Make Closure for functions

Description

MC makes closures for defining functions in a function.

Usage

MC(f, env=NULL)

Arguments

Details

MC declares functions to be used in f. When f is defined inside of a function, say fun, it cannot call other functions defined in fun. MC can enclose the functions needed by f and make it possible for f to call other functions defined in fun.

Author(s)

Knut M. Wittkowski kmw@rockefeller.edu

Examples

f1 <- function(x, y) x+y
f2 <- MC(function(x, y) x*y, list(f1=f1))

Test whether all expressions return TRUE

Description

This is typically used to combine multiple all.equal tests into a single test, in a test file called by do.test.

Usage

allTrue(...)

Arguments

Details

This is intended for use in test run by do.test. A typical test may contain lines that create one or more objects, followed by commands to check that those objects have the expected structure and/or that calculations were correct. By using allTrue, the tests can all be combined into the same expression that created the objects, so that if an error occurs it is easier to see where it occured.

Value

if all inputs are TRUE the value is TRUE. Otherwise a list indicating which arguments did not return TRUE, containing the actual values.

Author(s)

Tim Hesterberg

See Also

all.equal, do.test, expectStop, expectWarnings, identical.

Examples

# This is the type of expression that may be found in a test file
# to be run by do.test -- inside {} are lines that create one or
# more objects, followed by multiple tests (inside allTrue) that
# check those objects.
{
  y <- rnorm(30)
  x <- matrix(rnorm(60), ncol=2)
  fit <- lm(y~x)
  allTrue(# are important components included?
          all(is.element(c("coefficients", "residuals", "effects", "rank",
                           "fitted.values", "assign", "df.residual", "call"),
                         names(fit))),
          {
            # do coefficients match the algebraic form?
            # The algebraic form is inaccurate, so allow greater tolerance
            X <- cbind(1, x)
            all.equal(unname(fit$coefficients),
                      drop(solve( t(X) %*% X, t(X) %*% y)),
                      tol = 1e-5)
          },
          # are residuals computed correctly?
          all.equal(fit$residuals, y - X %*% fit$coefficients))
}
# The second test uses 'unname' to remove names and 'drop' to change a
# matrix to a vector, so the test should pass.
# The third test fails because fit$residuals is a vector with names
# while the %*% calculation returns a matrix.

Returns TRUE if missing values are round, otherwise FALSE

Description

Detection of missing values.

Usage

anyMissing(x)

Arguments

Value

logical, TRUE is missing values are detected.

See Also

is.missing, which.na.

Examples

anyMissing(1:5)
anyMissing(c(1, NA, 2))
anyMissing(list(a=1:3, b=NA))
anyMissing(data.frame(a=1:3, b=c(NA, 5:6)))

Covnert to rectangular object

Description

Rectangular data objects include matrices, data frames, and atomic vectors.

Arguments

Value

as.rectangular(x) returns x if x is already rectangular, or as.data.frame(x) if it is not.

See Also

is.rectangular.

Examples

## 
as.rectangular(list(a=1:10, b=11:20))

Convert to a seriesData

Description

Essentially just converts data to rectangular form, if possible.

Usage

asSeriesData(x)

Arguments

Value

rectangular conversion of input argument.

See Also

numericSequence, seriesData, seriesDataNew, seriesDataValid, signalSeries, as.data.frame.signalSeries, as.matrix.signalSeries, cumsum.signalSeries, deltat.signalSeries, diff.signalSeries, plot.signalSeries.

Internal Size of an integer

Description

Reports the size of an integer in bits.

Usage

bits.per.integer()

Details

Included for S-PLUS compatibility; R currently uses 32-bit integers, even on 64-bit machines.

Author(s)

Dunlap, W.M., and Taylor, C.F.

Examples

bits.per.integer()

Column and row summary function

Description

Summarizes the columns or rows of a rectangular object.

Usage

colIds(x, do.NULL=TRUE, prefix="col")
colMaxs(x, na.rm = FALSE, dims = 1, n = NULL)
colMedians(x, na.rm=FALSE)
colMins(x, na.rm = FALSE, dims = 1, n = NULL)
colRanges(x, na.rm = FALSE, dims = 1, n = NULL)
colVars(x, na.rm=FALSE, dims = 1, unbiased = TRUE,
        SumSquares = FALSE, weights = NULL, freq = NULL, n = NULL)
rowIds(x, do.NULL=TRUE, prefix="row")
rowMaxs(x, na.rm = FALSE, dims = 1, n = NULL)
rowMins(x, na.rm = FALSE, dims = 1, n = NULL)
rowRanges(x, na.rm = FALSE, dims = 1, n = NULL)

Arguments

Value

corresponding summary by row or by column.

