline.type = "NCSS", df=NULL, lambda = NULL,
npspline.segments = NULL, correctBoundaries = FALSE,
rates = NULL, suffices.rates = NULL, sep.rates = ".",
extra.derivs = NULL, suffices.extra.derivs=NULL,
na.x.action = "exclude", na.y.action = "trimx", ...)
{
#This table has been made obsolete with the introduction of extra.rate
# Result deriv suffix.deriv AGR RGR
# direct smoothing
# AGR, RGR 1 AGR NULL RGR
# AGR 1 AGR NULL NULL
# RGR 1 NULL NULL RGR
# log-smoothing
# AGR, RGR 1 RGR AGR NULL
# AGR 1 NULL AGR NULL
# RGR 1 RGR NULL NULL
#check input arguments
impArgs <- match.call()
if ("na.rm" %in% names(impArgs))
stop("na.rm has been deprecated; use na.x.action and na.y.action")
if ("smoothing.scale" %in% names(impArgs))
stop("smoothing.scale has been deprecated; use smoothing.method")
#Check that required cols are in data
checkNamesInData(c(response, x), data = data)
smethods <- c("direct", "logarithmic")
smethod <- smethods[check.arg.values(smoothing.method, options=smethods)]
stype <- c("NCSS", "PS")
stype <- stype[check.arg.values(spline.type, options=stype)]
if (is.null(lambda) && stype == "PS")
stop("Must specify lambda for spline.type set to PS")
if (!is.null(df) && !is.null(lambda) && stype == "NCSS")
stop("Only one of df and lambda can be specified for spline.type NCSS")
na.x <- na.y <- c("exclude", "omit", "fail")
na.y <- c(na.y, "allx", "trimx", "ltrimx", "utrimx")
na.act.x <- na.x[check.arg.values(na.x.action, options=na.x)]
na.act.y <- na.y[check.arg.values(na.y.action, options=na.y)]
options <- c("AGR", "PGR", "RGR")
if (is.allnull(rates))
grates <- NULL
else
grates <- options[unlist(lapply(rates, check.arg.values, options=options))]
if (correctBoundaries && !is.allnull(grates))
stop("Unable to correctBoundaries when rates is not NULL")
if (!is.allnull(grates) && "PGR" %in% grates)
stop("PGR is not available when rates are based on derivatives")
if (!is.null(suffices.rates) & length(grates) != length(suffices.rates))
stop("The length of of rates and suffices.rates should be equal")
if (!is.allnull(grates) && !is.null(extra.derivs) && (1 %in% extra.derivs))
stop("when rates is not NULL, 1 should not be included in extra.derivs")
if (is.null(suffices.rates))
suffices.rates <- grates
names(suffices.rates) <- grates
if (!is.null(extra.derivs) & !is.null(suffices.extra.derivs))
if (length(extra.derivs) != length(extra.derivs))
stop("The number of names supplied must equal the number of derivatives specified")
#Determine what is required from spline fitting
if (!is.allnull(grates))
{
derivs = 1
if (smethod == "direct")
{
if (!("AGR" %in% grates))
suffices.derivs <- "_tmp"
else
{
if (is.null(suffices.rates))
suffices.derivs <- "AGR"
else
suffices.derivs <- suffices.rates["AGR"]
}
names(suffices.derivs) <- "AGR"
if ("RGR" %in% grates)
{
extra.rate <- "RGR"
names(extra.rate) <- suffices.rates["RGR"]
}
else
extra.rate <- NULL
} else
{
if (!("RGR" %in% grates))
suffices.derivs <- "_tmp"
else
{
if (is.null(suffices.rates))
suffices.derivs <- "RGR"
else
suffices.derivs <- suffices.rates["RGR"]
}
names(suffices.derivs) <- "RGR"
if ("AGR" %in% grates)
{
extra.rate <- "AGR"
names(extra.rate) <- suffices.rates["AGR"]
}
else
extra.rate <- NULL
}
} else
{
derivs = NULL
suffices.derivs <- NULL
extra.rate <- NULL
}
if (!is.null(extra.derivs))
{
derivs <- unique(c(derivs, extra.derivs))
suffices.derivs <- c(suffices.derivs, suffices.extra.derivs)
names(derivs) <- suffices.derivs
}
tmp <- as.data.frame(data)
#Transform data, if required
if (smethod == "logarithmic")
tmp[[response]] <- log(tmp[[response]])
#Convert any infinite values to missing
if (any(is.infinite(tmp[[response]])))
{
tmp[[response]][is.infinite(tmp[[response]])] <- NA
warning("Some infinite values have been converted to missing")
}
#Set up fit data.frame
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
fit.names <- c(x, response.smoothed)
if (!is.allnull(derivs))
{
if (is.allnull(suffices.derivs))
fit.names <- c(fit.names, paste(response.smoothed,".dv",derivs,sep=""))
else
fit.names <- c(fit.names, paste(response.smoothed, suffices.derivs, sep=sep.rates))
}
#Process missing values
#tmp will have no missing values and is what is used in the fitting
#data retains missing values and is used to obtain the returned data.frame
#x.pred has the x-values for which predictions are required
# - it should include all the x values that are returned by smooth.spline;
# it will not include any x values that are missing, but may include
# x values for which there are missing y values, depending on the settings
# of na.y.action, and these x values will not have been supplied to smooth.spline.
nobs <- nrow(tmp)
if (nobs == 0)
{
warning("A response with no data values supplied")
} else
{
if (na.act.x == "fail")
stop("na.x.action is to set to fail and there are missing x values")
else #remove any observations with missing x values
{
if (na.act.x %in% c("omit", "exclude"))
{
tmp <- tmp[!is.na(tmp[[x]]), ]
if (na.act.x == "omit")
data <- tmp
}
}
x.pred <- tmp[[x]]
if (na.act.y == "fail")
stop("na.y.action is to set to fail and there are missing y values")
else #Are there any missing response values now
{
if (na.act.y %in% c("omit", "exclude"))
{
x.pred <- tmp[!is.na(tmp[[response]]), ][[x]]
} else
{
if (grepl("trimx", na.act.y, fixed = TRUE))
{
tmp <- tmp[order(tmp[[x]]), ]
y.nonmiss <- c(1:nrow(tmp))[!is.na(tmp[[response]])]
x.pred <- tmp[[x]]
if (length(y.nonmiss) > 0)
{
if (na.act.y %in% c("trimx", "ltrimx"))
{
x.pred <- x.pred[y.nonmiss[1]:length(tmp[[x]])]
y.nonmiss <- y.nonmiss - y.nonmiss[1] + 1
}
if (na.act.y %in% c("trimx", "utrimx"))
x.pred <- x.pred[1:y.nonmiss[length(y.nonmiss)]]
y.nonmiss <- diff(y.nonmiss)
if (any(y.nonmiss >= 3))
warning(paste("smoothSpline has found runs of 3 or more contiguous",
"missing y-values - is the current na.y.action setting,",
paste0("`", na.act.y,"', appropriate?")))
} else
x.pred <- NULL
}
}
tmp <- tmp[!is.na(tmp[[response]]), ]
if (na.act.y == "omit")
data <- tmp
}
}
#What are the distinct x values
distinct.xvals <- sort(unique(tmp[[x]]))
tol <- 1e-06 * IQR(distinct.xvals)
distinct.xvals <- remove.repeats(distinct.xvals, tolerance = tol)
if (length(distinct.xvals) < 4)
{
#< 4 distinct values and so all fitted values are set to NA
warning(paste("Need at least 4 distinct x values to fit a spline",
"- all fitted values set to NA", sep = " "))
#Set up fit data.frame
if (is.null(response.smoothed))
response.smoothed <- paste(response,"smooth",sep=".")
fit.names <- c(x, response.smoothed)
if (!is.allnull(derivs))
{
if (is.allnull(suffices.derivs))
fit.names <- c(fit.names, paste(response.smoothed,".dv",derivs,sep=""))
else
fit.names <- c(fit.names, paste(response.smoothed, suffices.derivs, sep=sep.rates))
}
#Add extra,rate if required
if (!is.null(extra.rate))
fit.names <- c(fit.names, paste(response.smoothed, names(extra.rate), sep=sep.rates))
fit <- as.data.frame(matrix(NA, nrow=nrow(data), ncol = length(fit.names)))
colnames(fit) <- fit.names
fit[x] <- data[[x]]
fit.spline <- NULL
} else
{
#smooth and obtain predictions corresponding to x.pred
fitcorrectBoundaries <- correctBoundaries
if (stype == "NCSS" && length(distinct.xvals) <= 5 && correctBoundaries)
{
warning(paste("Need more than 5 distinct x values to correct the end-points of a spline",
"- no corrections made", sep = " "))
fitcorrectBoundaries <- FALSE
}
if (stype == "NCSS")
{
if (is.null(df))
{
if (is.null(lambda))
fit.spline <- ncsSpline(tmp[c(x, response)], correctBoundaries = fitcorrectBoundaries,
...)
else
fit.spline <- ncsSpline(tmp[c(x, response)], correctBoundaries = fitcorrectBoundaries,
lambda = lambda, ...)
} else
if (is.null(lambda))
fit.spline <- ncsSpline(tmp[c(x, response)], correctBoundaries = fitcorrectBoundaries,
df = df, ...)
} else #PS
{
#Determine npspline.segments for a full set of x
if (is.null(npspline.segments))
npspline.segments <- max(10, ceiling((nrow(data)-1)/2))
#Adjust for the missing values in x
nfit <- length(distinct.xvals)
if (nfit < nobs)
{
nperseg <- nobs/npspline.segments
npspline.segments <- ceiling(nfit/nperseg)
}
fit.spline <- pSpline(tmp[c(x, response)], npspline.segments = npspline.segments, lambda = lambda, ...)
}
x.pred <- remove.repeats(sort(x.pred), tolerance = tol)
fit <- NULL
if (length(x.pred) == length(fit.spline$x))
{
if (all(abs(x.pred - fit.spline$x) < tol))
{
if (stype == "NCSS")
fit <- list(fit.spline$x, fit.spline$y)
else
fit <- list(fit.spline$x, fit.spline$y)
}
}
#Need to refit for current x.pred
if (is.null(fit))
{
if (stype == "NCSS")
fit <- predict.ncsSpline(fit.spline, x = x.pred,
correctBoundaries = fitcorrectBoundaries)
else
fit <- predict.pSpline(fit.spline, x = x.pred,
npspline.segments = fit.spline$uncorrected.fit$npspline.segments)
}
rsmooth <- response.smoothed
names(fit) <- c(x, rsmooth)
#backtransform if transformed
if (smethod == "logarithmic")
fit[[rsmooth]] <- exp(fit[[rsmooth]])
#get derivatives if required
if (!correctBoundaries & !is.null(derivs))
{
for (d in derivs)
{
if (is.null(suffices.derivs))
rsmooth.dv <- paste0(response.smoothed,".dv",d)
else
{
k <- ifelse(length(derivs) == 1, 1, match(d, derivs))
rsmooth.dv <- paste(response.smoothed, suffices.derivs[k], sep=sep.rates)
}
if (stype == "NCSS")
fit[[rsmooth.dv]] <- predict(fit.spline$uncorrected.fit, x = x.pred, deriv=d)$y
else
fit[[rsmooth.dv]] <- predict.pSpline(fit.spline, x = x.pred,
npspline.segments = fit.spline$uncorrected.fit$npspline.segments,
deriv=d)$y
}
#Add RGR if required
if (!is.null(extra.rate) && extra.rate == "RGR")
{
#Check have the required computed derivative
if (is.null(suffices.derivs))
rsmooth.dv <- paste0(response.smoothed,".dv",1)
else
rsmooth.dv <- paste(response.smoothed, suffices.derivs["AGR"], sep=sep.rates)
#get the extra derivative
if (!(rsmooth.dv %in% names(fit)))
stop("First derivative not available to calculate RGR")
fit[[paste(rsmooth,names(extra.rate),sep=sep.rates)]] <- fit[[rsmooth.dv]]/fit[[rsmooth]]
}
#Add AGR if required
if (!is.null(extra.rate) && extra.rate == "AGR")
{
#Check have the required computed derivative
if (is.null(suffices.derivs))
rsmooth.dv <- paste0(response.smoothed,".dv",1)
else
rsmooth.dv <- paste(response.smoothed, suffices.derivs["RGR"], sep=sep.rates)
#get the extra derivative
if (!(rsmooth.dv %in% names(fit)))
stop("First derivative not available to calculate AGR")
fit[[paste(rsmooth,names(extra.rate),sep=sep.rates)]] <- fit[[rsmooth.dv]]*fit[[rsmooth]]
}
}
fit <- as.data.frame(fit)
#Remove temporary rate, if there are any
if (length(grep("._tmp", names(fit), fixed = TRUE)))
fit <- fit[, -grep("._tmp", names(fit), fixed = TRUE)]
#Merge data and fit, preserving x-order in data
x.ord <- order(data[[x]])
fit <- merge(data[c(x,response)],fit, all.x = TRUE, sort = FALSE)
fit <- fit[,-match(response, names(fit))]
fit <- fit[order(fit[[x]]),]
fit[x.ord,] <- fit
}
rownames(fit) <- NULL
return(list(predictions = fit, fit.spline = fit.spline))
}
"probeSmooths" <- function(data, response = "PSA", response.smoothed = NULL,
individuals="Snapshot.ID.Tag", times = "DAP",
keep.columns = NULL,
get.rates = TRUE,
rates.method="differences", ntimes2span = NULL,
trait.types = c("response", "AGR", "RGR"),
smoothing.args =
args4smoothing(smoothing.methods = "direct",
spline.types = "NCSS",
df = NULL, lambdas = NULL),
x.title = NULL, y.titles = NULL, which.plots = "profiles",
profile.plot.args =
args4profile_plot(plots.by = NULL,
facet.x = ".", facet.y = ".",
include.raw = "no"),
meddevn.plot.args =
args4meddevn_plot(plots.by = NULL, plots.group = NULL,
facet.x = ".", facet.y = ".",
propn.note = TRUE,
propn.types = c(0.1, 0.5, 0.75)),
devnboxes.plot.args =
args4devnboxes_plot(plots.by = NULL,
facet.x = ".", facet.y = ".",
which.plots = "none"),
...)
