Write a Fragment of a C Program

Description

Writes either the C declaration of items in a list, or initializes them with the contents of the list.

Usage

Cfrag.list(x, file = NULL,  item.num = c(3, 10, 5),
	indent = c("\t", "\t\t"), declaration.only = FALSE,
	long = FALSE, append = FALSE)

Arguments

Value

if file is a non-empty character string, then the name of the file that is written. Otherwise, a character vector of the declarations – each element representing a different line.

Side Effects

if file is a non-empty character string, then the file is created, overwritten or appended.

Details

The type to declare is dependent on the storage mode of the component of x. You may need to coerce components to get them to be declared the correct type.

Note

Rcpp now probably makes most uses of this function obsolete.

See Also

.C, storage.mode, as.double, as.integer, as.character, cat.

Examples

test.list <- list(adoub=as.double(rnorm(20)), anint=as.integer(92:109),
	achar=c("aaa", "bbbb", "ccccc"))

Cfrag.list(test.list, file="")

## Not run: 
Cfrag.list(test.list, file="test.c")

Cfrag.list(test.list[1], file="test.c", dec=TRUE)
Cfrag.list(test.list[-1], file="test.c", dec=FALSE, append=TRUE)

## End(Not run)

Portion of an Array or Data Frame

Description

Returns an array (or data frame) of the same number of dimensions as the input, but generally with smaller dimensions.

Usage

  corner(x, corner = "tlffff", n = 6)

Arguments

Value

an array similar to the input x, but with smaller dimensions in general.

See Also

head, tail.

Examples

  corner(freeny.x)
  corner(freeny.x, "br", c(2,3))

  # with three-dimensional arrays:
  corner(iris3)
  corner(array(1:1000, rep(10, 3)), "brf", 2:4)

Genetic Optimization

Description

Approximately minimizes the value of a function using a simple heuristic optimizer that uses a combination of genetic and simulated annealing optimization.

Usage

  genopt(fun, population, lower = -Inf, upper = Inf,
         scale = dcontrol["eps"], add.args = NULL,
         control = genopt.control(...), ...)

Arguments

Details

There is a summary method for class genopt which shows the call, a summary of the set of objectives found, and the best solution (set of parameters).

Value

a list of class genopt with the following components:

References

The original version of this function appeared in "S Poetry".

See Also

genopt.control, summary.genopt.

Examples

  # two parameters, population size 5
  go1 <- genopt(function(x, other) sum(x, other),
                population=matrix(rexp(10), nrow=2, ncol=5),
                lower=0, add.arg=list(other=3), trace=FALSE)
  summary(go1)

  go2 <- genopt(function(x, other) sum(x, other), population=go1,
                lower=0, add.arg=list(other=3), trace=FALSE)

Control parameters for genopt

Description

Returns a list suitable as the control argument of the genopt function.

Usage

  genopt.control(births = 100, random.n = 0, jitters.n = 3,
                 trace = TRUE, eps = 0.1, prob = 0.4, scale.min = 1e-12,
                 maxeval = Inf)

Arguments

Value

a list with components:

See Also

genopt.

Examples

  goc1 <- genopt.control(random.n=200, births=1000, trace=FALSE)

  go1 <- genopt(function(x, other) sum(x, other),
                matrix(rexp(10), nrow=2, ncol=5), lower=0, add.arg=list(other=3),
                control=goc1)

  goc2 <- genopt.control(random.n=0, births=1000, trace=FALSE)

  go2 <- genopt(function(x, other) sum(x, other), go1,
                lower=0, add.arg=list(other=3), control=goc2)

Membership of ntile groups

Description

Creates groups where the groups each have as close to the same number of members as possible.

Usage

ntile(x, ngroups, na.rm = FALSE, result = "list",
    reverse = FALSE, checkBleed = TRUE)

Arguments

Value

the form of the value depends on the result argument.

The "list" result has the values of the input x grouped into ngroups components.

The "numeric" result is a vector of integers from 1 to ngroups indicating which group the corresponding element of x is in.

The "factor" result is an ordered factor version of the "numeric" result.

Note

A more primitive version of this function appeared in a blog post called "Miles of iles" on the Portfolio Probe website. A bit of comparison with the alternative functions in See Also may be found there.

Author(s)

Pat Burns

See Also

cut, cut2, quantcut.

Examples

ntile(setNames(state.area, state.name), 10)

ntile(Loblolly$height, 5, result="factor", reverse=TRUE)

ntile(c(-10:10, rep(0, 7)), 4)

Random Permutation Test

Description

Performs a random permutation test on the relationship between two discrete variables, or by using a function.

Usage

permutation.test.discrete(x, y = NULL, scores, alternative = "greater", 
	trials = 1000)
permutation.test.fun(x, y = NULL, fun = function(x, y) sum(x * y), 
	alternative = "greater", trials = 1000)

Arguments

Value

an object of class permtstBurSt which is a list with the following components:

Side effects

The object .Random.seed is either created or updated.