See Also

See 'aggregate' package for alternative definitions of colStdevs, colVars, rowStdevs and rowVars functions.

Examples

## create a matrix, add dimensions, and obtain
## various summaries
x <- matrix(sin(1:20), nrow=4)
dimnames(x) <- list(c("a","b","c","d"), paste("col", 1:5))
colIds(x)
colMaxs(x)
colMedians(x)
colMins(x)
colRanges(x)
rowIds(x)
rowMaxs(x)
rowMins(x)
rowRanges(x)

Deparses input argument

Description

Deparse the argument into a single string, with at most maxchars characters. New lines are turned into blanks, and truncated results end in "....".

Usage

deparseText(expr, maxchars=30)

Arguments

Value

deparsed character string.

Examples

deparseText(args(lm), maxchars=20)

Test Functions and Expressions - for automated testing

Description

Expressions are parsed and evaluated from file. Each expression should evaluate to a logical TRUE. Otherwise, do.test() prints the expression and its value.

Usage

do.test(file, verbose=FALSE, strict=FALSE, local=FALSE, check)

Arguments

Details

A test file typically contains a sequence of expressions to test different aspects of a function or set of functions, including testing that each input argument is handled appropriately, error handling, the output has the expected structure, correct output under a number of combinations of inputs, and error handling (warning and stop invoked when appropriate and with appropriate messages). Each expression may contain multiple lines grouped using {}, where early lines may do computations and the last line checks for expected results, usually using all.equal.

Some expressions may be included that aren't intended to test anything by finishing them with TRUE, e.g. to read data: {read.table("data.txt"); TRUE} or to remove objects at the end of a test file: {rm(a, b, x, y); TRUE}.

We recommend including comments inside expressions to indicate the purpose of each test; then if errors occur the comments are printed too.

To compare just numbers, not names or matrix dimensions, functions unname and drop are useful.

To exclude certain components or attributes from the comparison the function all.equal.excluding is useful. This is defined in the examples below.

Each test should run silently if everything is working correctly; there should be nothing printed. expectWarnings can be used to intercept warning statements.

Value

NULL

See Also

all.equal, allTrue, drop, expectStop, expectWarnings, identical, Quote, unname

Examples

## Not run: 
# Create a toy test file, and run it
cat('{all.equal(24/8, 3)}',
    '{all.equal(5, 6)}',      # this one will fail
    'expectWarnings( { # Test subscript replacement ',
    '  x <- data.frame(a=1:3,b=2:4)',
    '  x[,3] <- x',
    '  all.equal(ncol(x), 3)',
    '}, expected = "provided 2 variables to replace 1 var")',
    'expectStop(lm(5), expected = "invalid formula")',
    '{ rm(x) ; TRUE }',       # cleanup at end of test
    sep="\n", file = "testfile.t")
do.test("testfile.t")
## ------- Test file: testfile.t ---------
## {all.equal(5, 6)}
## [1] "Mean relative difference: 0.2"
#
# The test that fails is printed, with the results of the test.
# In R 2.6.1 the subscript replacement test above also fails
# (bug reported 14 Jan 2008), resulting in the additional printout:
## expectWarnings( {
##   x <- data.frame(a=1:3,b=2:4)
##   x[,3] <- x
##   all.equal(ncol(x), 3)
## }, expected = "provided 2 variables to replace 1 var")
## $`Test result`
## [1] "Mean relative  difference: 0.25"

