R: Compares, for a set of specified values of df and different...

probeSmoothing {growthPheno}

R Documentation

Compares, for a set of specified values of df and different smoothing methods, a response and the smooths of it, possibly along with growth rates calculated from the smooths

Description

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

Note: this function is soft deprecated and may be removed in future versions.
Use probeSmooths.

Usage

probeSmoothing(data, response = "Area", response.smoothed = NULL, 
               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, 
               ...)

Arguments

`data`	A `data.frame` containing the data.
`response`	A `character` specifying the response variable to be supplied to `smooth.spline` and that is to be plotted on the y-axis.
`response.smoothed`	A `character` specifying the name of the column containing the values of the smoothed response variable, corresponding to `response`. If `response.smoothed` is `NULL`, then `response.smoothed` is set to the `response` to which `.smooth` is added.
`x`	A `character` giving the variable to be plotted on the x-axis; it may incorporate an expression. If `x` is `NULL` then `xname` is used.
`xname`	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.
`times.factor`	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.
`individuals`	A `character` giving the name of the `factor` that defines the subsets of the `data` for which each subset corresponds to the `response` values for an individual (e.g. plant, pot, cart, plot or unit).
`na.x.action`	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.
`na.y.action`	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.
`df`	A `numeric` specifying the set of degrees of freedom to be probed.
`smoothing.methods`	A `character` giving the smoothing method to use. The two possibilites are (i) `"direct"`, for directly smoothing the observed `response`, and (ii) `"logarithmic"`, for smoothing the `log`-transformed `response` and then back-transforming by taking the exponentional of the fitted values.
`correctBoundaries`	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 `spline.type` must be `NCSS` and `lambda` and `deriv` must be `NULL` for `correctBoundaries` to be set to `TRUE`.
`get.rates`	A `logical` specifying whether or not the growth rates (AGR and RGR) are to be computed and stored.
`rates.method`	A `character` specifying the method to use in calculating the growth rates. The two possibilities are `"differences"` and `"derivates"`.
`facet.x`	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.
`facet.y`	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.
`labeller`	A `ggplot` `function` for labelling the facets of a plot produced using the `ggplot` function. For more information see `ggplot`.
`x.title`	Title for the x-axis. If `NULL` then set to `times.factor`.
`colour`	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`.
`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`.
`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.
`alpha`	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.
`trait.types`	A `character` giving the `trait.types` that are to be produced, and potentially plotted. One of more of `response`, `AGR` and `RGR`. If `all`, all three traits are produced.
`propn.types`	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 proportions of the median values of the traits are omitted.
`propn.note`	A `logical` indicating whether a note giving the proportion of the median values plotted in the `compare.medians` plots.
`which.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.
`deviations.plots`	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.
`alpha.med.devn`	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.
`ggplotFuncs`	A `list`, each element of which contains the results of evaluating a `ggplot` function. It is created by calling the `list` function with a `ggplot` function call for each element. These functions are applied to all three plots produced.
`ggplotFuncsMedDevn`	A `list`, each element of which contains the results of evaluating a `ggplot` function. It is created by calling the `list` function with a `ggplot` function call for each element. Note that these functions are applied to the `compare.median` deviations plots only.
`...`	allows passing of arguments to `plotLongitudinal`.

Value

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".

Author(s)

Chris Brien

Examples

data(exampleData)
vline <- list(ggplot2::geom_vline(xintercept=29, linetype="longdash", size=1),
              ggplot2::scale_x_continuous(breaks=seq(28, 42, by=2)))
probeSmoothing(data = longi.dat, response = "PSA", df = c(4,7), 
               xname = "xDAP", times = "DAP", 
               ggplotFuncs=vline)

[Package growthPheno version 2.1.25 Index]