predictPresent.asreml {asremlPlus}R Documentation

Forms the predictions for each of one or more terms and presents them in tables and/or graphs.

Description

This function forms the predictions for each term in terms using a supplied asreml object and predictPlus.asreml. Tables are produced using predictPlus.asreml, in conjunction with
allDifferences.data.frame, with the argument tables specifying which tables are printed. The argument plots, along with transform.power, controls which plots are produced. The plots are produced using plotPredictions.data.frame, with line plots produced when variables involving x.num or x.fac are involved in classify for the predictions and bar charts otherwise. In order to get the correct predictions you may need to supply additional arguments to predict.asreml through ... e.g. present, parallel, levels.

The order of plotting the levels of one of the factors indexing the predictions can be modified and is achieved using sort.alldiffs.

Usage

## S3 method for class 'asreml'
predictPresent(asreml.obj, terms, 
               linear.transformation = NULL, 
               wald.tab = NULL, dDF.na = "residual", dDF.values = NULL, 
               x.num = NULL, x.fac = NULL, nonx.fac.order = NULL,  
               x.pred.values = NULL, x.plot.values = NULL, 
               plots = "predictions", panels = "multiple", 
               graphics.device = NULL, 
               error.intervals = "Confidence", interval.annotate = TRUE,
               meanLSD.type = "overall", LSDby = NULL, 
               avsed.tolerance = 0.25, titles = NULL, 
               colour.scheme = "colour", save.plots = FALSE, 
               transform.power = 1, offset = 0, scale = 1, 
               pairwise = TRUE, Vmatrix = FALSE, 
               tables = "all", level.length = NA, 
               alpha = 0.05, inestimable.rm = TRUE, 
               sortFactor = NULL, sortWithinVals = NULL, 
               sortOrder = NULL, decreasing = FALSE, 
               trace = FALSE, ggplotFuncs = NULL, ...)

Arguments

asreml.obj

asreml object for a fitted model.

terms

A character vector giving the terms for which predictions are required.

linear.transformation

A formula or a matrix specifying a linear transformation to be applied to the predictions. If a formula is given then it is taken to be a submodel of the model term corresponding to the classify. The projection matrix that transforms the predictions so that they conform to the submodel is obtained; the submodel should involving the variables in the classify. For example, for classify set to "A:B", the submodel ~ A + B will result in the predictions for the combinations of A and B being made additive for the factors A and B.

If a matrix is provided then it will be used to apply the linear transformation to the predictions. It might be a contrast matrix or a matrix of weights for a factor used to obtain the weighted average over that factor. The number of rows in the matrix should equal the number of linear combinations of the predictions desired and the number of columns should equal the number of predictions.

In either case, as well as the values of the linear combinations, their standard errors, pairwise differences and associated statistics are returned in the alldiffs.object.

wald.tab

A data.frame containing the pseudo-anova table for the fixed terms produced by a call to wald.asreml. The main use of it here is in determinining the degrees of freedom for calculating confidence or half-LSD error.intervals and p-values, the latter to be stored in the p.differences component of the alldiffs.object that is created.

dDF.na

The method to use to obtain approximate denominator degrees of freedom. when the numeric or algebraic methods produce an NA. Consistent with when no denDF are available, the default is "residual" and so the residual degrees of freedom from asreml.obj$nedf are used. If dDF.na = "none", no subtitute denominator degrees of freedom are employed; if dDF.na = "maximum", the maximum of those denDF that are available, excluding that for the Intercept, is used; if all denDF are NA, asreml.obj$nedf is used. If dDF.na = "supplied", a vector of values for the denominator degrees of freedom is to be supplied in dDF.values. Any other setting is ignored and a warning message produced. Generally, substituting these degrees of freedom is anticonservative in that it is likely that the degrees of freedom used will be too large.

dDF.values

A vector of values to be used when dDF.na = "supplied". Its values will be used when denDF in a test for a fixed effect is NA. This vector must be the same length as the number of fixed terms, including (Intercept) whose value could be NA.

x.num

A character string giving the name of the numeric covariate that corresponds to x.fac, is potentially included in terms in the fitted model and which corresponds to the x-axis variable. It should have the same number of unique values as the number of levels in x.fac.

x.fac

A character string giving the name of the factor that corresponds to x.num, is potentially included in terms in the fitted model and which corresponds to the x-axis variable. It should have the same number of levels as the number of unique values in x.num. The levels of x.fac must be in the order in which they are to be plotted - if they are dates, then they should be in the form yyyymmdd, which can be achieved using as.Date. However, the levels can be non-numeric in nature, provided that x.num is also set.

nonx.fac.order

A character vector giving the order in which factors other than x.fac are to be plotted in plots with multiple panels (i.e. where the number of non-x factors is greater than 1). The first factor in the vector will be plotted on the X axis (if there is no x.num or x.fac. Otherwise, the order of plotting the factors is in columns (X facets) and then rows (Y facets). By default the order is in decreasing order for the numbers of levels of the non x factors.

x.pred.values

The values of x.num for which predicted values are required.