## End(Not run)

# This function is useful in some tests:
all.equal.excluding <- function(x, y, ..., excluding=NULL, attrs=NULL){
  # Like all.equal, but exclude components in `excluding',
  #   and excluding attributes named in `attrs'.
  #
  # `excluding' and `attrs' should be character, names of components 
  #   and attributes.
  # 
  # For example:
  #   all.equal.excluding(obj1, obj2, excluding = c("call", "x"))
  for(i in intersect(names(x), excluding)) x[[i]] <- NULL
  for(i in intersect(names(y), excluding)) y[[i]] <- NULL
  for(i in intersect(names(attributes(x)), attrs)) attr(x,i) <- NULL
  for(i in intersect(names(attributes(y)), attrs)) attr(y,i) <- NULL
  all.equal(x,y, ...)
}
# Test if two objects are the same except for "call" and "x":
data <- data.frame(x = 1:20, y = exp(1:20/20))
fit1 <- lm(y ~ x, data = data, x=TRUE)
fit2 <- update(fit1, x=)
all.equal.excluding(fit1, fit2, excluding = c("call", "x"))

Test whether expected stop() or warning() messages are produced.

Description

These functions are for use in automated testing using do.test, to test whether function give specified stop and warning messages.

Usage

expectStop(expr, expected = NULL)
expectWarnings(expr, expected)

Arguments

Details

expectStop is useful for checking error checking; that a function stops when it should, and gives the right message. For example, this may be in a file called by do.test:

{
  expectStop(var(1:5, 1:4), "incompatible")
}

The function returns TRUE if

• a stop() occurs, and

• the error message is expected.

Otherwise it returns appropriate messages.

expectStop intercepts the error. Execution continues, and assignments made earlier are committed.

Similarly, expectWarnings is useful to check that a function gives appropriate warnings. For example, this may be in a file called by do.test:

expectWarnings(
  {
    object1 <- (code generating warning messages);
    object2 <- (code generating possibly other warning messages);
    all.equal(object1, object2)
  },
  c("expected warning 1",
    "expected warning 2"))

The function returns TRUE if

• expr evaluates to TRUE; and

• each warning message produced by evaluating expr contains as a substring an element of expected, and each element of expected is a substring of at least one of the produced warning messages.

Otherwise it returns a list with components describing the test failures. Normal printing of warning messages is suppressed.

It is possible to test for warnings and a stop in a single expression, by nesting calls to the two functions.

Value

If all tests pass, then TRUE. Otherwise expectStop returns character strings describing the failure, while expectWarnings returns a list with one or more of the following components:

Author(s)

Tim Hesterberg

See Also

do.test

Examples

# Expressions like the following would typically be included in a file
# that is called by do.test

expectStop(lm(5), expected = "invalid formula")

expectStop(cov2cor( matrix(2:1) ),
           expected = "'V' is not a square numeric matrix")

expectWarnings( # Test subscript replacement; should discard extra
                # column and give a warning
  {
    x <- data.frame(a=1:3,b=2:4)
    x[,3] <- x
    all.equal(ncol(x), 3)
  },
  expected = "provided 2 variables to replace 1 var")

# Test for a warning and stop together:
{
  f <- function(x){
    warning("a warning")
    stop("a stop")
  }
  expectStop( expectWarnings( f(3), expected = "a warning"),
              expected = "a stop")
}
# The definition of f and the call to expectStop are included here
# within {} because that is how they would typically be grouped in
# a file that is called by do.test.  Also note that f has been saved
# (the assignment of f is committed, rather than aborted).

Conditional Data Selection

Description

This is equivalent to {if(test) x else y} . The main advantage of using this function is better formatting, and a more natural syntax when the result is being assigned; see examples below.

With 5 arguments, this is equivalent to {if(test1) x else if(test2) u else v} (where arguments are given by name, not position).

Usage

ifelse1(test, x, y, ...)

Arguments

Details

test should be a scalar logical, and only one of x or y is evaluated, depending on whether test = TRUE or test = FALSE, and x and y may be any objects. In contrast, for ifelse, test is normally a vector, both x and y are evaluated, even if not used, and x and y are vectors the same length as test.

Value

with three arguments, one of x or y. With k arguments, one of arguments 2, 4, ..., k-1, k.

See Also

ifelse, if.

Examples

ifelse1(TRUE, "cat", "dog")
ifelse1(FALSE, "one", FALSE, "two", "three")

Infinite

Description

is.inf returns a vector of the same length as the input object, indicating which elements are infinite (not missing).

Usage

  is.inf(x)

Arguments

Details

This calls is.infinite.

This returns a vector of the same length as x; the jth element is TRUE if x[j] is infinite (i.e., equal to one of Inf or -Inf). This will be FALSE if x is not numeric or complex. Complex numbers are infinite if either the real and imaginary part is.

