Takes a response
and, for each individual
, uses
splitSplines
to smooth its values for each individual
using the degrees of freedom values in df
.
Provided get.rates
is TRUE
,
both the Absolute Growth Rates (AGR) and the Relative Growth Rates (RGR)
are calculated for each smooth, either using differences or first
derivatives. A combination of the unsmoothed and smoothed values,
as well as the AGR and RGR, can be plotted for each value in
smoothing methods
in combination with df
. Note that the
arguments that modify the plots apply to all
plots that are produced. The handling of missing values is controlled
via na.x.action
and na.y.action
probeSmoothing(data, response = "Area", x = NULL, xname="xDays",
times.factor = "Days", individuals="Snapshot.ID.Tag",
na.x.action="exclude", na.y.action = "exclude",
df, smoothing.methods = "direct", correctBoundaries = FALSE,
get.rates = TRUE, rates.method="differences",
facet.x = "Treatment.1", facet.y = "Smarthouse",
labeller = NULL, x.title = NULL,
colour = "black", colour.column=NULL,
colour.values=NULL, alpha = 0.1,
trait.types = c("response", "AGR", "RGR"),
propn.types = c(0.1, 0.5, 0.75), propn.note = TRUE,
which.plots = "smoothedonly",
deviations.plots = "none", alpha.med.devn = 0.5,
ggplotFuncs = NULL, ggplotFuncsMedDevn = NULL,
...)
A data.frame
containing the data.
A character
specifying the response variable to be
supplied to smooth.spline
and that is to be plotted
on the y-axis.
A character
giving the variable to be plotted on the
x-axis; it may incorporate an expression, it being that x
may
incorporate an expression. If x
is NULL
then
xname
is used.
A character
giving the name of the
numeric
that contains the values of the predictor
variable to be supplied to smooth.spline
and
from which x
is derived.
A character
giving the name of the column in
data
containing the factor for times at which the data was
collected. Its levels will be used in calculating growth rates and
should be numeric values stored as characters.
A character
string that specifies the action to
be taken when values of x
are NA
. The possible
values are fail
, exclude
or omit
.
For exclude
and omit
, predictions and derivatives
will only be obtained for nonmissing values of x
.
The difference between these two codes is that for exclude
the returned
data.frame
will have as many rows as data
, the
missing values have been incorporated.
A character
string that specifies the action to
be taken when values of y
, or the response
, are
NA
. The possible values are fail
, exclude
,
omit
, allx
, trimx
, ltrimx
or
rtrimx
. For all options, except fail
, missing
values in y
will be removed before smoothing.
For exclude
and omit
, predictions
and derivatives will be obtained only for nonmissing values of
x
that do not have missing y
values. Again, the
difference between these two is that, only for exclude
will the missing values be incorporated into the
returned data.frame
. For allx
, predictions and
derivatives will be obtained for all nonmissing x
.
For trimx
, they will be obtained for all nonmissing
x
between the first and last nonmissing y
values
that have been ordered for x
; for ltrimx
and
utrimx
either the lower or upper missing y
values, respectively, are trimmed.
A numeric
specifying the set of degrees of freedom to
be probed.
A character
giving one or more methods
to use for smoothing. Currently, the two possibilites
are (i) "direct"
, for directly smoothing the observed
response
, and (ii) "logarithmic"
, for smoothing the
log
-transformed response
, followed by taking
exponentials of the fitted values to back-trasnform them.
A logical
indicating whether the fitted
spline values are to have the method of Huang (2001) applied
to them to correct for estimation bias at the end-points. Note that
if rates.method
is set to "derivatives"
then it is not
possible to have correctBoundaries
set to TRUE
.
A logical
specifying whether or not the growth
rates (AGR and RGR) are to be computed and stored.
A character
specifying the method to use in
calculating the growth rates. The two possibilities are
"differences"
and "derivates"
.
A data.frame
giving the variable to be used to
form subsets to be plotted in separate columns of plots.
Use "."
if a split into columns is not wanted. For
which.plots
set to methodscompare
or
dfcompare
, facet.x
is ignored.
A data.frame
giving the variable to be used to
form subsets to be plotted in separate rows of plots.
Use "."
if a split into columns is not wanted.
Title for the x-axis. If NULL
then set to times.factor
.
A character
specifying a single colour to use in
drawing the lines for the profiles. If colouring according to the
values of a variable is required then use colour.column
.
A character
giving the name of a column
in data
over whose values the colours of the lines are to be
varied. The colours can be specified using colour.values
.
A character
vector specifying the values of
the colours to use in drawing the lines for the profiles.
If this is a named vector, then the values will be matched based
on the names. If unnamed, values will be matched in order
(usually alphabetical) with the limits of the scale.
A numeric
specifying the degrees of transparency to
be used in plotting. It is a ratio in which the denominator
specifies the number of points (or lines) that must be overplotted
to give a solid cover.
A character
giving the trait.types
that
are to be produced. One of more of response
, AGR
and
RGR
. If all
, all three traits are produced.
The unsmoothed growth rates are not calculated if only smoothed plots
are requested.
A numeric
giving the proportion of the median
values of each of the trait.types
that are to be plotted in
the compare.medians
plots of the deviations of the observed
values from the smoothed values. If set
to NULL
, the plots of the proprotions of the median values of
the traits are omitted.
A logical
indicating whether a note giving the
proportion of the median values plotted in the compare.medians
plots.
A character
giving the plots that are to be
produced. If none
, no plots are produced. If smoothedonly
,
plots of the smoothed traits are plotted. If bothseparately
,
plots of the unsmoothed trait followed by the smoothed traits are
produced for each trait. If methodscompare
, a combined plot of
the smoothed traits for each smoothing.methods
is produced,
for each value of df
. If methods+rawcompare
, the unsmoothed
trait is added to the combined plot. if dfcompare
, a combined
plot of the smoothed trait for each df
is produced, for each
smoothing.methods
. If df+rawcompare
, the unsmoothed
trait is added to the combined plot.
A character
is either none
or any
combination of absolute.boxplots
, relative.boxplots
and
compare.medians
. If none
, no plots are produced.
Boxplots of the absolute and relative deviations are specified by
absolute.boxplots
and relative.boxplots
. The absolute
deviations are the values of a trait minus their smoothed values
(observed - smoothed); the relative deviations are the absolute
deviations divided by the smoothed values of the trait. The option
compare.medians
results in a plot that compares the medians
of the deviations over the times.factor
for each combination
of the smoothing.methods
and the df
. The argument
trait.types
controls the traits for which boxplots are produced.
A numeric
specifying the degrees of
transparency to be used in plotting a median deviations plot.
It is a ratio in which the denominator specifies the number of
points (or lines) that must be overplotted to give a solid cover.
allows passing of arguments to plotLongitudinal
.
A data.frame
containing individuals
,
times.factor
, facet.x
, facet.y
, xname
,
response
, and, for each df
, the smoothed
response, the AGR and the RGR. It is returned invisibly. The names of
the new data are constructed by joining elements separated by full
stops (.
). In all cases, the last element is the value of
df
. For the smoothed response, the other elements are
response
and "smooth"
; for AGR and RGR, the other elements
are the name of the smoothed response and either "AGR"
or
"RGR"
.
# NOT RUN {
data(exampleData)
vline <- list(ggplot2::geom_vline(xintercept=20, linetype="longdash", size=1),
ggplot2::scale_x_continuous(breaks=seq(12, 36, by=2)))
probeSmoothing(data = longi.dat, response = "Area", df = c(4,7), x="xDays+24.16666667",
ggplotFuncs=vline)
# }
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