{
smoothing.args <- smoothing.args
profile.plot.args <- profile.plot.args
meddevn.plot.args <- meddevn.plot.args
#check input arguments
impArgs <- match.call()
if ("na.rm" %in% names(impArgs))
stop("na.rm has been deprecated; use na.x.action and na.y.action")
if ("smoothing.scales" %in% names(impArgs))
stop("smoothing.scales has been deprecated; use smoothing.methods")
if ("deviations.boxplots" %in% names(impArgs))
stop("deviations.boxplots has been deprecated; use which.plots")
inargs <- list(...)
checkEllipsisArgs(c("probeSmooths","plotProfiles"), inargs)
smooth.cols <- c("Type","TunePar","TuneVal","Tuning","Method")
data[times] <- convertTimes2numeric(data[[times]])
#Deal with plot arguments
options <- c("none", "profiles", "absolute.boxplots", "relative.boxplots", "medians.deviations")
plots <- options[unlist(lapply(which.plots, check.arg.values, options=options))]
if ("none" %in% plots & length(plots) > 1)
plots <- "none"
if (is.null(x.title))
x.title <- times
if (!is.allnull(smoothing.args) && smoothing.args$combinations == "single")
{
if (any(unlist(lapply(names(smoothing.args)[1:4],
function(x, smth.args) length(smth.args[x]) > 1,
smth.args = smoothing.args))))
stop("All of the components of smoothing.args must be single-valued when combinations is single")
if (is.null(smoothing.args$df)) smoothing.args$df <- NA
if (is.null(smoothing.args$lambdas)) smoothing.args$lambdas <- NA
}
#Get the options for the profile plots options from the list
plots.by.pf <- profile.plot.args$plots.by
facet.x.pf <- profile.plot.args$facet.x
facet.y.pf <- profile.plot.args$facet.y
include.raw.pf <- profile.plot.args$include.raw
collapse.facets.x.pf <- profile.plot.args$collapse.facets.x
collapse.facets.y.pf <- profile.plot.args$collapse.facets.y
facet.labeller <- profile.plot.args$facet.labeller
scales.pf <- profile.plot.args$scales
breaks.spacing.x.pf <- profile.plot.args$breaks.spacing.x
colour.pf <- profile.plot.args$colour
colour.column.pf <- profile.plot.args$colour.column
colour.values.pf <- profile.plot.args$colour.values
alpha.pf <- profile.plot.args$alpha
addMediansWhiskers.pf <- profile.plot.args$addMediansWhiskers
ggplotFuncsProfile <- profile.plot.args$ggplotFuncs
plts.by <- plots.by.pf
#Get the options for the median deviations plots options from the list
plots.by.med <- meddevn.plot.args$plots.by
plots.group.med <- meddevn.plot.args$plots.group
facet.x.med <- meddevn.plot.args$facet.x
facet.y.med <- meddevn.plot.args$facet.y
facet.labeller = meddevn.plot.args$facet.labeller
facet.scales.med <- meddevn.plot.args$facet.scales
breaks.spacing.x.med <- meddevn.plot.args$breaks.spacing.x
colour.values.med <- meddevn.plot.args$colour.values
shape.values.med <- meddevn.plot.args$shape.values
alpha.med <- meddevn.plot.args$alpha
propn.note.med <- meddevn.plot.args$propn.note
propn.types.med <- meddevn.plot.args$propn.types
ggplotFuncsMedDevn <- meddevn.plot.args$ggplotFuncs
plts.by.med <- plots.by.med
plts.group.med <- plots.group.med
#Get the options for the deviations boxplots options from the list
plots.by.box <- devnboxes.plot.args$plots.by
facet.x.box <- devnboxes.plot.args$facet.x
facet.y.box <- devnboxes.plot.args$facet.y
include.raw.box <- devnboxes.plot.args$include.raw
collapse.facets.x.box <- devnboxes.plot.args$collapse.facets.x
collapse.facets.y.box <- devnboxes.plot.args$collapse.facets.y
facet.labeller <- devnboxes.plot.args$facet.labeller
scales.box <- devnboxes.plot.args$scales
breaks.spacing.x <- devnboxes.plot.args$breaks.spacing.x
ggplotFuncsDevnBoxes <- devnboxes.plot.args$ggplotFuncs
#Checking of the arguments that control the plots layout for boxplots
if (any(c("absolute.boxplots", "relative.boxplots") %in% plots))
checkLayoutArgs(data = NULL, plots.by.box, plts.group = NULL, facet.x.box, facet.y.box)
#Get columns need for facets
id.cols <- colour.column.pf
if (all(facet.x.pf != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.x.pf))
if (all(facet.x.med != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.x.med))
if (all(facet.x.box != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.x.box))
if (all(facet.y.pf != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.pf))
if (all(facet.y.med != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.med))
if (all(facet.y.box != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.box))
id.cols <- c(individuals, times, response, keep.columns, id.cols)
#Set up name for smoothed response
if (is.null(response.smoothed))
response.smooth <- paste0("s", response)
else
response.smooth <- response.smoothed
responses.smooth <- response.smooth
#Argument for what traits are to be plotted
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
grates <- c("AGR","RGR")[c("AGR","RGR") %in% traits]
if (is.smooths.frame(data))
{
#Check that required cols are in data
checkNamesInData(unique(id.cols), data = data)
smth <- data
if (!is.allnull(smoothing.args))
{
#Extract smooths specified by smoothing.args from smth
if (is.allnull(smoothing.args$df) && is.allnull(smoothing.args$lambdas))
stop("It must be that at least one of df and lambda is not NULL in smoothing.args")
#Get the smoothing arguments
options <- c("allvalid", "parallel", "single")
comb.opt <- options[check.arg.values(smoothing.args$combinations, options=options)]
smethods = smoothing.args$smoothing.methods
stypes = smoothing.args$spline.types
df = smoothing.args$df
lambdas = smoothing.args$lambdas
#Construct the set of schemes for which smooths are to be generated
spar.schemes <- makeSmoothSchemes(combinations = comb.opt,
smethods = smethods, stypes = stypes,
df = df, lambdas = lambdas)
#Convert smoothing combinations to factors, paying attention to levels order
spar.schemes[c("Type","TunePar","TuneVal","Tuning","Method")] <-
lapply(spar.schemes[c("Type","TunePar","TuneVal","Tuning","Method")],
function(x) factor(x, levels = as.character(unique(x))))
selection <- levels(with(spar.schemes, fac.combine(list(Type,TunePar,TuneVal,Method),
combine.levels = TRUE, sep = "-")))
combos.fac <- (with(smth, fac.combine(list(Type,TunePar,TuneVal,Method),
combine.levels = TRUE, sep = "-")))
combos <- levels(combos.fac)
if (!all(selection %in% combos))
stop("Not all combinations of the values of smoothing parameters specified by smoothing.args ",
"amongst those for the set of smooths in data")
#Get subset
tmp <- split(smth, combos.fac)
names(tmp) <- combos
smth <- lapply(selection, function(seln, tmp) tmp[[seln]], tmp = tmp)
smth <- do.call(rbind, smth)
smth[c("Type","TunePar","TuneVal","Method")] <- lapply(smth[c("Type","TunePar","TuneVal","Method")], factor)
smth <- as.smooths.frame(smth, individuals, times)
}
} else
{
if (is.allnull(smoothing.args))
stop("data is not a smooths.frame and smoothing.args is NULL so that there are no smooths available")
#Deal with data arguments for a data.frame
#Argument for which response the rates are to be computed
options <- c("none", "raw","smoothed")
if (is.logical(get.rates))
{
if (get.rates)
get.which <- c("raw","smoothed")
else
get.which <- "none"
}
else
get.which <- options[unlist(lapply(get.rates, check.arg.values, options=options))]
if (length(grates) == 0 && !("none" %in% get.which))
{
get.which <- "none"
warning("trait.types does not include AGR or RGR and so get.rates has been set to none")
} else
{
if (length(traits) > 1 || traits != "response")
{
if ("none" %in% get.which || !("smoothed" %in% get.which))
{
traits <- "response"
grates <- c("AGR","RGR")[c("AGR","RGR") %in% traits]
propn.types.med <- propn.types.med[1]
warning(paste("The calculation of smoothed growth rates have not been specified;",
"trait.types changed to response and propn.type reduced to its first element"))
}
}
}
if (!("raw" %in% get.which) && length(grates) > 0) #Check if rates are needed but not being obtained.
{
raw.rates <- paste(response, grates, sep = ".")
if (all(raw.rates %in% names(data)))
id.cols <- c(id.cols, raw.rates)
}
options <- c("differences","derivatives")
ratemeth.opt <- options[check.arg.values(rates.method, options=options)]
if ("smoothed" %in% get.which && is.null(ntimes2span))
{
if (ratemeth.opt == "differences")
ntimes2span <- 2
if (ratemeth.opt == "derivatives")
ntimes2span <- 3
}
#Get the smoothing arguments
smethods = smoothing.args$smoothing.methods
stypes = smoothing.args$spline.types
df = smoothing.args$df
lambdas = smoothing.args$lambdas
smoothing.segments = smoothing.args$smoothing.segments
npspline.segments = smoothing.args$npspline.segments
na.x.action = smoothing.args$na.x.action
na.y.action = smoothing.args$na.y.action
external.smooths = smoothing.args$external.smooths
correctBoundaries = smoothing.args$correctBoundaries
if ((is.allnull(df) && is.allnull(lambdas)))
stop("It must be that at least one of df and lambdas is not NULL in smoothing.args")
if (length(npspline.segments) > 1)
{
if (is.null(smoothing.segments))
stop("npspline.segments must be of length one in an unsegmented spline fit")
else
{
if (length(npspline.segments) != length(smoothing.segments))
stop("the number of values of npspline.segments should be one or ",
"equal to the number of segments in a segmented spline fit")
}
if (!all(diff(unlist(smoothing.segments)) > 0))
stop("the smoothing.segments are not a set of non-overlapping, successive intervals")
}
#Check that for overlapping smoothing segments and give an error or a warning depending on get.rates
if (!all(diff(unlist(smoothing.segments)) > 0))
{
if (!("none" %in% get.which))
stop("rates.method must be `none` when times values occur in more than one smoothing segment")
else
warning("The values for some times occur in multiple smoothing segments and so some individuals ",
"will have multiple rows in the returned data.frame, one for each segment in which the ",
"times occur.")
}
v <- unique(id.cols)
v <- setdiff(v, c("Type","TunePar","TuneVal","Tuning","Method")) #remove names yet to come
#Check that required cols are in data
checkNamesInData(v, data = data)
#Check that there is no more than one observation for each individuals-times combinations
if (!all(table(data[c(individuals, times)]) <= 1))
stop("There is more than one observation for one or more combinations of the individuals and times")
tmp <- data[v]
times.diffs.in.data <- paste0(times, ".diffs") %in% names(data)
#Form raw growth rates when get.rates includes raw.
#Do for all data even if segmented so that only the observation for the very first time is NA
if ("raw" %in% get.which)
tmp <- byIndv4Times_GRsDiff(data = tmp, response,
individuals=individuals,
times=times, avail.times.diffs = FALSE,
which.rates = grates, ntimes2span = ntimes2span)
#Construct the set of schemes for which smooths are to be generated
spar.schemes <- makeSmoothSchemes(combinations = smoothing.args$combinations,
smethods = smethods, stypes = stypes,
df = df, lambdas = lambdas)
#Generate the smooths
if (is.allnull(smoothing.segments))
smth <- smoothSchemes(tmp = tmp, spar.schemes = spar.schemes,
response = response, response.smooth = response.smooth,
times=times, ntimes2span = ntimes2span,
individuals = individuals, traits = traits,
get.rates = ("smoothed" %in% get.which),
ratemeth.opt = ratemeth.opt, grates = grates,
nseg = npspline.segments, correctBoundaries = correctBoundaries,
na.x.action = na.x.action, na.y.action = na.y.action)
else
{
knseg <- npspline.segments[1]
smth <- data.frame()
for (k in 1:length(smoothing.segments))
{
segm <- smoothing.segments[[k]]
subdat <- tmp[(tmp[times] >= segm[1]) & (tmp[times] <= segm[2]),]
if (length(npspline.segments) > 1) knseg <- npspline.segments[k]
#only get smooths when difference growth rates are required and ntimes2span is 2
smth <- rbind(smth,
smoothSchemes(tmp = subdat, spar.schemes = spar.schemes,
response = response, response.smooth = response.smooth,
times=times, ntimes2span = ntimes2span,
individuals = individuals, traits = traits,
get.rates = (ntimes2span == 2 &&
("smoothed" %in% get.which) &&
ratemeth.opt == "differences"),
ratemeth.opt = ratemeth.opt, grates = grates,
nseg = knseg, correctBoundaries = correctBoundaries,
na.x.action = na.x.action, na.y.action = na.y.action))
}
smth <- smth[do.call(order, smth), ]
#get overall difference growth rates for ntimes2apn == 2
if (ntimes2span == 2 && ("smoothed" %in% get.which) && ratemeth.opt == "differences")
{
smth <- split(smth, fac.combine(as.list(smth[c("Type","Tuning","Method")])))
smth <- lapply(smth, byIndv4Times_GRsDiff, responses = response.smooth,
individuals = individuals,
times=times, ntimes2span = ntimes2span,
which.rates = grates)
smth <- do.call(rbind, smth)
smth <- smth[do.call(order, smth), ]
}
}
#Add external.smooths, if required
if (!is.null(external.smooths))
{
#Determine which smoothing-parameter columns are in external.smooths & check have required other columns
external.smooths <- as.data.frame(external.smooths)
smth.cols <- intersect(smooth.cols, names(external.smooths))
if (length(smth.cols) == 0)
stop("No smoothing parameter columns have been included in ", deparse(substitute(external.smooths)))
if ("raw" %in% get.which)
{
ext.nam <- names(external.smooths)
tmp <- split(external.smooths, external.smooths[smth.cols])
tmp <- lapply(tmp, byIndv4Times_GRsDiff, responses = response, individuals=individuals,
times=times, avail.times.diffs = FALSE,
which.rates = grates, ntimes2span = ntimes2span)
external.smooths <- do.call(rbind, tmp)
external.smooths <- external.smooths[c(ext.nam, setdiff(names(external.smooths), ext.nam))]
external.smooths <- external.smooths[do.call(order, external.smooths),]
}
if ("smoothed" %in% get.which)
{
ext.nam <- names(external.smooths)
tmp <- split(external.smooths, external.smooths[smth.cols])
tmp <- lapply(tmp, byIndv4Times_GRsDiff, responses = response.smooth, individuals=individuals,
times=times, avail.times.diffs = FALSE,
which.rates = grates, ntimes2span = ntimes2span)
external.smooths <- do.call(rbind, tmp)
external.smooths <- external.smooths[c(ext.nam, setdiff(names(external.smooths), ext.nam))]
external.smooths <- external.smooths[do.call(order, external.smooths),]
}
extra.vars <- setdiff(names(smth), smooth.cols)
if (all(extra.vars %in% names(external.smooths)))
external.smooths <- external.smooths[c(smth.cols, extra.vars)]
else
stop(paste("Do not have the following required columns in data: ",
paste(extra.vars[!(extra.vars %in% names(external.smooths))],collapse=", "), "\n", sep=""))
#Add missing smoothing-parameter columns
smth.cols <- setdiff(smooth.cols, smth.cols)
smooth.pars <- rep("Other", length(smth.cols))
names(smooth.pars) <- smth.cols
smooth.pars <- rbind(smooth.pars)
rownames(smooth.pars) <- NULL
tmp <- cbind(smooth.pars, external.smooths)
tmp <- tmp[names(smth)]
#Add extra smooths to smth
smth <- rbind(smth,tmp)
#Update the smoothing-parameter schemes
tmp <- split(tmp, dae::fac.combine(as.list(lapply(tmp[smooth.cols], as.factor)),
combine.levels = TRUE, sep = "-"))
sch <- lapply(tmp, function(x) x[1, smooth.cols])
sch <- do.call(rbind, sch)
rownames(sch) <- NULL
spar.schemes <- rbind(spar.schemes, sch[colnames(spar.schemes)])
}
#Form a smooths.frame and check that it is valid
smth <- as.smooths.frame(smth, individuals, times)
}
validsmoothsframe <- validSmoothsFrame(smth)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
#Plot profiles of some combination of unsmoothed and smoothed response, AGR and RGR
if ("profiles" %in% plots && !is.allnull(profile.plot.args))
plotSmoothsComparison(data = smth,
response = response, response.smoothed = response.smooth,
times = times, individuals = individuals,
trait.types = traits,
x.title = x.title, y.titles = y.titles,
profile.plot.args = profile.plot.args, ...)
#Plot median deviations plots
if ("medians.deviations" %in% plots && !is.allnull(meddevn.plot.args))
plotSmoothsMedianDevns(data = smth,
response = response, response.smoothed = response.smooth,
times = times, individuals = individuals,
trait.types = traits,
x.title = x.title, y.titles = y.titles,
meddevn.plot.args = meddevn.plot.args,
...)
#Plot deviations of unsmoothed and smoothed response, and possibly AGR and RGR
if (any(c("absolute.boxplots", "relative.boxplots") %in% plots) && !is.allnull(devnboxes.plot.args))
{
boxp <- c("absolute.boxplots", "relative.boxplots")[c("absolute.boxplots", "relative.boxplots") %in% plots]
#Plot boxplots
plotSmoothsDevnBoxplots(data = smth,
response = response, response.smoothed = response.smooth,
times = times, individuals = individuals,
trait.types = traits,
which.plots = boxp,
x.title = x.title, y.titles = y.titles,
devnboxes.plot.args = devnboxes.plot.args,
...)