See Also

is.infinite

Examples

is.inf(Inf)
# [1] TRUE
is.inf(NA)
# [1] FALSE
is.inf(1)
# [1] FALSE

Check for missing values

Description

Check to see whether the input is either NA or a vector of length 0.

Usage

is.missing(x)

Arguments

Value

TRUE if the input is a vector of length 0; is.na(x) otherwise.

See Also

anyMissing.

Examples

is.missing(numeric(0))
is.missing(NA)
is.missing(c(1,2,3,NA,5))

Check Values

Description

Returns a logical vector describing if a numeric elements is a number.

Usage

is.number(x)

Arguments

Details

is.number is TRUE if the value is numeric or complex and is not missing (NA or NaN).

Examples

is.number(32)
# [1] TRUE
is.number(matrix(1:20, nrow=2))
#      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
# [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE  TRUE
# [2,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE  TRUE
is.number(list(matrix(1:20, nrow=2), 1:4))
# [1] TRUE TRUE
is.number('s')
# [1] TRUE

If a value can be ordered

Description

is.orderable(x) returns !is.na().

Usage

is.orderable(x)

Arguments

Details

x should not be a list; in that case the behavior currently differs between S-PLUS and R.

Value

is.orderable returns a logical vector of the same length as x.

See Also

is.na

Examples

x <- c(1, 4, NA, 0, 5)
is.orderable(x)
# [1]  TRUE  TRUE FALSE  TRUE  TRUE

Checks for object rectangularity

Description

Returns TRUE if the input object is rectangular.

Usage

is.rectangular(x)

Arguments

Value

logical, returns TRUE if input object is rectangular.

See Also

as.rectangular.

Examples

is.rectangular(matrix(1:12, nrow=3))
is.rectangular(list(1:3,2:4))
is.rectangular(as.rectangular(list(1:3,2:4)))

Tests whether an object is a vector or not

Description

The is.vector function returns a FALSE value in some cases where intuitively one might expect a TRUE value to be returned. For example, is.vector(z) returns FALSE for each of the following:

i

z <- 1:3;names(z) <- 1:3

ii

z <- matrix(1:3, nrow=1)

iii

z <- matrix(1:3, ncol=1)

These results are not necessarily incorrect, they are just one interpretion of the definition of a vector. Contrarily, the isNumericAtomicVector(z) function returns TRUE for each of the above examples. Thus, isNumericAtomicVector expands the basic definition of a vector to allow matrices containing a single row or column and named vectors. Also, unlike is.vector, isNumericAtomicVector returns FALSE for objects of class list.

Usage

isNumericAtomicVector(x)

Arguments

Value

a vector of character strings containing the result. The length of this vector is equal to length(x).

Examples

## cases where isNumericAtomicVector returns 
## TRUE 
z <- 1:3;names(z) <- letters[1:3]
isNumericAtomicVector(z)
isNumericAtomicVector(matrix(1:3, nrow=1))
isNumericAtomicVector(matrix(1:3, ncol=1))
isNumericAtomicVector(1:5)
isNumericAtomicVector(letters)

## cases where isNumericAtomicVector returns 
## FALSE 
isNumericAtomicVector(list(1:3))
isNumericAtomicVector(data.frame(1:3,2:4))
isNumericAtomicVector(matrix(1:12, nrow=4))

Case conversion

Description

Convert text to lower or upper case.

Usage

lowerCase(x)
upperCase(x)

Arguments

Value

a character string coerced to the specified case.

See Also

casefold, tolower, toupper.

Examples

x <- "A dog and a cat"
lowerCase(x)
upperCase(x)

Determine Number of Arguments to Function

Description

count the number of ... arguments passed.

Usage

nDotArgs(...)

Arguments

Value

the number of ... arguments in the call to the function which calls nDotArgs.

See Also

nargs.

Examples

myfun <- function(..., a=4) nDotArgs(...)
myfun()                 ## returns 0 
myfun(1:3,"bear")       ## returns 2 
myfun(a=5, 1:3, "bear") ## returns 2 (excludes a)

Object dimensions

Description

Returns number of rows or number of columns of rectangular input object.

Usage

numCols(x)
numRows(x)

Arguments

Value

the number of rows or columns of the input object.

See Also

as.data.frame,matrix,Subscript,nrow,dimnames.