Details

There are print and plot methods for this class of object. The print method merely describes the object and shows the p-value of the test (rounded, by default, to 4 digits).

A simple version of the p-value is the number of random permutations that are at least as extreme as the original divided by the total number of random permutations. The value computed, which is more correct, has 1 added to both numerator and denominator. In general the difference is of no consequence. However, there are cases where it does matter, for example when independent p-values are combined.

These functions are related to "Permuting Super Bowl Theory" which can be found in the working papers section of http://www.burns-stat.com. The paper explains permutation tests via a discussion of the Super Bowl indicator of the stock market.

See Also

plot.permtstBurSt.

Examples

winner <- c('N', 'N', 'A', 'N', 'A', 'N')
market <- c('+', '-', '-', '+', '+', '+')
smat <- diag(2)
dimnames(smat) <- list(c('N', 'A'), c('+', '-'))

pt1 <- permutation.test.discrete(winner, market, smat)
print(pt1)
plot(pt1)

pt2 <- permutation.test.fun(ToothGrowth[, -2], fun=cor)
print(pt2)
plot(pt2)


smat2 <- matrix(c(-3, -.5, 3, -1, 1, 0, 0, 1, -1, 3, -.5, -3),
	3, 4, dimnames=list(c('Up', 'Neut', 'Down'), 
	c('Q1', 'Q2', 'Q3', 'Q4')))
my.results <- data.frame(results=sample(c('Up', 'Neut', 'Down'),
       100, replace=TRUE), quartile=sample(
       c('Q1', 'Q2', 'Q3', 'Q4'), 100, replace=TRUE))
permutation.test.discrete(my.results[, c("results", "quartile")], 
	score=smat2)

Plot Distribution from a Permutation Test

Description

Shows the distribution of the scores of the random permutations relative to the score from the actual data.

Usage

## S3 method for class 'permtstBurSt'
plot(x, col = c("black", "red"), width = 10, 
	uniqlim = 10, main = "", xlab = "Scores", ...)

Arguments

Side effects

a plot is produced.

Details

Two styles of plot are possible. A histogram is more appropriate when there are more than a few values in the permutation distribution. When there are only a few distinct values, then a bar-type plot is more informative. The uniqlim argument controls the definition of "a few".

These functions are related to "Permuting Super Bowl Theory" which can be found in the working papers section of http://www.burns-stat.com. The paper explains permutation tests via a discussion of the Super Bowl indicator of the stock market.

See Also

permutation.test.discrete.

Examples

## Not run: 
pt1 <- permutation.test.discrete(winner, market, smat)
plot(pt1, col=c("blue", "gold"))

## End(Not run)

Search R scripts for text

Description

Returns a list of text matches in the scripts in a directory.

Usage

scriptSearch(pattern, path = ".", subdirs = TRUE, suffix = "\\.[rR]$",
      commentsIncluded = FALSE, ..., verbose = FALSE)

Arguments

Details

The default suffix argument restricts the search to R scripts. An R script is considered to be a file that ends in dot-R, where the "R" can be lowercase or uppercase.

Value

a list: the names are equal to the file names containing matches, and each component is a character vector of the whitespace-trimmed lines in the file that match the pattern.

Limitations

Using commentsIncluded=FALSE does not preclude a match being declared when it is in a comment not starting the line.

Author(s)

Pat Burns

See Also

grep

Examples

## Not run: 
scriptSearch("list.files")
scriptSearch("garch", "~/../burns-stat3/webpages/blog")

# search for an assignment
# ' *' means zero or more spaces
scriptSearch("specialObject *<- ", "~/myScriptDirectory", subdirs=FALSE)

# search COBOL files
scriptSearch("blah", suffix="\\.cbl$")


## End(Not run)

Summary of genopt object

Description

The call, best solution and summary of objectives in the final population.

Usage

## S3 method for class 'genopt'
summary(object, ...)

Arguments

Value

a list with components:

See Also

genopt.

Examples

  # two parameters, population size 5
  go1 <- genopt(function(x, other) sum(x, other),
                population=matrix(rexp(10), nrow=2, ncol=5),
                lower=0, add.arg=list(other=3), trace=FALSE)
  summary(go1)

Text for a testthat test

Description

Automatically writes the text of a testthat::expect_equal test of an expression.

Usage

writeExpectTest(expr, filename = "", ...)

Arguments

Value

NULL

Side effects

text is written to the console or to a file.

Note

Some functions are hard to test, like scriptSearch. It seems like this one would be too, but in fact it is self-testing. If the test it writes doesn't pass in the same environment, then it must have done something wrong.

See Also

expect_equal.

Examples

writeExpectTest(head(1:10))
writeExpectTest(head(cars))