| plotSmoothsComparison | R Documentation |
Plots the smoothed values for an observed response and, optionally, the unsmoothed
observed response using plotProfiles. Depending on the setting of
trait.types (response, AGR or RGR), the computed traits of the
Absolute Growth Rates (AGR) and/or the Relative Growth Rates (RGR) are plotted. This
function will also calculate and produce, using plotDeviationsBoxes, boxplots
of the deviations of the supplied smoothed values from the observed response values for the
traits and for combinations of the different smoothing parameters and for subsets of
non-smoothing-factor combinations. The observed and smoothed values are
supplied in long format i.e. with the values for each set of smoothing parameters stacked
one under the other in the supplied smooths.frame. Such data can be generated
using probeSmooths; to prevent probeSmooths producing the
plots, which it is does using plotSmoothsComparison, plotDeviationsBoxes
and plotSmoothsMedianDevns, set which.plots to none.
The smoothing parameters include spline.types, df, lambdas and
smoothing.methods (see probeSmooths).
Multiple plots, possibly each having multiple facets, are produced using ggplot2.
The layout of these plots is controlled via the arguments plots.by,
facet.x and facet.y. The basic principle is that the number of levels
combinations of the smoothing-parameter factors Type, TunePar,
TuneVal, Tuning (the combination of (TunePar and TuneVal), and
Method that are included in plots.by, facet.x and
facet.y must be the same as those covered by the combinations of the values
supplied to spline.types, df, lambdas and Method and incorporated
into the smooths.frame input to plotSmoothsComparison via the
data argument. This ensures that smooths from different parameter sets are not
pooled into the same plot. The factors other than the smoothing-parameter
factors can be supplied to the plots.by and facet arguments.
The following profiles plots can be produced: (i) separate plots of the
smoothed traits for each combination of the smoothing parameters
(include Type, Tuning and Method in plots.by);
(ii) as for (i), with the corresponding plot for the unsmoothed trait
preceeding the plots for the smoothed trait (also set include.raw to
alone); (iii) profiles plots that compare a smoothed trait for all
combinations of the values of the smoothing parameters, arranging the plots
side-by-side or one above the other (include Type, Tuning and
Method in facet.x and/or facet.y - to include the
unsmoothed trait set include.raw to one of facet.x or
facet.y; (iv) as for (iii), except that separate plots are
produced for each combination of the levels of the factors
in plot.by and each plot compares the smoothed traits for the
smoothing-parameter factors included in facet.x
and/or facet.y (set both plots.by and one or more of
facet.x and facet.y).
plotSmoothsComparison(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, ...)
data |
A |
response |
A |
response.smoothed |
A |
times |
A |
individuals |
A |
trait.types |
A |
x.title |
Title for the x-axis, used for all plots. If |
y.titles |
A |
profile.plot.args |
A named |
printPlot |
A |
... |
allows passing of arguments to |
A multilevel list that contains the ggplot
objects for the plots produced. The first-level list
has a component for each trait.types and each of these is a
second-level list that contains the trait
profile plots and for a trait. It may contain components labelled
Unsmoothed, all or for one of the levels of the
factors in plots.by; each of these third-level
ists contains a ggplot object that can
be plotted using print.
Chris Brien
traitSmooth, probeSmooths, args4profile_plot, plotDeviationsBoxes, plotSmoothsMedianDevns, ggplot2.
data(exampleData)
vline <- list(ggplot2::geom_vline(xintercept=29, linetype="longdash", size=1))
traits <- probeSmooths(data = longi.dat,
response = "PSA", response.smoothed = "sPSA",
times = "DAP",
#only df is changed from the probeSmooth default
smoothing.args =
args4smoothing(smoothing.methods = "direct",
spline.types = "NCSS",
df = c(4,7), lambdas = NULL),
which.plots = "none")
plotSmoothsComparison(data = traits,
response = "PSA", response.smoothed = "sPSA",
times = "DAP", x.title = "DAP",
#only facet.x is changed from the probeSmooth default
profile.plot.args =
args4profile_plot(plots.by = NULL,
facet.x = "Tuning", facet.y = ".",
include.raw = "no",
ggplotFuncs = vline))
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