x.plot.values

The actual values to be plotted on the x axis or in the labels of tables. They are needed when values different to those in x.num are to be plotted or x.fac is to be plotted because there is no x.num term corresponding to the same term with x.fac.

plots

Possible values are "none", "predictions", "backtransforms" and "both". Plots are not produced if the value is "none". If data are not transformed for analysis (transform.power = 1), a plot of the predictions is produced provided plots is not "none". If the data are transformed, the value of plots determines what is produced.

panels

Possible values are "single" and "multiple". When line plots are to be produced, because variables involving x.num or x.fac are involved in classify for the predictions, panels determines whether or not a single panel or multiple panels in a single window are produced. The panels argument is ignored for for bar charts.

graphics.device

A character specifying a graphics device for plotting. The default is
graphics.device = NULL, which will result in plots being produced on the current graphics device. Setting it to "windows", for example, will result in a windows graphics device being opened.

error.intervals

A character string indicating the type of error interval, if any, to calculate in order to indicate uncertainty in the results. Possible values are "none", "StandardError", "Confidence" and "halfLeastSignificant". The default is for confidence limits to be used. The "halfLeastSignificant" option results in half the Least Significant Difference (LSD) being added and subtracted to the predictions, the LSD being calculated using the square root of the mean of the variances of all or a subset of pairwise differences between the predictions. If the LSD is zero, as can happen when predictions are constrained to be equal, then the limits of the error intervals are set to NA. If meanLSD.type is set to overall, the avsed.tolerance is not NA and the range of the SEDs divided by the average of the SEDs exceeds avsed.tolerance then the error.intervals calculations and the plotting will revert to confidence intervals.

interval.annotate

A logical indicating whether the plot annotation indicating the type of error.interval is to be included in the plot.

avsed.tolerance

A numeric giving the value of the SED range, the range of the SEDs divided by the square root of the mean of the variances of all or a subset of the pairwise differences, that is considered reasonable in calculating error.intervals. It should be a value between 0 and 1. The following rules apply:

  1. If avsed.tolerance is NA then mean LSDs of the type specified by meanLSD.type are calculated and used in error.intervals and plots.

  2. Irrespective of the setting of meanLSD.type, if avsed.tolerance is not exceeded then the mean LSDs are used in error.intervals and plots.

  3. If meanLSD.type is set to overall, avsed.tolerance is not NA, and avsed.tolerance is exceeded then error.intervals and plotting revert to confidence intervals.

  4. If meanLSD.type is set to factor.combinations and avsed.tolerance is not exceeded for any factor combination then the half LSDs are used in error.intervals and plots; otherwise, error.intervals and plotting revert to confidence intervals.

  5. If meanLSD.type is set to per.prediction and avsed.tolerance is not exceeded for any prediction then the half LSDs are used in error.intervals and plots; otherwise, error.intervals and plotting revert to confidence intervals.

meanLSD.type

A character string determining whether the mean LSD stored is (i) the overall mean, based on the square root of the mean of the variances of all pairwise variances, (ii) the mean for each factor.combination of the factors specified by LSDby, which is based on the square root of the mean of the variances for all pairwise differences for each factor combination, unless there is only one predction for a factor.combination, when notional LSDs are calculated that are based on the standard error of the prediction multiplied by the square root of two, or (iii) the per.prediction mean, based, for each prediction, on the square root of the mean of the variances for all pairwise differences involving that prediction. It also determines, in conjunction with avsed.tolerance, which LSD will be used in calculating error.intervals and, hence, is used for plots.

LSDby

A character (vector) of variables names, being the names of the factors or numerics in the classify for each combination of which a mean LSD, minLSD and max LSD is stored in the LSD component of the alldiffs.object when meanLSD.type is factor.combinatons.

titles

A list, each component of which is named for a column in the data.frame for asreml.obj and contains a character string giving a title to use in output (e.g. tables and graphs). Here they will be used for axis labels.

colour.scheme

A character string specifying the colour scheme for the plots. The default is "colour" which produces coloured lines and bars, a grey background and white gridlines. A value of "black" results in black lines, grey bars and gridlines and a white background.

save.plots

A logical that determines whether any plots will be saved. If they are to be saved, a file name will be generated that consists of the following elements separated by full stops: the response variable name with .back if backtransformed values are being plotted, the classify term, Bar or Line and, if error.intervals is not "none", one of SE, CI or LSI. The file will be saved as a ‘png’ file in the current work directory.

transform.power

A number specifying the power of a transformation, if one has been applied to the response variable. Unless it is equal to 1, the default, back-transforms of the predictions will be obtained and presented in tables or graphs as appropriate. The back-transform will raise the predictions to the power equal to the reciprocal of transform.power, unless it equals 0 in which case the exponential will be taken. Any scaling and offsetting will also be taken into account in the backtransformation.

offset

A number that has been added to each value of the response after any scaling and before applying any power transformation. Unless it is equal to 0, the default, back-transforms of the predictions will be obtained and presented in tables or graphs as appropriate. The backtransformation will, after backtransforming for any power transformation, subtract the offset.

scale

A number by which each value of the response has been multiply before adding any offset and applying any power transformation. Unless it is equal to 1, the default, back-transforms of the predictions will be obtained and presented in tables or graphs as appropriate. The backtransformation will, after backtransforming for any power transformation and then subtracting the offset, divide by the scale.

pairwise

A logical indicating whether all pairise differences of the predictions and their standard errors and p-values are to be computed and stored. If tables is equal to "differences" or "all" or error.intervals is equal to "halfLeastSignificant", they will be stored irrespective of the value of pairwise.