}
invisible(smth)
}
"traitChooseSmooth" <- function(smooths, response.smoothed, individuals, times,
keep.columns = NULL,
x.title = NULL, y.titles = NULL,
trait.types = c("response.smoothed", "AGR", "RGR"),
chosen.smooth = args4chosen_smooth(),
chosen.plot.args = args4chosen_plot(),
mergedata = NULL,
...)
{
chosen.smooth <- chosen.smooth
chosen.plot.args <- chosen.plot.args
inargs <- list(...)
checkEllipsisArgs(c("traitChooseSmooth", "plotProfiles"), inargs)
#Process chosen.smooth and other arguments
options <- c("allvalid","parallel","single")
comb.opt <- options[check.arg.values(chosen.smooth$combinations, options=options)]
if (comb.opt != "single")
stop("combinations must be single for chosen.smooth.args")
if (any(unlist(lapply(chosen.smooth, function(x) (length(x) > 1)))))
stop("All of the components of chosen.smooth.args must be single-valued")
if ((!is.allnull(chosen.smooth$df) && !is.allnull(chosen.smooth$lambdas)))
stop("One of df and lambda must be NULL in chosen.smooth.args")
combos <- levels(with(smooths, fac.combine(list(Type,TunePar,TuneVal,Method),
combine.levels = TRUE, sep = "-")))
tparams <- conv2TuneParams(smth.args = chosen.smooth, smth = smooths)
stype <- tparams$stype
tunepar <- tparams$tunepar
tuneval <- tparams$tuneval
smethod <- tparams$smethod
choice <- paste0(sapply(tparams, as.character), collapse = "-")
if (!(choice %in% combos))
stop("The combination of the values of Type, TunePar, TuneVal and Method ",
"given in chosen.smooth.args are not amongst those for the set of smooths in data")
#Get subset
smth <- smooths[smooths$Type == stype & smooths$TunePar == tunepar &
smooths$TuneVal == tuneval & smooths$Method == smethod,
setdiff(names(smooths), c("Type","TunePar","TuneVal","Method","Tuning"))]
class(smth) <- "data.frame"
#merge it with the mergedata
if (!is.null(mergedata))
{
checkNamesInData(c(individuals, times), mergedata)
if (!is.null(keep.columns))
smth <- smth[setdiff(names(smth), keep.columns)]
mergedata <- mergedata[c(individuals, times, setdiff(names(mergedata), names(smth)))]
smth <- merge(mergedata, smth, sort = FALSE)
#Order the columns
smth <- smth[c(names(mergedata),setdiff(names(smth), names(mergedata)))]
}
if (!is.allnull(chosen.plot.args))
{
smth[times] <- convertTimes2numeric(smth[[times]])
#Plot the profile plots for the chosen smooth
options <- c("response.smoothed", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
grates <- c("AGR", "RGR")
else
{
grates <- traits[-match("response.smoothed", traits)]
}
grates <- c("AGR","RGR")[c("AGR","RGR") %in% grates]
if (length(grates) == 0)
responses.plot <- response.smoothed
else
responses.plot <- c(response.smoothed, paste(response.smoothed, grates, sep = "."))
if (is.null(x.title))
x.title <- times
if (is.null(y.titles))
{
y.titles <- responses.plot
}
else
{
if (length(y.titles) != length(responses.plot))
stop("y.titles is not NULL and have not been provided for the response, the AGR and the RGE")
}
names(y.titles) <- responses.plot
#Find out if any plotProfiles arguments that args4profile_plot handles have been supplied in '...'
if (length(inargs))
{
usedProfile.args <- c("facet.x","facet.y","labeller","scales","colour","colour.column","colour.values",
"alpha","addMediansWhiskers","ggplotFuncs") #formalArgs(args4profile_plot)
doubleargs <- intersect(names(inargs), usedProfile.args)
if (length(doubleargs) > 0)
stop("the 'plotProfiles' arguments ",paste0(doubleargs, collapse = ", "),
" conflict with 'args4profile_plot' arguments")
} else
usedProfile.args <- NULL
#extract any valid plotProfiles arguments from inargs
pltProfile.args <- setdiff(formalArgs(plotProfiles), usedProfile.args)
pltProfile.args <- names(inargs)[names(inargs) %in% pltProfile.args]
if (length(pltProfile.args))
pltProfile.args <- inargs[pltProfile.args]
else
pltProfile.args <- NULL
if (is.null(pltProfile.args) || !("title" %in% names(pltProfile.args)))
title <- paste0("Plot for the choice ", paste0(c(substring(smethod,1,3),stype,
tunepar,tuneval), collapse = "-"))
#Call plot longitudinal with just the plotProfile args from inargs
for (kresp in responses.plot)
do.call(plotProfiles, list(data = smth, times = times, response = kresp,
individuals = individuals,
facet.x = chosen.plot.args$facet.x,
facet.y = chosen.plot.args$facet.y,
labeller = chosen.plot.args$facet.labeller,
scales = chosen.plot.args$facet.scales,
breaks.spacing.x = chosen.plot.args$breaks.spacing.x,
colour = chosen.plot.args$colour,
colour.column = chosen.plot.args$colour.column,
colour.values = chosen.plot.args$colour.values,
alpha = chosen.plot.args$alpha,
title = title,
x.title = x.title,
y.title = y.titles[kresp],
printPlot = TRUE,
ggplotFuncs = c(list(theme(axis.text.x =
element_text(angle = chosen.plot.args$angle.x))),
chosen.plot.args$ggplotFuncs),
...))
}
#Make sure that times is of the same type as times in data
smth[times] <- convertTimesExnumeric(smth[[times]], mergedata[[times]])
invisible(smth)
}
"traitSmooth" <- function(data, response, response.smoothed, individuals, times,
keep.columns = NULL,
get.rates = TRUE,
rates.method="differences", ntimes2span = NULL,
trait.types = c("response", "AGR", "RGR"),
smoothing.args = args4smoothing(),
x.title = NULL, y.titles = NULL,
which.plots = c("profiles", "medians.deviations"),
profile.plot.args = args4profile_plot(),
meddevn.plot.args = args4meddevn_plot(),
devnboxes.plot.args = args4devnboxes_plot(),
chosen.smooth.args = args4chosen_smooth(),
chosen.plot.args = args4chosen_plot(),
mergedata = NULL,
...)
{
#This is needed to make sure that the functions have been evaluated
smoothing.args <- smoothing.args
profile.plot.args <- profile.plot.args
meddevn.plot.args <- meddevn.plot.args
chosen.smooth.args <- chosen.smooth.args
chosen.plot.args <- chosen.plot.args
devnboxes.plot.args = devnboxes.plot.args
inargs <- list(...)
checkEllipsisArgs(c("traitSmooth","plotProfiles"), inargs)
tmp <- data
tmp[times] <- convertTimes2numeric(tmp[[times]])
#Call probeSmooths
smth <- do.call(probeSmooths, list(data = tmp,
response = response, response.smoothed,
times = times, individuals = individuals,
keep.columns = keep.columns,
trait.types = trait.types,
get.rates = get.rates,
rates.method = rates.method,
ntimes2span = ntimes2span,
x.title = x.title, y.titles = y.titles,
which.plots = which.plots,
smoothing.args = smoothing.args,
profile.plot.args = profile.plot.args,
meddevn.plot.args = meddevn.plot.args,
devnboxes.plot.args = devnboxes.plot.args,
...))
#Process chosen model
if (!is.allnull(chosen.smooth.args))
{
#Check of chosen.plot factors
chosen.plotfacs <- unique(c(chosen.plot.args$plots.by, chosen.plot.args$facet.x, chosen.plot.args$facet.y))
chosen.plotfacs <- setdiff(chosen.plotfacs, ".")
#Check that required cols are in data
checkNamesInData(c(chosen.plotfacs, chosen.plot.args$colour.column), data = smth)
#Check that no smoothing parameter factors have been supplied in plots.by, facet.x and facet.y for the chosen plot
if (any(smooth.cols %in% chosen.plotfacs))
stop("The smoothing parameter factor(s) ",
paste(smooth.cols[smooth.cols %in% chosen.plotfacs], collapse = ", "),
" occur(s) in chosen.plots.args - only a single smooth is to be plotted and they are unnecessary")
traits <- c("response.smoothed", "AGR", "RGR", "all")
traits <- traits[unlist(lapply(trait.types, check.arg.values, options=traits))]
if ("all" %in% traits)
traits <- c("response.smoothed", "AGR", "RGR")
#reduce ellipsis args to plotProfiles args
pltProfile.args <- names(inargs)[names(inargs) %in% formalArgs(plotProfiles)]
if (length(pltProfile.args))
pltProfile.args <- inargs[pltProfile.args]
else
pltProfile.args <- NULL
ch.smth <- do.call(traitChooseSmooth,
c(list(smooths = smth, response.smoothed = response.smoothed,
individuals = individuals, times = times,
keep.columns = keep.columns,
x.title = x.title, y.titles = y.titles,
trait.types = traits,
chosen.smooth = chosen.smooth.args,
chosen.plot.args = chosen.plot.args,
mergedata = mergedata),
pltProfile.args))
#The chosen smooth is to be returned
if (!is.null(mergedata))
smth <- ch.smth
} else # merge with the original data if there is only one smooth
{
if (all(sapply(smth[smooth.cols], function(x) nlevels(factor(x)) == 1)))
{
if (is.smooths.frame(data))
{
if (is.null(mergedata))
smth <- smth[setdiff(names(smth), c("Type","TunePar","TuneVal","Method","Tuning"))]
else
{
checkNamesInData(c(individuals, times), mergedata)
if (!is.null(keep.columns))
smth <- smth[setdiff(names(smth), keep.columns)]
mergedata <- mergedata[c(individuals, times, setdiff(names(mergedata), names(smth)))]
smth <- merge(mergedata, smth, sort = FALSE)
#Order the columns
smth <- smth[c(names(mergedata),setdiff(names(smth), names(mergedata)))]
}
} else
{
smth <- smth[setdiff(names(smth), c("Type","TunePar","TuneVal","Method","Tuning"))]
if (!is.null(keep.columns))
smth <- smth[setdiff(names(smth), keep.columns)]
class(smth) <- "data.frame"
tmp <- tmp[c(individuals, times, setdiff(names(tmp), names(smth)))]
smth <- merge(tmp, smth, sort = FALSE)
#Order the columns
smth <- smth[c(names(tmp),setdiff(names(smth), names(tmp)))]
}
}
}
#Make sure that times is of the same type as times in data
smth[times] <- convertTimesExnumeric(smth[[times]], data[[times]])
invisible(smth)
}
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Defines functions predict.pSpline pSpline predict.ncsSpline ncsSpline plotDeviationsBoxes
Documented in plotDeviationsBoxes
#Function to produce a single plot of deviations boxplots
plotDeviationsBoxes <- function(data, observed, smoothed, x.factor,
x.title = NULL, y.titles = NULL,
facet.x = ".", facet.y = ".",
facet.labeller = NULL,
facet.scales = "fixed",
angle.x = 0,
deviations.plots = "absolute",
ggplotFuncs = NULL, printPlot = TRUE, ...)
{
options <- c("none", "profiles", "absolute.boxplots", "relative.boxplots",
"medians.deviations", "compare.medians") #needed for probeSmoothing only
devnplots <- options[unlist(lapply(deviations.plots, check.arg.values,
options=options))]
if ("none" %in% devnplots)
devnplots <- "none"
else
{
devnplots <- devnplots[grepl("boxplots", devnplots, fixed = TRUE)]
if (length(devnplots) == 0)
devnplots <- "none"
}
plts <- list()
#only do deviations boxplots if requested
if (all(devnplots != "none"))
{
dat <- data
if (!all(c(observed, smoothed) %in% names(dat)))
stop(paste("One or more of", observed, "and", smoothed, "is missing from ",
deparse(substitute(data))))
if (is.null(x.title))
x.title <- x.factor
strip.text.size <- 10
if (is.null(y.titles) && !("none" %in% devnplots))
{
y.titles <- c(paste("Absolute", observed, "deviations", sep = " "),
paste("Relative", observed, "deviations", sep = " "))
names(y.titles ) <- c("absolute.boxplots", "relative.boxplots")
y.titles <- y.titles[c("absolute.boxplots", "relative.boxplots") %in% devnplots]
}
if (length(y.titles) != length(devnplots))
stop("y.titles does not have a title for each plot in deviations.plot")
names(y.titles) <- devnplots
ggfacet <- list()
#Set up facet if have any
if (all(facet.x != ".") | all(facet.y != "."))
{
facet.form <- facet.char2formula(facet.x,facet.y)
if (is.null(facet.labeller))
ggfacet <- list(facet_grid(facet.form, scales = facet.scales))
else
ggfacet <- list(facet_grid(facet.form, labeller = facet.labeller, scales = facet.scales))
ggfacet <- c(ggfacet,
list(theme(strip.text = element_text(size=strip.text.size, face="bold"))))
}
plotDevnBox <- function(dat, y.ytitle, ggplotFuncs = NULL, printPlot = TRUE)
{
plt <- ggplot(data = dat,
aes(x = .data[[!!x.factor]], y = .data[["deviations"]]), ...) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "grey60", linewidth = 0.5),
panel.grid.minor = element_line(colour = "grey80", linewidth = 0.5),
axis.title = element_text(face="bold"),
axis.text.x = element_text(angle = angle.x)) +
geom_boxplot() +
geom_hline(yintercept=0, colour="blue") +
ylab(y.ytitle) + xlab(x.title) +
ggfacet
if (!is.null(ggplotFuncs))
{
for(f in ggplotFuncs)
plt <- plt + f
}
if (printPlot)
print(plt)
return(plt)
}
if ("absolute.boxplots" %in% devnplots)
{
dat$deviations <- dat[[observed]] - dat[[smoothed]]
plts[["absolute"]] <- plotDevnBox(dat, y.ytitle = y.titles["absolute.boxplots"],
ggplotFuncs = ggplotFuncs, printPlot = printPlot)
}
if ("relative.boxplots" %in% devnplots)
{
dat$deviations <- (dat[[observed]] - dat[[smoothed]])/dat[[smoothed]]
plts[["relative"]] <- plotDevnBox(dat, y.ytitle = y.titles["relative.boxplots"],
ggplotFuncs = ggplotFuncs, printPlot = printPlot)
}
}
invisible(plts)
}
"plotSmoothsDevnBoxplots" <- function(data, response, response.smoothed = NULL,
individuals = "Snapshot.ID.Tag", times = "DAP",
trait.types = c("response", "AGR", "RGR"),
which.plots = "absolute.boxplots",
x.title = NULL, y.titles = NULL,
devnboxes.plot.args =
args4devnboxes_plot(plots.by = NULL,
facet.x = ".", facet.y = "."),
printPlot = TRUE, ...)
