multiEvaluator: Evaluates an Expression in One or More R-language...
In TIBCOSoftware/RinR: Evaluate R Expressions in a Different R Interpreter

multiREvaluator

R Documentation

Evaluates an Expression in One or More R-language Interpreters

Description

Evaluates an R-language expression in one or more R-language interpreters and optionally compares the results.

Usage

RCompare(expr, REvaluators = DefaultREvaluators(), substitute = TRUE,
    data = NULL, envirData = parent.frame(), tolerance = 0,
    returnValues = TRUE, verbose = FALSE, randomCheck = FALSE, ...)
multiREvaluator(expr, REvaluators = DefaultREvaluators(), substitute = TRUE,
    data = NULL, envirData = parent.frame(), verbose = FALSE,
    sideBySide = TRUE, PRINTFUN = "print", randomCheck = FALSE, ...)
DefaultREvaluators()

Arguments

`expr`	an expression to evaluate.
`REvaluators`	a list of `REvaluator` objects as created by the `makeREvaluator` function. The defaults evaluate the expressions in open-source R and \TERR.
`substitute`	a logical value. Specifies how to use the `expr` argument. If `TRUE` (the default), the unevaluated `expr` is sent to the other interpreter to be evaluated. If `FALSE`, the unevaluated `expr` is evaluated before being sent to the other interpreter. The evaluated expression must also be a language object. Setting `subsitute` to `FALSE` makes it easier to call this function from another function.
`data`	if not `NULL`, then either a character vector or a named list specifying the objects that must be put into the environment where `expr` is evaluated. See `TERREvaluator` for details.
`envirData`	the environment where items in the character vector `data` are found.
`verbose`	a logical value. If `TRUE`, debugging information is printed. The default is `FALSE`.
`tolerance`	a small positive numeric value that is passed to `all.equal` to specify how far apart two numeric results must be before a difference is reported.
`returnValues`	a logical value. If `TRUE` (the default), then `RCompare` returns a list containing the results of all the evaluations, along with a list describing how each result (except the first) differs from the first. If `FALSE`, it returns only the description of the differences.
`sideBySide`	a logical value. If `TRUE` (the default), then give the output of `multiEvaluator` the class `"sideBySide"` so the output components are printed next to each other, if possible.
`PRINTFUN`	a function or the name of a function. This is passed to `sideBySide` to specify how to print the output components.
`randomCheck`	a logical value. If `TRUE`, an attribute `"usedRandomSeed"` is added to the output to indicate if random numbers were used during the requested calculations. If `FALSE`, that information is not returned. The check determines if `.GlobalEnv$.Random.seed` changes during the calculations. Note that code calling `set.seed()` can cause `randomCheck` to report a false negative.
`...`	`multiREvaluator` and `RCompare` will pass any unrecognized arguments to the `REvaluators` that they invoke.

Value

`multiREvaluator`	returns a list of the results of evaluating the expression in each R interpreter. The names of the list are taken from `version$version.string` in each interpreter. The returned list has the class `"sideBySide"`, whose print method attempts to print the outputs beside each other for easier visual comparison. If the argument `randomCheck` is `TRUE`, then the output includes an attribute, `usedRandomSeed`, which is a logical vector with one element per evaluator indicating whether random numbers are used in the calculations on each evaluator.
`RCompare`	usually returns the same value as `multiEvaluator`, with the addition of the attribute `all.equal`, describing the differences between the results.
`RCompare(..., returnValues)`	returns just the list of differences.
`DefaultREvaluators`	returns `getOption("REvaluators")` if it is a list of `"REvaluator"` objects. Otherwise, it returns `list(RinR::REvaluator, RinR::TERREvaluator)`, two of the built-in `REvaluator`s.

Examples

## Not run: 
RCompare(log2(xTest), data = list(xTest=4:16))
RCompare(lm(hp ~ gear + am, data=mtcars), REvaluators=list(REvaluator, LocalEvaluator), tol=1e-12)
myData <- subset(iris, Species == "setosa", -Species)
RCompare(prcomp(myData), data = "myData")
RCompare(kmeans(cbind(sin(1:10), cos(1:10)), 3), randomCheck=TRUE)

## End(Not run)

TIBCOSoftware/RinR documentation built on March 19, 2022, 5:16 p.m.