Vmatrix

A logical indicating whether the variance matrix of the predictions will be stored as a component of the alldiffs.object that is returned. If linear.transformation is set, it will be stored irrespective of the value of Vmatrix.

tables

A character vector containing a combination of predictions, vcov, backtransforms, differences, p.differences, sed, LSD and all. These nominate which components of the alldiffs.object to print.

level.length

The maximum number of characters from the the levels of factors to use in the row and column labels of the tables produced by allDifferences.data.frame.

alpha

The significance level for LSDs or 1 - alpha is the confidence level for confidence intervals.

inestimable.rm

A logical indicating whether rows for predictions that are not estimable are to be removed from the components of the alldiffs.object.

sortFactor

A character containing the name of the factor that indexes the set of predicted values that determines the sorting of the components of the alldiffs.object by sort.alldiffs. If NULL then sorting is not carried out. If there is more than one variable in the classify term then sortFactor is sorted for the predicted values within each combination of the values of the sortWithin variables: the classify variables, excluding the sortFactor. There should be only one predicted value for each unique value of sortFactor within each set defined by a combination of the values of the sortWithin variables.

sortWithinVals

A list with a component named for each factor and numeric that is a classify variable for the predictions, excluding sortFactor. Each component should contain a single value that is a value of the variable. The combination of this set of values will be used to define a subset of the predicted values whose order will define the order of sortFactor to be used for all combinations of the sortWithinVals variables. If sortWithinVals is NULL then the first value of each sortWithin variable in predictions component is used to define sortWithinVals. If there is only one variable in the classify then sortWithinVals is ignored.

sortOrder

A character vector whose length is the same as the number of levels for sortFactor in the predictions component of the alldiffs.object. It specifies the desired order of the levels in the reordered components of the alldiffs.object. The argument sortWithinVals is ignored.

The following creates a sortOrder vector levs for factor f based on the values in x: levs <- levels(f)[order(x)].

decreasing

A logical passed to order that detemines whether the order for sorting the components of the alldiffs.object is for increasing or decreasing magnitude of the predicted values.

trace

If TRUE then partial iteration details are displayed when ASReml-R functions are invoked; if FALSE then no output is displayed.

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. It is passed to plotPredictions.data.frame.

...

further arguments passed to predict.asreml via predictPlus.asreml and to ggplot via plotPredictions.data.frame.

Value

A list containing an alldiffs.object for each term for which tables are produced. The names of the components of this list are the terms with full-stops (.) replacing colons (:). Plots are also preduced depending on the setting of the plot argument.

Author(s)

Chris Brien

See Also

predictPlus.asreml, allDifferences.data.frame, sort.alldiffs, subset.alldiffs,
redoErrorIntervals.alldiffs, recalcLSD.alldiffs, plotPredictions.data.frame,
print.alldiffs, as.Date, Devices

Examples

## Not run: 
data(WaterRunoff.dat)
titles <- list("Days since first observation", "Days since first observation", 
               "pH", "Turbidity (NTU)")
names(titles) <- names(WaterRunoff.dat)[c(5,7,11:12)]
asreml.options(keep.order = TRUE) #required for asreml-R4 only
current.asr <- asreml(fixed = log.Turbidity ~ Benches + Sources + Type + Species + 
                                 Sources:Type + Sources:Species + Sources:Species:xDay + 
                                 Sources:Species:Date, 
                      data = WaterRunoff.dat, keep.order = TRUE)
current.asrt <- as.asrtests(current.asr, NULL, NULL)

#### Get the observed combinations of the factors and variables in classify
class.facs <- c("Sources","Species","Date","xDay")
levs <- as.data.frame(table(WaterRunoff.dat[class.facs]))
levs <- levs[do.call(order, levs), ]
levs <- as.list(levs[levs$Freq != 0, class.facs])
levs$xDay <- as.numfac(levs$xDay)
  
#### parallel and levels are arguments from predict.asreml
diff.list <- predictPresent.asreml(asreml.obj = current.asrt$asreml.obj, 
                                   terms = "Date:Sources:Species:xDay",
                                   x.num = "xDay", x.fac = "Date", 
                                   parallel = TRUE, levels = levs, 
                                   wald.tab = current.asrt$wald.tab, 
                                   plots = "predictions", 
                                   error.intervals = "StandardError", 
                                   titles = titles, 
                                   transform.power = 0, 
                                   present = c("Type","Species","Sources"), 
                                   tables = "none", 
                                   level.length = 6)

## End(Not run)

[Package asremlPlus version 4.2-32 Index]