Examples

x <- matrix(1:12, nrow=3)
numCols(x)
numRows(x)

Constructor for numericSequence Class

Description

Constructor function for numericSequence objects. At least three of the four arguments must be supplied, unless the function is called with no arguments.

Usage

numericSequence(from, to, by, length.)

Arguments

Value

a numericSequence object with properties given by the arguments, or the default numericSequence if no arguments are supplied.

S3 METHODS

S4

supported S4 methods include: Math, Math2, Ops, Summary, [, [[, duplicated, is.na, length, match, mean, median, quantile, show, sort, summary, unique, which.na. There are also [ and [[ S4 style replacment methods available.

as

s4 style conversion methods ala as(x, foo) where foo is one of the following conversion classes: "character", "integer", "numeric".

See Also

seriesData, asSeriesData, seriesDataNew, seriesDataValid, signalSeries, as.data.frame.signalSeries, as.matrix.signalSeries, cumsum.signalSeries, deltat.signalSeries, diff.signalSeries, plot.signalSeries.

Methods for class signalSeries in package splus2R

Description

Methods for class signalSeries in package splus2R

Details

Supported (generic) methods include:

as

Target classes: character, integer, numeric.

Math

Math functions

Math2

Math2 functions

Ops

Ops functions

Data access and replacement

Single- and double-bracket access and replacement functions

duplicated

Find duplciated entries in sequence

is.na

Test for NA entries in sequence

length

Length of sequence

match

Matching functions for sequence

mean

Mean of sequence

median

Median of sequence

quantile

Quantile of sequence

rev

Reverse sequence

show

Show the sequence

sort

Sort the sequence

unique

Find unique sequence entries

summary

Summarize the sequence

unique

Find unique elements of a sequence

which.na

Find NA entries in the sequence

See Also

signalSeries.

Class conversion

Description

the old-style version of function unclass; it sets oldClass to NULL, rather than class.

Usage

oldUnclass(x)

Arguments

Value

unclassed version of input object.

See Also

oldClass,unclass.

Examples

oldUnclass(matrix(1:10))

Local maxima

Description

Finds the local maxima in a vector, or time series, or in each column of a matrix.

Usage

peaks(x, span=3, strict=TRUE, endbehavior=0)

Arguments

Value

an object like x of logical values. Values that are TRUE correspond to local peaks in the data.

Note

The value of endbehavior acts as follows: 0 is as if the series had runs of halfwidth values of Inf attached to either end, 1 is as if there were runs of halfwidth values of -Inf attached, and 2 is as if there were runs ofhalfwidth values of NA attached.

See Also

max, cummax, pmax.

Examples

x <- as.vector(sunspots)
z <- peaks(x, span=51)
plot(x, type="l")
abline(v=which(z), col="red", lty="dashed")

Positions of signalSeries objects

Description

Access the positions of series objects.

Usage

positions(object)

Arguments

Value

the positions associated with the input time series: an object of class numericSequence.

See Also

seriesData, signalSeries.

Examples

x <- signalSeries(1:10, from=pi, by=0.1)
positions(x)

Multivariate Normal (Gaussian) Distribution

Description

Random generation for the multivariate normal (also called Gaussian) distribution.

Usage

rmvnorm(n, mean=rep(0,d), cov=diag(d), sd, rho, d=2)

Arguments

Value

random sample ( rmvnorm) for the multivariate normal distribution.

See Also

rnorm,set.seed.

Examples

## 5 rows and 2 independent columns 
rmvnorm(5)

## 5 rows and 3 independent columns 
rmvnorm(5, mean=c(9,3,1))

## 2 columns, std. dev. 1, correlation .9 
rmvnorm(5, rho=.9)

## specify variable means and covariance matrix 
rmvnorm(5, mean=c(9,3), cov=matrix(c(4,1,1,2), 2))

Access Data Of series Objects

Description

Access the data slot of series objects.

Usage

seriesData(object)

Arguments

Value

the data slot of object.

S3 METHODS

[<-

single level data replacement method.

Usage: x[1:4] <- 1:4)

See Also

numericSequence, asSeriesData, seriesDataNew, seriesDataValid, signalSeries, as.data.frame.signalSeries, as.matrix.signalSeries, cumsum.signalSeries, deltat.signalSeries, diff.signalSeries, plot.signalSeries.