{
devnboxes.plot.args <- devnboxes.plot.args
inargs <- list(...)
checkEllipsisArgs(c("plotSmoothsDevnBoxplots","plotDeviationsBoxes"), inargs)
options <- c("none", "absolute.boxplots", "relative.boxplots")
plots <- options[unlist(lapply(which.plots, check.arg.values, options=options))]
if ("none" %in% plots && length(plots) > 1)
plots <- "none"
plts <- med.devn.dat <- NULL
#Check that plots are wanted
if (!("none" %in% plots) && !is.allnull(devnboxes.plot.args))
{
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
#Get the options for the deviations boxplots options from the list
plots.by.box <- devnboxes.plot.args$plots.by
facet.x.box <- devnboxes.plot.args$facet.x
facet.y.box <- devnboxes.plot.args$facet.y
collapse.facets.x.box <- devnboxes.plot.args$collapse.facets.x
collapse.facets.y.box <- devnboxes.plot.args$collapse.facets.y
facet.labeller <- devnboxes.plot.args$facet.labeller
scales.box <- devnboxes.plot.args$scales
angle.x.box <- devnboxes.plot.args$angle.x
ggplotFuncsDevnBoxes <- devnboxes.plot.args$ggplotFuncs
#Checking of the arguments that control the plots layout for boxplots
checkLayoutArgs(data = NULL, plots.by.box, plts.group = NULL, facet.x.box, facet.y.box)
plts.by <- plots.by.box
#Check have a valid smooths.frame
validsmoothsframe <- validSmoothsFrame(data)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
checkPlotsArgs(data, plts.by = plts.by, facet.x = facet.x.box, facet.y = facet.y.box)
options <- c("none", "absolute.boxplots", "relative.boxplots")
plots <- options[unlist(lapply(which.plots, check.arg.values, options=options))]
if ("none" %in% plots & length(plots) > 1)
plots <- "none"
if (is.null(x.title))
x.title <- times
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
response.smooth <- response.smoothed
#Check that responses, response.smoothed, individuals and times are in data
checkNamesInData(c(response, response.smoothed, individuals, times), data = data)
addRates <- function(traits, response, sep = ".")
{
unlist(lapply(traits,
function(trait, response)
{
if (!("response" %in% trait))
response <- paste(response, trait, sep = sep)
return(response)
}, response = response))
}
kresp <- addRates(traits, response = response)
if (!all(kresp %in% names(data)))
stop("The following traits are not in the smooths.frame: ",
paste0(kresp[!(kresp %in% names(data))], collapse = ", "),
"; perhaps, trait.types needs to be set differently")
kresp.sm <- addRates(traits, response = response.smoothed)
names(kresp.sm) <- kresp
if (is.null(y.titles))
{
y.titles <- addRates(traits, response = response, sep = " ")
names(y.titles) <- kresp
} else
{
if (length(y.titles) != length(kresp))
stop("y.titles should be the same length as trait.types")
else
names(y.titles) <- kresp
}
data[times] <- convertTimes2numeric(data[[times]])
times.factor <- ".Time.fac"
data[times.factor] <- data[times]
data[times.factor] <- with(data, eval(parse(text =times)))
data[times.factor] <- factor(unlist(data[times.factor]),
labels = unique(data[times.factor])[order(unique(data[[times.factor]])),])
#Determine whether there are any smooth.cols on the facets - if not must be in plots.by.box
smoothing.facets <- length(intersect(union(facet.x.box, facet.y.box), smooth.cols)) != 0
#Set up the facets
modfacet <- setupFacet(data = data, facet = facet.x.box, collapse.facets = collapse.facets.x.box,
combined.name = "Combined.x", smooth.cols = smooth.cols)
xfacet <- modfacet$newfacet
data <- modfacet$data
modfacet <- setupFacet(data = data, facet = facet.y.box, collapse.facets = collapse.facets.y.box,
combined.name = "Combined.y", smooth.cols = smooth.cols)
yfacet <- modfacet$newfacet
data <- modfacet$data
#Do the plots
plts <- list()
for (k in kresp)
{
plts[[k]] <- list()
if (is.allnull(plts.by)) #a single plot only
levs.by <- "all"
else
{
data$plots.by.box <- fac.mixcombine(data, plts.by, smooth.cols = smooth.cols)
levs.by <- levels(data$plots.by.box)
}
#Loop over plots.by.box
for (by in levs.by)
{
if (any(c("absolute.boxplots", "relative.boxplots") %in% plots))
{
y.titles.devn <- c(paste("Absolute", k, "deviations", sep = " "),
paste("Relative", k, "deviations", sep = " "))
names(y.titles.devn) <- c("absolute.boxplots", "relative.boxplots")
y.titles.devn <- y.titles.devn[c("absolute.boxplots", "relative.boxplots") %in% plots]
#Plot deviation plots for current plots.by.box
if (is.allnull(plts.by))
tmp1 <- data
else
{
tmp1 <- data[data$plots.by.box==by,]
ggplotFuncsDevnBoxes <- c(ggplotFuncsDevnBoxes, list(ggtitle(paste0("Plot for ", by))))
}
plt <- plotDeviationsBoxes(data = tmp1, x.factor = times.factor,
observed = k, smoothed = kresp.sm[k],
deviations.plots = plots,
x.title = x.title, y.titles = y.titles.devn,
facet.x=xfacet, facet.y=facet.y.box,
facet.labeller = facet.labeller,
facet.scales = scales.box,
angle.x = angle.x.box,
ggplotFuncs = ggplotFuncsDevnBoxes,
printPlot = printPlot)
plts[[k]][["absolute"]][[by]] <- plt[["absolute"]]
plts[[k]][["relative"]][[by]] <- plt[["relative"]]
}
}
}
}
invisible(plts)
}
"plotSmoothsMedianDevns" <- function(data, response, response.smoothed = NULL,
individuals = "Snapshot.ID.Tag", times = "DAP",
trait.types = c("response", "AGR", "RGR"),
x.title = NULL, y.titles = NULL,
meddevn.plot.args =
args4meddevn_plot(plots.by = NULL, plots.group = NULL,
facet.x = ".", facet.y = ".",
propn.note = TRUE,
propn.types = c(0.1, 0.5, 0.75)),
printPlot = TRUE, ...)
{
meddevn.plot.args <- meddevn.plot.args
inargs <- list(...)
checkEllipsisArgs("plotSmoothsMedianDevns", inargs)
plts <- med.devn.dat <- NULL
#Check that plots are wanted
if (!is.allnull(meddevn.plot.args))
{
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
#Check that responses, response.smoothed, individuals and times are in data
checkNamesInData(c(response, response.smoothed, individuals, times), data = data)
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
#Get the options for the median deviations plots options from the list
plots.by.med <- meddevn.plot.args$plots.by
plots.group.med <- meddevn.plot.args$plots.group
facet.x.med <- meddevn.plot.args$facet.x
facet.y.med <- meddevn.plot.args$facet.y
facet.labeller = meddevn.plot.args$facet.labeller
facet.scales.med <- meddevn.plot.args$facet.scales
breaks.spacing.x <- meddevn.plot.args$breaks.spacing.x
angle.x <- meddevn.plot.args$angle.x
colour.values.med <- meddevn.plot.args$colour.values
shape.values.med <- meddevn.plot.args$shape.values
alpha.med <- meddevn.plot.args$alpha
propn.note.med <- meddevn.plot.args$propn.note
propn.types.med <- meddevn.plot.args$propn.types
ggplotFuncsMedDevn <- meddevn.plot.args$ggplotFuncs
plts.by <- plots.by.med
plts.group <- plots.group.med
#Check have a valid smooths.frame
validsmoothsframe <- validSmoothsFrame(data)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
checkPlotsArgs(data, plts.by, plts.group, facet.x = facet.x.med, facet.y = facet.y.med)
strip.text.size <- 10
dat <- data
#Form data.frame with just columns needed
#Create a factor Times that has the plotted values of x for its labels
if (is.null(x.title))
x.title <- times
data[times] <- convertTimes2numeric(data[[times]])
id.cols <- c(individuals, times) #, colour.column)
times.factor <- ".Time.fac"
dat[times.factor] <- dat[times]
dat[times.factor] <- with(dat, eval(parse(text =times)))
dat[times.factor] <- factor(unlist(dat[times.factor]),
labels = unique(dat[times.factor])[order(unique(dat[[times.factor]])),])
fac.group <- NULL
if (!is.allnull(plts.group))
{
dat$SmoothParams <- fac.mixcombine(dat, plts.group, smooth.cols = smooth.cols)
fac.group <- "SmoothParams"
id.cols <- c(fac.group, id.cols)
}
#Determine xfacet and fac.by
xfacet <- facet.x.med
if (!is.allnull(plts.by))
{
dat$fac.by <- fac.mixcombine(dat, plts.by, smooth.cols = smooth.cols)
dat$fac.by <- factor(dat$fac.by)
id.cols <- c("fac.by", id.cols)
}
if (all(xfacet != "."))
id.cols <- c(id.cols, fac.getinFormula(xfacet))
if (all(facet.y.med != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.med))
#Set up facet
ggfacet <- list()
facet.cols <- NULL
if (all(xfacet != ".") || all(facet.y.med != "."))
{
facet.form <- facet.char2formula(xfacet, facet.y.med)
if (is.null(facet.labeller))
ggfacet <- list(facet_grid(facet.form, scales = facet.scales.med))
else
ggfacet <- list(facet_grid(facet.form, scales = facet.scales.med, labeller = facet.labeller))
facet.cols <- c(xfacet, facet.y.med)
facet.cols <- facet.cols[facet.cols != "."]
}
#Form raw and smoothed trait names
addRates <- function(traits, response, sep = ".")
{
unlist(lapply(traits,
function(trait, response)
{
if (!("response" %in% trait))
response <- paste(response, trait, sep = sep)
return(response)
}, response = response))
}
kresp <- addRates(traits, response = response)
kresp.sm <- addRates(traits, response = response.smoothed)
id.cols <- c(id.cols, kresp, kresp.sm)
if (!all(id.cols %in% names(dat)))
stop(paste("Do not have the following required columns in data: ",
paste(id.cols[!(id.cols %in% names(dat))],collapse=", "), "\n", sep=""))
kresp.devn <- paste(kresp, "devn", sep = ".")
names(kresp.sm) <- kresp
names(kresp.devn) <- kresp
if (is.null(y.titles))
{
y.titles <- paste("median", addRates(traits, response = response, sep = " "), "deviations")
names(y.titles) <- kresp
} else
{
if (length(y.titles) != length(kresp))
stop("y.titles should be the same length as trait.types")
else
names(y.titles) <- kresp
}
#Calculate the deviations
dat[kresp.devn] <- dat[kresp] - dat[kresp.sm]
#Calculate the median deviations
split.facs <- c(if (!is.allnull(plts.by)) "fac.by",
facet.cols, fac.group, times.factor) #NULL objects will be ignored
dat <- dat[c(split.facs, setdiff(names(dat), split.facs))]
dat <- dat[do.call(order, dat),]
tmp <- dat
tmp$split.fac <- dae::fac.combine(as.list(dat[split.facs]), combine.levels = TRUE)
dat.split <- split(tmp, f = tmp$split.fac)
med.devn.dat <- lapply(dat.split,
function(data, kresp.devn)
{
med <- unlist(lapply(data[kresp.devn], median, na.rm = TRUE))
krow <- cbind(data[1,split.facs], rbind(med))
return(krow)
},
kresp.devn = kresp.devn)
med.devn.dat <- as.data.frame(do.call(rbind, med.devn.dat))
med.devn.dat[times] <- dae::as.numfac(unlist(med.devn.dat[times.factor]))
#Remove the times.factor
med.devn.dat <- med.devn.dat[,-match(times.factor, names(med.devn.dat))]
#Remove any missing values
med.devn.dat <- med.devn.dat[which(!is.na(med.devn.dat[kresp.devn[1]])), ]
#Calculate the median responses
if (propn.note.med && !is.null(propn.types.med))
{
if (length(propn.types.med) != length(kresp))
stop("Length of propn.types.med is not the same as the number of trait.types")
names(propn.types.med) <- kresp
med.resp.dat <- lapply(dat.split,
function(data, kresp)
{
med <- unlist(lapply(data[kresp], median, na.rm = TRUE))
krow <- cbind(data[1,split.facs], rbind(med))
return(krow)
},
kresp = kresp)
med.resp.dat <- as.data.frame(do.call(rbind, med.resp.dat))
med.resp.dat <- rbind(med.resp.dat, med.resp.dat)
med.resp.dat <- cbind(sign = rep(c(1,-1), each = nrow(med.resp.dat)/2),
med.resp.dat)
med.resp.dat[kresp] <- as.data.frame(mapply(function(var, propn)
{
var <- propn * rep(c(1, -1), each = length(var)/2) * var
},
med.resp.dat[kresp], propn.types.med))
med.resp.dat[times] <- dae::as.numfac(unlist(med.resp.dat[times.factor]))
#Remove the times.factor
med.resp.dat <- med.resp.dat[,-match(times.factor, names(med.resp.dat))]
#Remove any missing values
med.resp.dat <- med.resp.dat[which(!is.na(med.resp.dat[kresp[1]])), ]
}
#Plot the median deviations for each trait
if (is.null(shape.values.med))
shape.values.med <- c(21:24,7,9,10,11,3,4)
plts <- list()
for (k in kresp)
{
plts[[k]] <- list()
if (is.allnull(plts.by))
levs.by <- "all"
else
levs.by <- levels(med.devn.dat$fac.by)
for (p in levs.by)
{
if (is.allnull(plts.by))
tmp <- med.devn.dat
else
tmp <- med.devn.dat[med.devn.dat$fac.by == p,]
if ("Method" %in% names(tmp))
tmp$Method <- with(tmp, dae::fac.recast(Method,
newlevels = substring(levels(Method),1,3)))
plts[[k]][[p]] <- ggplot(tmp, aes(x = .data[[!!times]], .data[[!!kresp.devn[k]]]),
...) +
ggfacet +
geom_hline(yintercept=0, linetype="solid", linewidth=0.5, colour = "maroon", alpha=0.7) +
setScaleTime(tmp[[times]], breaks.spacing.x = breaks.spacing.x) +
xlab(x.title) + ylab(y.titles[k]) + theme_bw() +
theme(strip.text = element_text(size=strip.text.size, face="bold"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(angle = angle.x),
panel.grid.major = element_line(colour = "grey60", linewidth = 0.5),
panel.grid.minor = element_line(colour = "grey80", linewidth = 0.5))
if (is.null(fac.group))
{
if (is.allnull(colour.values.med))
{
plts[[k]][[p]] <- plts[[k]][[p]] + geom_line (linewidth=0.4, alpha=alpha.med)
if (is.allnull(shape.values.med))
plts[[k]][[p]] <- plts[[k]][[p]] + geom_point(alpha=alpha.med, size=1.5)
else
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_point(shape=shape.values.med[1], alpha=alpha.med, size=1.5)
}
else
{
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_line (colour=colour.values.med[1], linewidth=0.4, alpha=alpha.med)
if (is.allnull(shape.values.med))
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_point(colour=colour.values.med[1],
fill=colour.values.med[1], alpha=alpha.med, size=1.5)
else
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_point(colour=colour.values.med[1], shape=shape.values.med[1],
fill=colour.values.med[1], alpha=alpha.med, size=1.5)
}
}
else
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_line (aes(colour = .data[[!!fac.group]]), linewidth=0.4, alpha=alpha.med) +
geom_point(aes(colour = .data[[!!fac.group]],
shape = .data[[!!fac.group]],
fill = .data[[!!fac.group]]),
alpha=alpha.med, size=1.5)
if (!(is.null(colour.values.med)))
plts[[k]][[p]] <- plts[[k]][[p]] + scale_colour_manual(values = colour.values.med)
if (!(is.null(shape.values.med)))
plts[[k]][[p]] <- plts[[k]][[p]] + scale_shape_manual(values = shape.values.med)
else
plts[[k]][[p]] <- plts[[k]][[p]] + scale_shape_manual(values = c(21:24,7,9,10,11))
if (!is.allnull(plts.group) && fac.group == "SmoothParams")
plts[[k]][[p]] <- plts[[k]][[p]] + guides(shape=guide_legend(title = "Smoothing\nparameters"),
fill=guide_legend(title = "Smoothing\nparameters"),
colour=guide_legend(title = "Smoothing\nparameters"))
if (!is.allnull(plts.by))
plts[[k]][[p]] <- plts[[k]][[p]] + ggtitle(paste("Plot for",p))
#Plot an envelope of the response median
if (propn.note.med && !is.null(propn.types.med))
{
if (is.allnull(plts.by))
med.resp.tmp <- med.resp.dat
else
med.resp.tmp <- med.resp.dat[med.resp.dat$fac.by == p, ]
if ("Method" %in% names(med.resp.tmp))
med.resp.tmp$Method <- with(med.resp.tmp,
dae::fac.recast(Method,
newlevels = substring(levels(Method),1,3)))
#Construct message to be plotted
if (propn.note.med)
{
xmin <- min(med.resp.tmp[times], na.rm = TRUE)
xrange <- max(med.resp.tmp[times], na.rm = TRUE) - xmin #not used at present
ymin <- min(med.resp.tmp[k], na.rm = TRUE)
envel <- data.frame(rep(xmin, 2),
c(ymin+2.75, ymin))
names(envel) <- c(times, kresp.devn[k])
ncol.x <- 1
if (all(facet.x.med != "."))
{
lastlevs <- lapply(facet.x.med,
function(fac, med.resp.tmp)
{
lastlev <- levels(med.resp.tmp[[fac]])
lastlev <- rep(factor(lastlev[length(lastlev)], levels = lastlev), 2)
}, med.resp.tmp = med.resp.tmp)
names(lastlevs) <- facet.x.med
envel <- cbind(envel, lastlevs)
}
if (all(facet.y.med != "."))
{
lastlevs <- lapply(facet.y.med,
function(fac, med.resp.tmp)
{
lastlev <- levels(med.resp.tmp[[fac]])
lastlev <- rep(factor(lastlev[length(lastlev)], levels = lastlev), 2)
}, med.resp.tmp = med.resp.tmp)
names(lastlevs) <- facet.y.med
envel <- cbind(envel, lastlevs)
}
if (ncol.x >3)
envel$lab <- c("Envelope:",
paste(propn.types.med[k],"of response median"))
else
{
envel <- envel[2,]
envel$lab <- paste("Envelope:", propn.types.med[k],"of response median")
}
}
#Plot the envelope
plts[[k]][[p]] <- plts[[k]][[p]] + geom_line(data = med.resp.tmp,
aes(y = .data[[!!k]],
group = .data[["sign"]]),
linetype="dashed")
if (propn.note.med)
plts[[k]][[p]] <- plts[[k]][[p]] +
geom_text(data = envel,
mapping = aes(x = .data[[!!times]], y = .data[[!!kresp.devn[k]]],
label = .data[["lab"]]),
hjust = 0, vjust=-Inf,
fontface = "plain", size = 3)
}
if (!is.null(ggplotFuncsMedDevn))
{
for(f in ggplotFuncsMedDevn)
plts[[k]][[p]] <- plts[[k]][[p]] + f
}
if (printPlot)
print(plts[[k]][[p]])
}
}
}
invisible(list(plots = plts, med.devn.dat = med.devn.dat))
}
"plotSmoothsComparison" <- function(data, response, response.smoothed = NULL,
individuals = "Snapshot.ID.Tag", times = "DAP",
trait.types = c("response", "AGR", "RGR"),
x.title = NULL, y.titles = NULL,
profile.plot.args = args4profile_plot(plots.by = NULL,
facet.x = ".", facet.y = ".",
include.raw = "no"),
printPlot = TRUE, ...)
{
profile.plot.args <- profile.plot.args
inargs <- list(...)
checkEllipsisArgs(c("plotSmoothsComparison","plotProfiles"), inargs)
plts <- NULL
#Check that plots are wanted
if (!is.allnull(profile.plot.args))
{
#Find out if any plotProfiles arguments that args4profile_plot handles have been supplied in '...'
if (length(inargs))
{
usedProfile.args <- formalArgs(args4profile_plot)
doubleargs <- intersect(names(inargs), usedProfile.args)
if (length(doubleargs))
stop("the 'plotProfiles' arguments ",paste0(doubleargs, collapse = ", "),
" conflict with 'args4profile_plot' arguments")
}
pltProfile.args <- NULL
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
#Get the options for the profile plots options from the list
plots.by.pf <- profile.plot.args$plots.by
facet.x.pf <- profile.plot.args$facet.x
facet.y.pf <- profile.plot.args$facet.y
include.raw.pf <- profile.plot.args$include.raw
collapse.facets.x.pf <- profile.plot.args$collapse.facets.x
collapse.facets.y.pf <- profile.plot.args$collapse.facets.y
facet.labeller <- profile.plot.args$facet.labeller
facet.scales.pf <- profile.plot.args$facet.scales
breaks.spacing.x <- profile.plot.args$breaks.spacing.x
angle.x <- profile.plot.args$angle.x
colour.pf <- profile.plot.args$colour
colour.column.pf <- profile.plot.args$colour.column
colour.values.pf <- profile.plot.args$colour.values
alpha.pf <- profile.plot.args$alpha
addMediansWhiskers.pf <- profile.plot.args$addMediansWhiskers
ggplotFuncsProfile <- profile.plot.args$ggplotFuncs
#Check include.raw.pf value
incl.raw.opt <- c("no", "alone", "facet.x", "facet.y")
incl.raw.opt <- incl.raw.opt[check.arg.values(include.raw.pf, options=incl.raw.opt)]
incl.raw <- incl.raw.opt != "no"
#Checking of the arguments that control the plots layout
checkLayoutArgs(data = data, plots.by.pf, plts.group = NULL, facet.x.pf, facet.y.pf)
plts.by <- plots.by.pf
#Check have a valid smooths.frame
validsmoothsframe <- validSmoothsFrame(data)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
checkPlotsArgs(data, plts.by = plts.by, facet.x = facet.x.pf, facet.y = facet.y.pf)
if (is.null(x.title))
x.title <- times
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
response.smooth <- response.smoothed
#Check that responses, response.smoothed, individuals and times are in data
checkNamesInData(c(response, response.smoothed, individuals, times), data = data)
addRates <- function(traits, response, sep = ".")
{
unlist(lapply(traits,
function(trait, response)
{
if (!("response" %in% trait))
response <- paste(response, trait, sep = sep)
return(response)
}, response = response))
}
kresp <- addRates(traits, response = response)
if (!all(kresp %in% names(data)))
stop("The following traits are not in the smooths.frame: ",
paste0(kresp[!(kresp %in% names(data))], collapse = ", "),
"; perhaps, trait.types needs to be set differently")
kresp.sm <- addRates(traits, response = response.smoothed)
names(kresp.sm) <- kresp
if (is.null(y.titles))
{
y.titles <- addRates(traits, response = response, sep = " ")
names(y.titles) <- kresp
} else
{
if (length(y.titles) != length(kresp))
stop("y.titles should be the same length as trait.types")
else
names(y.titles) <- kresp
}
data[times] <- convertTimes2numeric(data[[times]])
times.factor <- ".Time.fac"
data[times.factor] <- data[times]
data[times.factor] <- with(data, eval(parse(text =times)))
data[times.factor] <- factor(unlist(data[times.factor]),
labels = unique(data[times.factor])[order(unique(data[[times.factor]])),])
#Determine whether there are any smooth.cols on the facets - if not must be in plots.by.pf
smoothing.facets <- length(intersect(union(facet.x.pf, facet.y.pf), smooth.cols)) != 0
if (incl.raw.opt %in% c("facet.x", "facet.y") && all(c(facet.x.pf, facet.y.pf) == "."))
stop(paste0("The argument incl.raw is set to ", include.raw.pf,
", but ", include.raw.pf, " has not been set to include a variable"))
#Set up the facets
modfacet <- setupFacet(data = data, facet = facet.x.pf, collapse.facets = collapse.facets.x.pf,
combined.name = "Combined.x", smooth.cols = smooth.cols)
xfacet <- modfacet$newfacet
data <- modfacet$data
modfacet <- setupFacet(data = data, facet = facet.y.pf, collapse.facets = collapse.facets.y.pf,
combined.name = "Combined.y", smooth.cols = smooth.cols)
yfacet <- modfacet$newfacet
data <- modfacet$data
#Do the plots
plts <- list()
for (k in kresp)
{
plts[[k]] <- list()
if (incl.raw.opt == "alone")
{
#Get a single instance of the unsmoothed data
tmp <- split(data, data[smooth.cols])[[1]]
#Removing smoothing factors from facets
xfacet.tmp <- setdiff(xfacet, smooth.cols)
if (length(xfacet.tmp) == 0)
xfacet.tmp <- "."
yfacet.tmp <- setdiff(yfacet, smooth.cols)
if (length(yfacet.tmp) == 0)
yfacet.tmp <- "."
plts[[k]][["profiles"]][["Unsmoothed"]] <-
do.call(plotProfiles,
c(list(data = tmp, times = times, response = k,
individuals = individuals,
facet.x=xfacet.tmp, facet.y=yfacet,
labeller = facet.labeller, scales = facet.scales.pf,
breaks.spacing.x = breaks.spacing.x,
angle.x = angle.x,
colour = colour.pf,
colour.column = colour.column.pf,
colour.values = colour.values.pf,
alpha = alpha.pf,
title="Plot of unsmoothed response",
x.title = x.title, y.title = y.titles[k],
addMediansWhiskers = addMediansWhiskers.pf,
printPlot=FALSE,
ggplotFuncs = ggplotFuncsProfile),
pltProfile.args))
if (printPlot)
print(plts[[k]][["profiles"]][["Unsmoothed"]])
}
if (is.allnull(plts.by)) #all profiles in a single plot
levs.by <- "all"
else
{
data$plots.by.pf <- fac.mixcombine(data, plts.by, smooth.cols = smooth.cols)
data$plots.by.pf <- factor(data$plots.by.pf)
levs.by <- levels(data$plots.by.pf)
}
#Loop over plots.by.pf
for (by in levs.by)
{
{
if (is.allnull(plts.by))
{
title <- NULL
tmp1 <- data
} else
{
title <- paste0("Plot for ", by)
tmp1 <- data[data$plots.by.pf==by,]
if ("Combined.x" %in% names(tmp1))
tmp1["Combined.x"] <- factor(tmp1[["Combined.x"]])
if ("Combined.y" %in% names(tmp1))
tmp1["Combined.y"] <- factor(tmp1[["Combined.y"]])
}
xfacet.tmp <- xfacet
yfacet.tmp <- yfacet
if (incl.raw.opt %in% c("facet.x", "facet.y"))
{
if (incl.raw.opt == "facet.x") comb.name <- xfacet else comb.name <- yfacet
if (any(comb.name == "."))
{
comb.name <- ".Response"
tmp1[comb.name] <- factor("Smoothed")
if (incl.raw.opt == "facet.x") xfacet.tmp <- comb.name else yfacet.tmp <- comb.name
}
comb.name <- comb.name[length(comb.name)]
tmp2 <- tmp1
tmp2[kresp.sm[k]] <- tmp2[k]
tmp2[comb.name] <- "Raw"
levs <- c("Raw", levels(factor(tmp1[[comb.name]])))
tmp1 <- rbind(tmp2,tmp1)
tmp1[comb.name] <- factor(tmp1[[comb.name]], levels = levs)
}
plts[[k]][["profiles"]][[by]] <-
do.call(plotProfiles,
c(list(data = tmp1, times = times,
response = kresp.sm[k],
individuals = individuals,
facet.x = xfacet.tmp, facet.y = yfacet.tmp,
labeller = facet.labeller,
scales = facet.scales.pf,
breaks.spacing.x = breaks.spacing.x,
angle.x = angle.x,
colour = colour.pf,
colour.column = colour.column.pf,
colour.values = colour.values.pf,
alpha = alpha.pf,
title = title,
x.title = x.title, y.title = y.titles[k],
addMediansWhiskers = addMediansWhiskers.pf,
printPlot=FALSE,
ggplotFuncs = ggplotFuncsProfile),
pltProfile.args))
if (printPlot)
print(plts[[k]][["profiles"]][[by]])
}
}
}
}
invisible(plts)
}
#Function to fit splines, including possible boundary correction from Huang (2001)
ncsSpline <- function(vars, correctBoundaries = FALSE,
df, lambda, cv = FALSE, ...)
{
if (ncol(vars) != 2)
stop("Must supply a two-column matrix or data.frame")
if (!correctBoundaries)
{
if (missing(df))
{
if (missing(lambda))
fity <- smooth.spline(vars, all.knots=TRUE, ...)
else
fity <- smooth.spline(vars, all.knots=TRUE, lambda = lambda, ...)
} else
{
if (missing(lambda))
fity <- smooth.spline(vars, all.knots=TRUE, df=df, ...)
else
stop("Only one of df and lambda can be specified")
}
fit.spline <- list(x = fity$x,
y = fity$y,
lev = fity$lev,
lambda = fity$lambda,
df = fity$df,
uncorrected.fit = fity)
} else
{
nval <- nrow(vars)
W <- matrix(NA, nrow = nval, ncol = 4)
W2 <- matrix(NA, nrow = nval, ncol = 4)
W3 <- matrix(NA, nrow = nval, ncol = 4)
x <- vars[,1]
y <- vars[,2]
# construct the four polynomials
W[,1] <- x^2/2-x^4/4+x^5/10
W[,2] <- x^3/6-x^4/6+x^5/20
W[,3] <- x^4/4-x^5/10
W[,4] <- -x^4/12+x^5/20
if (missing(df))
{
if (!missing(lambda))
stop("lambda must not be set for correctBoundaries = TRUE")
lam <- vector(mode = "numeric", length = nval)
rss <- vector(mode = "numeric", length = nval)
tr <- vector(mode = "numeric", length = nval)
gcv <- vector(mode = "numeric", length = nval)
# use GCV to search for best lambda
for(i in 1:nval)
{
u <- -7+7.0*(i-1)/(nval-1)
lam[i] <- 10^u
slam <- lam[i]
# get regular fit for y and four polynomials
fity <- smooth.spline(x,y,all.knots=TRUE,spar=slam, ...)
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,spar=slam, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,spar=slam, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,spar=slam, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,spar=slam, ...)$y
#Apply boundary correction to the fitted spline
W2 <- W-W2
h <- solve(t(W2)%*%W2,t(W2)%*%(y-fity$y))
# get the newfit, rss and calculate the trace of new S
newfit <- fity$y+W2%*%h
rss[i] <- sum((newfit-y)^2)
W3[,1] <- smooth.spline(x,W2[,1],all.knots=TRUE,spar=slam, ...)$y
W3[,2] <- smooth.spline(x,W2[,2],all.knots=TRUE,spar=slam, ...)$y
W3[,3] <- smooth.spline(x,W2[,3],all.knots=TRUE,spar=slam, ...)$y
W3[,4] <- smooth.spline(x,W2[,4],all.knots=TRUE,spar=slam, ...)$y
W3 <- W2-W3
K <- solve(t(W2)%*%W2,t(W3))
tr[i] <- sum(fity$lev)+sum(diag(K%*%W2))
gcv[i] <- nval*rss[i]/(nval-tr[i])^2
}
# get the optimal lambda and apply to y and the four polynomials
lam.opt<- lam[order(gcv)[1]]
slam <- lam.opt
fity <- smooth.spline(x,y,all.knots=TRUE,spar=slam)
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,spar=slam, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,spar=slam, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,spar=slam, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,spar=slam, ...)$y
} else #df is specified
{
# get regular fit for y and four polynomials for specified df
fity <- smooth.spline(x,y,all.knots=TRUE,df=df, ...)
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,df=df, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,df=df, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,df=df, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,df=df, ...)$y
# calculate the trace of new S
W3[,1] <- smooth.spline(x,W2[,1],all.knots=TRUE,df=df, ...)$y
W3[,2] <- smooth.spline(x,W2[,2],all.knots=TRUE,df=df, ...)$y
W3[,3] <- smooth.spline(x,W2[,3],all.knots=TRUE,df=df, ...)$y
W3[,4] <- smooth.spline(x,W2[,4],all.knots=TRUE,df=df, ...)$y
W3 <- W2-W3
K <- solve(t(W2)%*%W2,t(W3))
}
#Apply boundary correction to the fitted spline
W2 <- W-W2
h <- solve(t(W2)%*%W2,t(W2)%*%(y-fity$y))
newy <- as.vector(fity$y+W2%*%h)
# get the newfit leverage values of new S
lev <- as.vector(fity$lev+diag(W2%*%K))
tr <- sum(lev)
rss <- sum((y - newy)^2)
lambda <- nval*rss/(nval-tr)^2
# Set up list to return
fit.spline <- list(x = fity$x,
y = newy,
lev = lev,
lambda = lambda,
df = fity$df,
uncorrected.fit = fity)
if (!missing(df))
fit.spline$df <- df
}
class(fit.spline) <- "ncsSpline"
return(fit.spline)
}
predict.ncsSpline <- function(object, x, correctBoundaries = FALSE,
df, cv = FALSE, ...)
{
if (!inherits(object, what = "ncsSpline"))
stop("Must supply a an object of class ncsSpline")
fit <- predict(object$uncorrected.fit, x = x)
#Correct boundaries
if (correctBoundaries)
{
nval <- length(x)
W <- matrix(NA, nrow = nval, ncol = 4)
W2 <- matrix(NA, nrow = nval, ncol = 4)
vars <- data.frame(x = object$uncorrected.fit$x,
yin = object$uncorrected.fit$yin)
vars <- merge(as.data.frame(fit), vars, all.x = TRUE)
vars$yin[is.na(vars$yin)] <- vars$y[is.na(vars$yin)]
vars <- vars[order(vars$x), ]
x <- vars$x
# construct the four polynomials
W[,1] <- x^2/2-x^4/4+x^5/10
W[,2] <- x^3/6-x^4/6+x^5/20
W[,3] <- x^4/4-x^5/10
W[,4] <- -x^4/12+x^5/20
if (missing(df))
{
slam <- object$lambda
# get regular fit for y and four polynomials
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,spar=slam, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,spar=slam, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,spar=slam, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,spar=slam, ...)$y
} else #df is specified
{
# get regular fit for y and four polynomials for specified df
W2[,1] <- smooth.spline(x,W[,1],all.knots=TRUE,df=df, ...)$y
W2[,2] <- smooth.spline(x,W[,2],all.knots=TRUE,df=df, ...)$y
W2[,3] <- smooth.spline(x,W[,3],all.knots=TRUE,df=df, ...)$y
W2[,4] <- smooth.spline(x,W[,4],all.knots=TRUE,df=df, ...)$y
}
#Apply boundary correction to the fitted spline
W2 <- W-W2
h <- solve(t(W2)%*%W2,t(W2)%*%(vars$yin-vars$y))
fit <- list(x = vars$x,
y = as.vector(vars$y+W2%*%h))
}
return(fit)
}
#Functions to fit P-splines using JOPS
pSpline <- function(vars, npspline.segments, lambda = NULL, ...)
{
if (ncol(vars) != 2)
stop("Must supply a two-column matrix or data.frame")
fity <- JOPS::psNormal(x = vars[[1]], y = vars[[2]], nseg = npspline.segments, lambda = lambda,
xgrid = vars[[1]])
fit.spline <- list(x = fity$x,
y = as.vector(fity$muhat),
lev = NULL,
lambda = fity$lambda,
df = fity$effdim,
npspline.segments = npspline.segments,
uncorrected.fit = fity)
class(fit.spline) <- "PSpline"
class(fit.spline$uncorrected.fit) <- "pspfit"
return(fit.spline)
}
predict.pSpline <- function(object, x, npspline.segments, deriv = 0, ...)
{
fit.obj <- object$uncorrected.fit
bdeg <- fit.obj$bdeg
xmin <- min(x, na.rm = TRUE)
xmax <- max(x, na.rm = TRUE)
if (!inherits(object, what = "PSpline"))
stop("Must supply a an object of class PSpline")
if (deriv == 0)
{
preds <- predict(fit.obj, x = x, type = "mu")
fit <- list(x = x, y = preds)
} else #obtain a derivative of order deriv based on Eqn 2.15 from Eilers and Marx (2021) JOPS
{
if ((bdeg - deriv) <= 0)
stop("The degree of the spline (3) is insufficient to compute a derivative of order ", deriv)
alpha <- as.vector(fit.obj$pcoef)
alphaDeriv <- diff(alpha, differences = deriv) / (((xmax - xmin)/fit.obj$nseg)^deriv)
Bderiv <- JOPS::bbase(x, xl = xmin, xr = xmax, nseg = fit.obj$nseg, bdeg = (bdeg - deriv))
fit <- as.vector(Bderiv %*% alphaDeriv)
fit <- list(x = x, y = fit)
}
return(fit)
}
#Function to fit a spline using smooth.spline or JOPS
"smoothSpline" <- function(data, response, response.smoothed = NULL, x,
smoothing.method = "direct",
spline.type = "NCSS", df=NULL, lambda = NULL,
npspline.segments = NULL, correctBoundaries = FALSE,
rates = NULL, suffices.rates = NULL, sep.rates = ".",
extra.derivs = NULL, suffices.extra.derivs=NULL,
na.x.action = "exclude", na.y.action = "trimx", ...)
{
#This table has been made obsolete with the introduction of extra.rate
# Result deriv suffix.deriv AGR RGR
# direct smoothing
# AGR, RGR 1 AGR NULL RGR
# AGR 1 AGR NULL NULL
# RGR 1 NULL NULL RGR
# log-smoothing
# AGR, RGR 1 RGR AGR NULL
# AGR 1 NULL AGR NULL
# RGR 1 RGR NULL NULL
#check input arguments
impArgs <- match.call()
if ("na.rm" %in% names(impArgs))
stop("na.rm has been deprecated; use na.x.action and na.y.action")
if ("smoothing.scale" %in% names(impArgs))
stop("smoothing.scale has been deprecated; use smoothing.method")
#Check that required cols are in data
checkNamesInData(c(response, x), data = data)
smethods <- c("direct", "logarithmic")
smethod <- smethods[check.arg.values(smoothing.method, options=smethods)]
stype <- c("NCSS", "PS")
stype <- stype[check.arg.values(spline.type, options=stype)]
if (is.null(lambda) && stype == "PS")
stop("Must specify lambda for spline.type set to PS")
if (!is.null(df) && !is.null(lambda) && stype == "NCSS")
stop("Only one of df and lambda can be specified for spline.type NCSS")
na.x <- na.y <- c("exclude", "omit", "fail")
na.y <- c(na.y, "allx", "trimx", "ltrimx", "utrimx")
na.act.x <- na.x[check.arg.values(na.x.action, options=na.x)]
na.act.y <- na.y[check.arg.values(na.y.action, options=na.y)]
options <- c("AGR", "PGR", "RGR")
if (is.allnull(rates))
grates <- NULL
else
grates <- options[unlist(lapply(rates, check.arg.values, options=options))]
if (correctBoundaries && !is.allnull(grates))
stop("Unable to correctBoundaries when rates is not NULL")
if (!is.allnull(grates) && "PGR" %in% grates)
stop("PGR is not available when rates are based on derivatives")
if (!is.null(suffices.rates) & length(grates) != length(suffices.rates))
stop("The length of of rates and suffices.rates should be equal")
if (!is.allnull(grates) && !is.null(extra.derivs) && (1 %in% extra.derivs))
stop("when rates is not NULL, 1 should not be included in extra.derivs")
if (is.null(suffices.rates))
suffices.rates <- grates
names(suffices.rates) <- grates
if (!is.null(extra.derivs) & !is.null(suffices.extra.derivs))
if (length(extra.derivs) != length(extra.derivs))
stop("The number of names supplied must equal the number of derivatives specified")
#Determine what is required from spline fitting
if (!is.allnull(grates))
{
derivs = 1
if (smethod == "direct")
{
if (!("AGR" %in% grates))
suffices.derivs <- "_tmp"
else
{
if (is.null(suffices.rates))
suffices.derivs <- "AGR"
else
suffices.derivs <- suffices.rates["AGR"]
}
names(suffices.derivs) <- "AGR"
if ("RGR" %in% grates)
{
extra.rate <- "RGR"
names(extra.rate) <- suffices.rates["RGR"]
}
else
extra.rate <- NULL
} else
{
if (!("RGR" %in% grates))
suffices.derivs <- "_tmp"
else
{
if (is.null(suffices.rates))
suffices.derivs <- "RGR"
else
suffices.derivs <- suffices.rates["RGR"]
}
names(suffices.derivs) <- "RGR"
if ("AGR" %in% grates)
{
extra.rate <- "AGR"
names(extra.rate) <- suffices.rates["AGR"]
}
else
extra.rate <- NULL
}
} else
{
derivs = NULL
suffices.derivs <- NULL
extra.rate <- NULL
}
if (!is.null(extra.derivs))
{
derivs <- unique(c(derivs, extra.derivs))
suffices.derivs <- c(suffices.derivs, suffices.extra.derivs)
names(derivs) <- suffices.derivs
}
tmp <- as.data.frame(data)
#Transform data, if required
if (smethod == "logarithmic")
tmp[[response]] <- log(tmp[[response]])
#Convert any infinite values to missing
if (any(is.infinite(tmp[[response]])))
{
tmp[[response]][is.infinite(tmp[[response]])] <- NA
warning("Some infinite values have been converted to missing")
}
#Set up fit data.frame
if (is.null(response.smoothed))
response.smoothed <- paste0("s", response)
fit.names <- c(x, response.smoothed)
if (!is.allnull(derivs))
{
if (is.allnull(suffices.derivs))
fit.names <- c(fit.names, paste(response.smoothed,".dv",derivs,sep=""))
else
fit.names <- c(fit.names, paste(response.smoothed, suffices.derivs, sep=sep.rates))
}
#Process missing values
#tmp will have no missing values and is what is used in the fitting
#data retains missing values and is used to obtain the returned data.frame
#x.pred has the x-values for which predictions are required
# - it should include all the x values that are returned by smooth.spline;
# it will not include any x values that are missing, but may include
# x values for which there are missing y values, depending on the settings
# of na.y.action, and these x values will not have been supplied to smooth.spline.
nobs <- nrow(tmp)
if (nobs == 0)
{
warning("A response with no data values supplied")
} else
{
if (na.act.x == "fail")
stop("na.x.action is to set to fail and there are missing x values")
else #remove any observations with missing x values
{
if (na.act.x %in% c("omit", "exclude"))
{
tmp <- tmp[!is.na(tmp[[x]]), ]
if (na.act.x == "omit")
data <- tmp
}
}
x.pred <- tmp[[x]]
if (na.act.y == "fail")
stop("na.y.action is to set to fail and there are missing y values")
else #Are there any missing response values now
{
if (na.act.y %in% c("omit", "exclude"))
{
x.pred <- tmp[!is.na(tmp[[response]]), ][[x]]
} else
{
if (grepl("trimx", na.act.y, fixed = TRUE))
{
tmp <- tmp[order(tmp[[x]]), ]
y.nonmiss <- c(1:nrow(tmp))[!is.na(tmp[[response]])]
x.pred <- tmp[[x]]
if (length(y.nonmiss) > 0)
{
if (na.act.y %in% c("trimx", "ltrimx"))
{
x.pred <- x.pred[y.nonmiss[1]:length(tmp[[x]])]
y.nonmiss <- y.nonmiss - y.nonmiss[1] + 1
}
if (na.act.y %in% c("trimx", "utrimx"))
x.pred <- x.pred[1:y.nonmiss[length(y.nonmiss)]]
y.nonmiss <- diff(y.nonmiss)
if (any(y.nonmiss >= 3))
warning(paste("smoothSpline has found runs of 3 or more contiguous",
"missing y-values - is the current na.y.action setting,",
paste0("`", na.act.y,"', appropriate?")))
} else
x.pred <- NULL
}
}
tmp <- tmp[!is.na(tmp[[response]]), ]
if (na.act.y == "omit")
data <- tmp
}
}
#What are the distinct x values
distinct.xvals <- sort(unique(tmp[[x]]))
tol <- 1e-06 * IQR(distinct.xvals)
distinct.xvals <- remove.repeats(distinct.xvals, tolerance = tol)
if (length(distinct.xvals) < 4)
{
#< 4 distinct values and so all fitted values are set to NA
warning(paste("Need at least 4 distinct x values to fit a spline",
"- all fitted values set to NA", sep = " "))
#Set up fit data.frame
if (is.null(response.smoothed))
response.smoothed <- paste(response,"smooth",sep=".")
fit.names <- c(x, response.smoothed)
if (!is.allnull(derivs))
{
if (is.allnull(suffices.derivs))
fit.names <- c(fit.names, paste(response.smoothed,".dv",derivs,sep=""))
else
fit.names <- c(fit.names, paste(response.smoothed, suffices.derivs, sep=sep.rates))
}
#Add extra,rate if required
if (!is.null(extra.rate))
fit.names <- c(fit.names, paste(response.smoothed, names(extra.rate), sep=sep.rates))
fit <- as.data.frame(matrix(NA, nrow=nrow(data), ncol = length(fit.names)))
colnames(fit) <- fit.names
fit[x] <- data[[x]]
fit.spline <- NULL
} else
{
#smooth and obtain predictions corresponding to x.pred
fitcorrectBoundaries <- correctBoundaries
if (stype == "NCSS" && length(distinct.xvals) <= 5 && correctBoundaries)
{
warning(paste("Need more than 5 distinct x values to correct the end-points of a spline",
"- no corrections made", sep = " "))
fitcorrectBoundaries <- FALSE
}
if (stype == "NCSS")
{
if (is.null(df))
{
if (is.null(lambda))
fit.spline <- ncsSpline(tmp[c(x, response)], correctBoundaries = fitcorrectBoundaries,
...)
else
fit.spline <- ncsSpline(tmp[c(x, response)], correctBoundaries = fitcorrectBoundaries,
lambda = lambda, ...)
} else
if (is.null(lambda))
fit.spline <- ncsSpline(tmp[c(x, response)], correctBoundaries = fitcorrectBoundaries,
df = df, ...)
} else #PS
{
#Determine npspline.segments for a full set of x
if (is.null(npspline.segments))
npspline.segments <- max(10, ceiling((nrow(data)-1)/2))
#Adjust for the missing values in x
nfit <- length(distinct.xvals)
if (nfit < nobs)
{
nperseg <- nobs/npspline.segments
npspline.segments <- ceiling(nfit/nperseg)
}
fit.spline <- pSpline(tmp[c(x, response)], npspline.segments = npspline.segments, lambda = lambda, ...)
}
x.pred <- remove.repeats(sort(x.pred), tolerance = tol)
fit <- NULL
if (length(x.pred) == length(fit.spline$x))
{
if (all(abs(x.pred - fit.spline$x) < tol))
{
if (stype == "NCSS")
fit <- list(fit.spline$x, fit.spline$y)
else
fit <- list(fit.spline$x, fit.spline$y)
}
}
#Need to refit for current x.pred
if (is.null(fit))
{
if (stype == "NCSS")
fit <- predict.ncsSpline(fit.spline, x = x.pred,
correctBoundaries = fitcorrectBoundaries)
else
fit <- predict.pSpline(fit.spline, x = x.pred,
npspline.segments = fit.spline$uncorrected.fit$npspline.segments)
}
rsmooth <- response.smoothed
names(fit) <- c(x, rsmooth)
#backtransform if transformed
if (smethod == "logarithmic")
fit[[rsmooth]] <- exp(fit[[rsmooth]])
#get derivatives if required
if (!correctBoundaries & !is.null(derivs))
{
for (d in derivs)
{
if (is.null(suffices.derivs))
rsmooth.dv <- paste0(response.smoothed,".dv",d)
else
{
k <- ifelse(length(derivs) == 1, 1, match(d, derivs))
rsmooth.dv <- paste(response.smoothed, suffices.derivs[k], sep=sep.rates)
}
if (stype == "NCSS")
fit[[rsmooth.dv]] <- predict(fit.spline$uncorrected.fit, x = x.pred, deriv=d)$y
else
fit[[rsmooth.dv]] <- predict.pSpline(fit.spline, x = x.pred,
npspline.segments = fit.spline$uncorrected.fit$npspline.segments,
deriv=d)$y
}
#Add RGR if required
if (!is.null(extra.rate) && extra.rate == "RGR")
{
#Check have the required computed derivative
if (is.null(suffices.derivs))
rsmooth.dv <- paste0(response.smoothed,".dv",1)
else
rsmooth.dv <- paste(response.smoothed, suffices.derivs["AGR"], sep=sep.rates)
#get the extra derivative
if (!(rsmooth.dv %in% names(fit)))
stop("First derivative not available to calculate RGR")
fit[[paste(rsmooth,names(extra.rate),sep=sep.rates)]] <- fit[[rsmooth.dv]]/fit[[rsmooth]]
}
#Add AGR if required
if (!is.null(extra.rate) && extra.rate == "AGR")
{
#Check have the required computed derivative
if (is.null(suffices.derivs))
rsmooth.dv <- paste0(response.smoothed,".dv",1)
else
rsmooth.dv <- paste(response.smoothed, suffices.derivs["RGR"], sep=sep.rates)
#get the extra derivative
if (!(rsmooth.dv %in% names(fit)))
stop("First derivative not available to calculate AGR")
fit[[paste(rsmooth,names(extra.rate),sep=sep.rates)]] <- fit[[rsmooth.dv]]*fit[[rsmooth]]
}
}
fit <- as.data.frame(fit)
#Remove temporary rate, if there are any
if (length(grep("._tmp", names(fit), fixed = TRUE)))
fit <- fit[, -grep("._tmp", names(fit), fixed = TRUE)]
#Merge data and fit, preserving x-order in data
x.ord <- order(data[[x]])
fit <- merge(data[c(x,response)],fit, all.x = TRUE, sort = FALSE)
fit <- fit[,-match(response, names(fit))]
fit <- fit[order(fit[[x]]),]
fit[x.ord,] <- fit
}
rownames(fit) <- NULL
return(list(predictions = fit, fit.spline = fit.spline))
}
"probeSmooths" <- function(data, response = "PSA", response.smoothed = NULL,
individuals="Snapshot.ID.Tag", times = "DAP",
keep.columns = NULL,
get.rates = TRUE,
rates.method="differences", ntimes2span = NULL,
trait.types = c("response", "AGR", "RGR"),
smoothing.args =
args4smoothing(smoothing.methods = "direct",
spline.types = "NCSS",
df = NULL, lambdas = NULL),
x.title = NULL, y.titles = NULL, which.plots = "profiles",
profile.plot.args =
args4profile_plot(plots.by = NULL,
facet.x = ".", facet.y = ".",
include.raw = "no"),
meddevn.plot.args =
args4meddevn_plot(plots.by = NULL, plots.group = NULL,
facet.x = ".", facet.y = ".",
propn.note = TRUE,
propn.types = c(0.1, 0.5, 0.75)),
devnboxes.plot.args =
args4devnboxes_plot(plots.by = NULL,
facet.x = ".", facet.y = ".",
which.plots = "none"),
...)
{
smoothing.args <- smoothing.args
profile.plot.args <- profile.plot.args
meddevn.plot.args <- meddevn.plot.args
#check input arguments
impArgs <- match.call()
if ("na.rm" %in% names(impArgs))
stop("na.rm has been deprecated; use na.x.action and na.y.action")
if ("smoothing.scales" %in% names(impArgs))
stop("smoothing.scales has been deprecated; use smoothing.methods")
if ("deviations.boxplots" %in% names(impArgs))
stop("deviations.boxplots has been deprecated; use which.plots")
inargs <- list(...)
checkEllipsisArgs(c("probeSmooths","plotProfiles"), inargs)
smooth.cols <- c("Type","TunePar","TuneVal","Tuning","Method")
data[times] <- convertTimes2numeric(data[[times]])
#Deal with plot arguments
options <- c("none", "profiles", "absolute.boxplots", "relative.boxplots", "medians.deviations")
plots <- options[unlist(lapply(which.plots, check.arg.values, options=options))]
if ("none" %in% plots & length(plots) > 1)
plots <- "none"
if (is.null(x.title))
x.title <- times
if (!is.allnull(smoothing.args) && smoothing.args$combinations == "single")
{
if (any(unlist(lapply(names(smoothing.args)[1:4],
function(x, smth.args) length(smth.args[x]) > 1,
smth.args = smoothing.args))))
stop("All of the components of smoothing.args must be single-valued when combinations is single")
if (is.null(smoothing.args$df)) smoothing.args$df <- NA
if (is.null(smoothing.args$lambdas)) smoothing.args$lambdas <- NA
}
#Get the options for the profile plots options from the list
plots.by.pf <- profile.plot.args$plots.by
facet.x.pf <- profile.plot.args$facet.x
facet.y.pf <- profile.plot.args$facet.y
include.raw.pf <- profile.plot.args$include.raw
collapse.facets.x.pf <- profile.plot.args$collapse.facets.x
collapse.facets.y.pf <- profile.plot.args$collapse.facets.y
facet.labeller <- profile.plot.args$facet.labeller
scales.pf <- profile.plot.args$scales
breaks.spacing.x.pf <- profile.plot.args$breaks.spacing.x
colour.pf <- profile.plot.args$colour
colour.column.pf <- profile.plot.args$colour.column
colour.values.pf <- profile.plot.args$colour.values
alpha.pf <- profile.plot.args$alpha
addMediansWhiskers.pf <- profile.plot.args$addMediansWhiskers
ggplotFuncsProfile <- profile.plot.args$ggplotFuncs
plts.by <- plots.by.pf
#Get the options for the median deviations plots options from the list
plots.by.med <- meddevn.plot.args$plots.by
plots.group.med <- meddevn.plot.args$plots.group
facet.x.med <- meddevn.plot.args$facet.x
facet.y.med <- meddevn.plot.args$facet.y
facet.labeller = meddevn.plot.args$facet.labeller
facet.scales.med <- meddevn.plot.args$facet.scales
breaks.spacing.x.med <- meddevn.plot.args$breaks.spacing.x
colour.values.med <- meddevn.plot.args$colour.values
shape.values.med <- meddevn.plot.args$shape.values
alpha.med <- meddevn.plot.args$alpha
propn.note.med <- meddevn.plot.args$propn.note
propn.types.med <- meddevn.plot.args$propn.types
ggplotFuncsMedDevn <- meddevn.plot.args$ggplotFuncs
plts.by.med <- plots.by.med
plts.group.med <- plots.group.med
#Get the options for the deviations boxplots options from the list
plots.by.box <- devnboxes.plot.args$plots.by
facet.x.box <- devnboxes.plot.args$facet.x
facet.y.box <- devnboxes.plot.args$facet.y
include.raw.box <- devnboxes.plot.args$include.raw
collapse.facets.x.box <- devnboxes.plot.args$collapse.facets.x
collapse.facets.y.box <- devnboxes.plot.args$collapse.facets.y
facet.labeller <- devnboxes.plot.args$facet.labeller
scales.box <- devnboxes.plot.args$scales
breaks.spacing.x <- devnboxes.plot.args$breaks.spacing.x
ggplotFuncsDevnBoxes <- devnboxes.plot.args$ggplotFuncs
#Checking of the arguments that control the plots layout for boxplots
if (any(c("absolute.boxplots", "relative.boxplots") %in% plots))
checkLayoutArgs(data = NULL, plots.by.box, plts.group = NULL, facet.x.box, facet.y.box)
#Get columns need for facets
id.cols <- colour.column.pf
if (all(facet.x.pf != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.x.pf))
if (all(facet.x.med != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.x.med))
if (all(facet.x.box != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.x.box))
if (all(facet.y.pf != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.pf))
if (all(facet.y.med != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.med))
if (all(facet.y.box != "."))
id.cols <- c(id.cols, fac.getinFormula(facet.y.box))
id.cols <- c(individuals, times, response, keep.columns, id.cols)
#Set up name for smoothed response
if (is.null(response.smoothed))
response.smooth <- paste0("s", response)
else
response.smooth <- response.smoothed
responses.smooth <- response.smooth
#Argument for what traits are to be plotted
options <- c("response", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
traits <- c("response", "AGR", "RGR")
grates <- c("AGR","RGR")[c("AGR","RGR") %in% traits]
if (is.smooths.frame(data))
{
#Check that required cols are in data
checkNamesInData(unique(id.cols), data = data)
smth <- data
if (!is.allnull(smoothing.args))
{
#Extract smooths specified by smoothing.args from smth
if (is.allnull(smoothing.args$df) && is.allnull(smoothing.args$lambdas))
stop("It must be that at least one of df and lambda is not NULL in smoothing.args")
#Get the smoothing arguments
options <- c("allvalid", "parallel", "single")
comb.opt <- options[check.arg.values(smoothing.args$combinations, options=options)]
smethods = smoothing.args$smoothing.methods
stypes = smoothing.args$spline.types
df = smoothing.args$df
lambdas = smoothing.args$lambdas
#Construct the set of schemes for which smooths are to be generated
spar.schemes <- makeSmoothSchemes(combinations = comb.opt,
smethods = smethods, stypes = stypes,
df = df, lambdas = lambdas)
#Convert smoothing combinations to factors, paying attention to levels order
spar.schemes[c("Type","TunePar","TuneVal","Tuning","Method")] <-
lapply(spar.schemes[c("Type","TunePar","TuneVal","Tuning","Method")],
function(x) factor(x, levels = as.character(unique(x))))
selection <- levels(with(spar.schemes, fac.combine(list(Type,TunePar,TuneVal,Method),
combine.levels = TRUE, sep = "-")))
combos.fac <- (with(smth, fac.combine(list(Type,TunePar,TuneVal,Method),
combine.levels = TRUE, sep = "-")))
combos <- levels(combos.fac)
if (!all(selection %in% combos))
stop("Not all combinations of the values of smoothing parameters specified by smoothing.args ",
"amongst those for the set of smooths in data")
#Get subset
tmp <- split(smth, combos.fac)
names(tmp) <- combos
smth <- lapply(selection, function(seln, tmp) tmp[[seln]], tmp = tmp)
smth <- do.call(rbind, smth)
smth[c("Type","TunePar","TuneVal","Method")] <- lapply(smth[c("Type","TunePar","TuneVal","Method")], factor)
smth <- as.smooths.frame(smth, individuals, times)
}
} else
{
if (is.allnull(smoothing.args))
stop("data is not a smooths.frame and smoothing.args is NULL so that there are no smooths available")
#Deal with data arguments for a data.frame
#Argument for which response the rates are to be computed
options <- c("none", "raw","smoothed")
if (is.logical(get.rates))
{
if (get.rates)
get.which <- c("raw","smoothed")
else
get.which <- "none"
}
else
get.which <- options[unlist(lapply(get.rates, check.arg.values, options=options))]
if (length(grates) == 0 && !("none" %in% get.which))
{
get.which <- "none"
warning("trait.types does not include AGR or RGR and so get.rates has been set to none")
} else
{
if (length(traits) > 1 || traits != "response")
{
if ("none" %in% get.which || !("smoothed" %in% get.which))
{
traits <- "response"
grates <- c("AGR","RGR")[c("AGR","RGR") %in% traits]
propn.types.med <- propn.types.med[1]
warning(paste("The calculation of smoothed growth rates have not been specified;",
"trait.types changed to response and propn.type reduced to its first element"))
}
}
}
if (!("raw" %in% get.which) && length(grates) > 0) #Check if rates are needed but not being obtained.
{
raw.rates <- paste(response, grates, sep = ".")
if (all(raw.rates %in% names(data)))
id.cols <- c(id.cols, raw.rates)
}
options <- c("differences","derivatives")
ratemeth.opt <- options[check.arg.values(rates.method, options=options)]
if ("smoothed" %in% get.which && is.null(ntimes2span))
{
if (ratemeth.opt == "differences")
ntimes2span <- 2
if (ratemeth.opt == "derivatives")
ntimes2span <- 3
}
#Get the smoothing arguments
smethods = smoothing.args$smoothing.methods
stypes = smoothing.args$spline.types
df = smoothing.args$df
lambdas = smoothing.args$lambdas
smoothing.segments = smoothing.args$smoothing.segments
npspline.segments = smoothing.args$npspline.segments
na.x.action = smoothing.args$na.x.action
na.y.action = smoothing.args$na.y.action
external.smooths = smoothing.args$external.smooths
correctBoundaries = smoothing.args$correctBoundaries
if ((is.allnull(df) && is.allnull(lambdas)))
stop("It must be that at least one of df and lambdas is not NULL in smoothing.args")
if (length(npspline.segments) > 1)
{
if (is.null(smoothing.segments))
stop("npspline.segments must be of length one in an unsegmented spline fit")
else
{
if (length(npspline.segments) != length(smoothing.segments))
stop("the number of values of npspline.segments should be one or ",
"equal to the number of segments in a segmented spline fit")
}
if (!all(diff(unlist(smoothing.segments)) > 0))
stop("the smoothing.segments are not a set of non-overlapping, successive intervals")
}
#Check that for overlapping smoothing segments and give an error or a warning depending on get.rates
if (!all(diff(unlist(smoothing.segments)) > 0))
{
if (!("none" %in% get.which))
stop("rates.method must be `none` when times values occur in more than one smoothing segment")
else
warning("The values for some times occur in multiple smoothing segments and so some individuals ",
"will have multiple rows in the returned data.frame, one for each segment in which the ",
"times occur.")
}
v <- unique(id.cols)
v <- setdiff(v, c("Type","TunePar","TuneVal","Tuning","Method")) #remove names yet to come
#Check that required cols are in data
checkNamesInData(v, data = data)
#Check that there is no more than one observation for each individuals-times combinations
if (!all(table(data[c(individuals, times)]) <= 1))
stop("There is more than one observation for one or more combinations of the individuals and times")
tmp <- data[v]
times.diffs.in.data <- paste0(times, ".diffs") %in% names(data)
#Form raw growth rates when get.rates includes raw.
#Do for all data even if segmented so that only the observation for the very first time is NA
if ("raw" %in% get.which)
tmp <- byIndv4Times_GRsDiff(data = tmp, response,
individuals=individuals,
times=times, avail.times.diffs = FALSE,
which.rates = grates, ntimes2span = ntimes2span)
#Construct the set of schemes for which smooths are to be generated
spar.schemes <- makeSmoothSchemes(combinations = smoothing.args$combinations,
smethods = smethods, stypes = stypes,
df = df, lambdas = lambdas)
#Generate the smooths
if (is.allnull(smoothing.segments))
smth <- smoothSchemes(tmp = tmp, spar.schemes = spar.schemes,
response = response, response.smooth = response.smooth,
times=times, ntimes2span = ntimes2span,
individuals = individuals, traits = traits,
get.rates = ("smoothed" %in% get.which),
ratemeth.opt = ratemeth.opt, grates = grates,
nseg = npspline.segments, correctBoundaries = correctBoundaries,
na.x.action = na.x.action, na.y.action = na.y.action)
else
{
knseg <- npspline.segments[1]
smth <- data.frame()
for (k in 1:length(smoothing.segments))
{
segm <- smoothing.segments[[k]]
subdat <- tmp[(tmp[times] >= segm[1]) & (tmp[times] <= segm[2]),]
if (length(npspline.segments) > 1) knseg <- npspline.segments[k]
#only get smooths when difference growth rates are required and ntimes2span is 2
smth <- rbind(smth,
smoothSchemes(tmp = subdat, spar.schemes = spar.schemes,
response = response, response.smooth = response.smooth,
times=times, ntimes2span = ntimes2span,
individuals = individuals, traits = traits,
get.rates = (ntimes2span == 2 &&
("smoothed" %in% get.which) &&
ratemeth.opt == "differences"),
ratemeth.opt = ratemeth.opt, grates = grates,
nseg = knseg, correctBoundaries = correctBoundaries,
na.x.action = na.x.action, na.y.action = na.y.action))
}
smth <- smth[do.call(order, smth), ]
#get overall difference growth rates for ntimes2apn == 2
if (ntimes2span == 2 && ("smoothed" %in% get.which) && ratemeth.opt == "differences")
{
smth <- split(smth, fac.combine(as.list(smth[c("Type","Tuning","Method")])))
smth <- lapply(smth, byIndv4Times_GRsDiff, responses = response.smooth,
individuals = individuals,
times=times, ntimes2span = ntimes2span,
which.rates = grates)
smth <- do.call(rbind, smth)
smth <- smth[do.call(order, smth), ]
}
}
#Add external.smooths, if required
if (!is.null(external.smooths))
{
#Determine which smoothing-parameter columns are in external.smooths & check have required other columns
external.smooths <- as.data.frame(external.smooths)
smth.cols <- intersect(smooth.cols, names(external.smooths))
if (length(smth.cols) == 0)
stop("No smoothing parameter columns have been included in ", deparse(substitute(external.smooths)))
if ("raw" %in% get.which)
{
ext.nam <- names(external.smooths)
tmp <- split(external.smooths, external.smooths[smth.cols])
tmp <- lapply(tmp, byIndv4Times_GRsDiff, responses = response, individuals=individuals,
times=times, avail.times.diffs = FALSE,
which.rates = grates, ntimes2span = ntimes2span)
external.smooths <- do.call(rbind, tmp)
external.smooths <- external.smooths[c(ext.nam, setdiff(names(external.smooths), ext.nam))]
external.smooths <- external.smooths[do.call(order, external.smooths),]
}
if ("smoothed" %in% get.which)
{
ext.nam <- names(external.smooths)
tmp <- split(external.smooths, external.smooths[smth.cols])
tmp <- lapply(tmp, byIndv4Times_GRsDiff, responses = response.smooth, individuals=individuals,
times=times, avail.times.diffs = FALSE,
which.rates = grates, ntimes2span = ntimes2span)
external.smooths <- do.call(rbind, tmp)
external.smooths <- external.smooths[c(ext.nam, setdiff(names(external.smooths), ext.nam))]
external.smooths <- external.smooths[do.call(order, external.smooths),]
}
extra.vars <- setdiff(names(smth), smooth.cols)
if (all(extra.vars %in% names(external.smooths)))
external.smooths <- external.smooths[c(smth.cols, extra.vars)]
else
stop(paste("Do not have the following required columns in data: ",
paste(extra.vars[!(extra.vars %in% names(external.smooths))],collapse=", "), "\n", sep=""))
#Add missing smoothing-parameter columns
smth.cols <- setdiff(smooth.cols, smth.cols)
smooth.pars <- rep("Other", length(smth.cols))
names(smooth.pars) <- smth.cols
smooth.pars <- rbind(smooth.pars)
rownames(smooth.pars) <- NULL
tmp <- cbind(smooth.pars, external.smooths)
tmp <- tmp[names(smth)]
#Add extra smooths to smth
smth <- rbind(smth,tmp)
#Update the smoothing-parameter schemes
tmp <- split(tmp, dae::fac.combine(as.list(lapply(tmp[smooth.cols], as.factor)),
combine.levels = TRUE, sep = "-"))
sch <- lapply(tmp, function(x) x[1, smooth.cols])
sch <- do.call(rbind, sch)
rownames(sch) <- NULL
spar.schemes <- rbind(spar.schemes, sch[colnames(spar.schemes)])
}
#Form a smooths.frame and check that it is valid
smth <- as.smooths.frame(smth, individuals, times)
}
validsmoothsframe <- validSmoothsFrame(smth)
if (is.character(validsmoothsframe))
stop(validsmoothsframe)
#Plot profiles of some combination of unsmoothed and smoothed response, AGR and RGR
if ("profiles" %in% plots && !is.allnull(profile.plot.args))
plotSmoothsComparison(data = smth,
response = response, response.smoothed = response.smooth,
times = times, individuals = individuals,
trait.types = traits,
x.title = x.title, y.titles = y.titles,
profile.plot.args = profile.plot.args, ...)
#Plot median deviations plots
if ("medians.deviations" %in% plots && !is.allnull(meddevn.plot.args))
plotSmoothsMedianDevns(data = smth,
response = response, response.smoothed = response.smooth,
times = times, individuals = individuals,
trait.types = traits,
x.title = x.title, y.titles = y.titles,
meddevn.plot.args = meddevn.plot.args,
...)
#Plot deviations of unsmoothed and smoothed response, and possibly AGR and RGR
if (any(c("absolute.boxplots", "relative.boxplots") %in% plots) && !is.allnull(devnboxes.plot.args))
{
boxp <- c("absolute.boxplots", "relative.boxplots")[c("absolute.boxplots", "relative.boxplots") %in% plots]
#Plot boxplots
plotSmoothsDevnBoxplots(data = smth,
response = response, response.smoothed = response.smooth,
times = times, individuals = individuals,
trait.types = traits,
which.plots = boxp,
x.title = x.title, y.titles = y.titles,
devnboxes.plot.args = devnboxes.plot.args,
...)
}
invisible(smth)
}
"traitChooseSmooth" <- function(smooths, response.smoothed, individuals, times,
keep.columns = NULL,
x.title = NULL, y.titles = NULL,
trait.types = c("response.smoothed", "AGR", "RGR"),
chosen.smooth = args4chosen_smooth(),
chosen.plot.args = args4chosen_plot(),
mergedata = NULL,
...)
{
chosen.smooth <- chosen.smooth
chosen.plot.args <- chosen.plot.args
inargs <- list(...)
checkEllipsisArgs(c("traitChooseSmooth", "plotProfiles"), inargs)
#Process chosen.smooth and other arguments
options <- c("allvalid","parallel","single")
comb.opt <- options[check.arg.values(chosen.smooth$combinations, options=options)]
if (comb.opt != "single")
stop("combinations must be single for chosen.smooth.args")
if (any(unlist(lapply(chosen.smooth, function(x) (length(x) > 1)))))
stop("All of the components of chosen.smooth.args must be single-valued")
if ((!is.allnull(chosen.smooth$df) && !is.allnull(chosen.smooth$lambdas)))
stop("One of df and lambda must be NULL in chosen.smooth.args")
combos <- levels(with(smooths, fac.combine(list(Type,TunePar,TuneVal,Method),
combine.levels = TRUE, sep = "-")))
tparams <- conv2TuneParams(smth.args = chosen.smooth, smth = smooths)
stype <- tparams$stype
tunepar <- tparams$tunepar
tuneval <- tparams$tuneval
smethod <- tparams$smethod
choice <- paste0(sapply(tparams, as.character), collapse = "-")
if (!(choice %in% combos))
stop("The combination of the values of Type, TunePar, TuneVal and Method ",
"given in chosen.smooth.args are not amongst those for the set of smooths in data")
#Get subset
smth <- smooths[smooths$Type == stype & smooths$TunePar == tunepar &
smooths$TuneVal == tuneval & smooths$Method == smethod,
setdiff(names(smooths), c("Type","TunePar","TuneVal","Method","Tuning"))]
class(smth) <- "data.frame"
#merge it with the mergedata
if (!is.null(mergedata))
{
checkNamesInData(c(individuals, times), mergedata)
if (!is.null(keep.columns))
smth <- smth[setdiff(names(smth), keep.columns)]
mergedata <- mergedata[c(individuals, times, setdiff(names(mergedata), names(smth)))]
smth <- merge(mergedata, smth, sort = FALSE)
#Order the columns
smth <- smth[c(names(mergedata),setdiff(names(smth), names(mergedata)))]
}
if (!is.allnull(chosen.plot.args))
{
smth[times] <- convertTimes2numeric(smth[[times]])
#Plot the profile plots for the chosen smooth
options <- c("response.smoothed", "AGR", "RGR", "all")
traits <- options[unlist(lapply(trait.types, check.arg.values, options=options))]
if ("all" %in% traits)
grates <- c("AGR", "RGR")
else
{
grates <- traits[-match("response.smoothed", traits)]
}
grates <- c("AGR","RGR")[c("AGR","RGR") %in% grates]
if (length(grates) == 0)
responses.plot <- response.smoothed
else
responses.plot <- c(response.smoothed, paste(response.smoothed, grates, sep = "."))
if (is.null(x.title))
x.title <- times
if (is.null(y.titles))
{
y.titles <- responses.plot
}
else
{
if (length(y.titles) != length(responses.plot))
stop("y.titles is not NULL and have not been provided for the response, the AGR and the RGE")
}
names(y.titles) <- responses.plot
#Find out if any plotProfiles arguments that args4profile_plot handles have been supplied in '...'
if (length(inargs))
{
usedProfile.args <- c("facet.x","facet.y","labeller","scales","colour","colour.column","colour.values",
"alpha","addMediansWhiskers","ggplotFuncs") #formalArgs(args4profile_plot)
doubleargs <- intersect(names(inargs), usedProfile.args)
if (length(doubleargs) > 0)
stop("the 'plotProfiles' arguments ",paste0(doubleargs, collapse = ", "),
" conflict with 'args4profile_plot' arguments")
} else
usedProfile.args <- NULL
#extract any valid plotProfiles arguments from inargs
pltProfile.args <- setdiff(formalArgs(plotProfiles), usedProfile.args)
pltProfile.args <- names(inargs)[names(inargs) %in% pltProfile.args]
if (length(pltProfile.args))
pltProfile.args <- inargs[pltProfile.args]
else
pltProfile.args <- NULL
if (is.null(pltProfile.args) || !("title" %in% names(pltProfile.args)))
title <- paste0("Plot for the choice ", paste0(c(substring(smethod,1,3),stype,
tunepar,tuneval), collapse = "-"))
#Call plot longitudinal with just the plotProfile args from inargs
for (kresp in responses.plot)
do.call(plotProfiles, list(data = smth, times = times, response = kresp,
individuals = individuals,
facet.x = chosen.plot.args$facet.x,
facet.y = chosen.plot.args$facet.y,
labeller = chosen.plot.args$facet.labeller,
scales = chosen.plot.args$facet.scales,
breaks.spacing.x = chosen.plot.args$breaks.spacing.x,
colour = chosen.plot.args$colour,
colour.column = chosen.plot.args$colour.column,
colour.values = chosen.plot.args$colour.values,
alpha = chosen.plot.args$alpha,
title = title,
x.title = x.title,
y.title = y.titles[kresp],
printPlot = TRUE,
ggplotFuncs = c(list(theme(axis.text.x =
element_text(angle = chosen.plot.args$angle.x))),
chosen.plot.args$ggplotFuncs),
...))
}
#Make sure that times is of the same type as times in data
smth[times] <- convertTimesExnumeric(smth[[times]], mergedata[[times]])
invisible(smth)
}
"traitSmooth" <- function(data, response, response.smoothed, individuals, times,
keep.columns = NULL,
get.rates = TRUE,
rates.method="differences", ntimes2span = NULL,
trait.types = c("response", "AGR", "RGR"),
smoothing.args = args4smoothing(),
x.title = NULL, y.titles = NULL,
which.plots = c("profiles", "medians.deviations"),
profile.plot.args = args4profile_plot(),
meddevn.plot.args = args4meddevn_plot(),
devnboxes.plot.args = args4devnboxes_plot(),
chosen.smooth.args = args4chosen_smooth(),
chosen.plot.args = args4chosen_plot(),
mergedata = NULL,
...)
{
#This is needed to make sure that the functions have been evaluated
smoothing.args <- smoothing.args
profile.plot.args <- profile.plot.args
meddevn.plot.args <- meddevn.plot.args
chosen.smooth.args <- chosen.smooth.args
chosen.plot.args <- chosen.plot.args
devnboxes.plot.args = devnboxes.plot.args
inargs <- list(...)
checkEllipsisArgs(c("traitSmooth","plotProfiles"), inargs)
tmp <- data
tmp[times] <- convertTimes2numeric(tmp[[times]])
#Call probeSmooths
smth <- do.call(probeSmooths, list(data = tmp,
response = response, response.smoothed,
times = times, individuals = individuals,
keep.columns = keep.columns,
trait.types = trait.types,
get.rates = get.rates,
rates.method = rates.method,
ntimes2span = ntimes2span,
x.title = x.title, y.titles = y.titles,
which.plots = which.plots,
smoothing.args = smoothing.args,
profile.plot.args = profile.plot.args,
meddevn.plot.args = meddevn.plot.args,
devnboxes.plot.args = devnboxes.plot.args,
...))
#Process chosen model
if (!is.allnull(chosen.smooth.args))
{
#Check of chosen.plot factors
chosen.plotfacs <- unique(c(chosen.plot.args$plots.by, chosen.plot.args$facet.x, chosen.plot.args$facet.y))
chosen.plotfacs <- setdiff(chosen.plotfacs, ".")
#Check that required cols are in data
checkNamesInData(c(chosen.plotfacs, chosen.plot.args$colour.column), data = smth)
#Check that no smoothing parameter factors have been supplied in plots.by, facet.x and facet.y for the chosen plot
if (any(smooth.cols %in% chosen.plotfacs))
stop("The smoothing parameter factor(s) ",
paste(smooth.cols[smooth.cols %in% chosen.plotfacs], collapse = ", "),
" occur(s) in chosen.plots.args - only a single smooth is to be plotted and they are unnecessary")
traits <- c("response.smoothed", "AGR", "RGR", "all")
traits <- traits[unlist(lapply(trait.types, check.arg.values, options=traits))]
if ("all" %in% traits)
traits <- c("response.smoothed", "AGR", "RGR")
#reduce ellipsis args to plotProfiles args
pltProfile.args <- names(inargs)[names(inargs) %in% formalArgs(plotProfiles)]
if (length(pltProfile.args))
pltProfile.args <- inargs[pltProfile.args]
else
pltProfile.args <- NULL
ch.smth <- do.call(traitChooseSmooth,
c(list(smooths = smth, response.smoothed = response.smoothed,
individuals = individuals, times = times,
keep.columns = keep.columns,
x.title = x.title, y.titles = y.titles,
trait.types = traits,
chosen.smooth = chosen.smooth.args,
chosen.plot.args = chosen.plot.args,
mergedata = mergedata),
pltProfile.args))
#The chosen smooth is to be returned
if (!is.null(mergedata))
smth <- ch.smth
} else # merge with the original data if there is only one smooth
{
if (all(sapply(smth[smooth.cols], function(x) nlevels(factor(x)) == 1)))
{
if (is.smooths.frame(data))
{
if (is.null(mergedata))
smth <- smth[setdiff(names(smth), c("Type","TunePar","TuneVal","Method","Tuning"))]
else
{
checkNamesInData(c(individuals, times), mergedata)
if (!is.null(keep.columns))
smth <- smth[setdiff(names(smth), keep.columns)]
mergedata <- mergedata[c(individuals, times, setdiff(names(mergedata), names(smth)))]
smth <- merge(mergedata, smth, sort = FALSE)
#Order the columns
smth <- smth[c(names(mergedata),setdiff(names(smth), names(mergedata)))]
}
} else
{
smth <- smth[setdiff(names(smth), c("Type","TunePar","TuneVal","Method","Tuning"))]
if (!is.null(keep.columns))
smth <- smth[setdiff(names(smth), keep.columns)]
class(smth) <- "data.frame"
tmp <- tmp[c(individuals, times, setdiff(names(tmp), names(smth)))]
smth <- merge(tmp, smth, sort = FALSE)
#Order the columns
smth <- smth[c(names(tmp),setdiff(names(smth), names(tmp)))]
}
}
}
#Make sure that times is of the same type as times in data
smth[times] <- convertTimesExnumeric(smth[[times]], data[[times]])
invisible(smth)
}
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