Creates template for new seriesData object

Description

Creates template for new seriesData object., basically a NULL matrix.

Usage

seriesDataNew()

Value

nULL matrix.

See Also

numericSequence, seriesData, asSeriesData, seriesDataValid, signalSeries, as.data.frame.signalSeries, as.matrix.signalSeries, cumsum.signalSeries, deltat.signalSeries, diff.signalSeries, plot.signalSeries.

Validates the structure of seriesData object

Description

Checks to see if input object is rectangular.

Usage

seriesDataValid(object)

Arguments

Value

logical value, TRUE is a valid seriesData object.

See Also

numericSequence, seriesData, asSeriesData, seriesDataNew, signalSeries, as.data.frame.signalSeries, as.matrix.signalSeries, cumsum.signalSeries, deltat.signalSeries, diff.signalSeries, plot.signalSeries.

Describe the structure of an object

Description

Describe the structure of an object, recursively.

Usage

showStructure(x, maxlen = 20, describeAttributes = TRUE,
   short = NULL, prefix = "", attri = FALSE, ...)

Arguments

Details

This supports recursive objects, using recursive calls. Each level of recursion is indented two additional spaces. List components are shown with $, slots with @, and attributes with &.

Value

This prints a description; it doesn't return anything useful.

Author(s)

Tim Hesterberg

See Also

names, str.

Examples

a <- c(m=1, n=2)
b <- diag(1:3)
cc <- cbind(a=1:5, b=2:6, c=letters[1:5])
d <- data.frame(cc)
attr(d, "dup.row.names") <- TRUE
e <- ts(1:10, frequency = 4, start = c(1959, 2))
f <- list(a,b=b)
setClass("track", representation(x="numeric", y="numeric"))
g <- new("track", x=1:5, y=1:5)

showStructure(a)
showStructure(b)
showStructure(cc)
showStructure(d)
showStructure(e)
showStructure(f)
showStructure(g)  # prints with @ rather than $
showStructure(list(a=a, b=b))
showStructure(list(cc=cc, d, list(a,e)))

Constructor function for the signalSeries class

Description

Construct a signalSeries object from positions and data, or return an empty signalSeries object.

Usage

signalSeries(data, positions., units, units.position, from=1, by=1)

Arguments

Details

If no arguments are supplied, the default (empty) signalSeries object is returned. Otherwise, a signalSeries object is created with the given positions and data, and units if they are supplied. As an alternative to supplying the positions directly, they can be supplied by giving from and by, in which case the positions are generated as a numeric sequence with the right length to match the data.

Value

a signalSeries object with the given data and positions.

S3 METHODS

as

s4 style conversion to another class ala as(x, foo) where foo is any of the following: "character", "complex", "data.frame", "integer", "logical", "matrix", "numeric", "vector".

as.data.frame

convert to a data.frame.

as.matrix

convert to a matrix.

cumsum

cumulative summation over series.

deltat

samlig intervals of series.

diff

differencing operation applied to the series. Usage: diff(x, ...) where the ... are additional arguments sent directly to the diff function.

plot

plots the series.

See Also

numericSequence.

Methods for class signalSeries in package splus2R

Description

Methods for class signalSeries in package splus2R

Details

Supported (generic) methods include:

as

Target classes: complex, character, matrix, numeric, logical, integer, vector, and data.frame. S3 style methods include as.numeric and as.vector.

Arith

Arithmetic functions

Compare

Comparison functions

Logic

Logical functions

Math

Math functions

Math2

Math2 functions

Ops

Ops functions

dim

Dimension of series (NULL is returned)

length

Length of series

mean

Mean of series

min

Minimum of series

ncol

Number of columns of series (1 is returned)

plot

Plot the series

show

Display the series

sum

Sum the series.

summary

Sumamrize the series object

See Also

numericSequence.

Standard deviation

Description

Calculates the standard deviation of a series.

Usage

stdev(x, ...)

Arguments

Value

the standard deviation of the input series.

See Also

var.

Examples

stdev(c(pi, 1, 3))

Uniform Rectangular Data Subscripting Function

Description

subscript2d is for subscripting matrices and data frames.

Usage

subscript2d(x,i,j)
subscript2dMatrix(x,i,j)
subscript2dDataFrame(x,i,j)

Arguments

Value

subscript2d(x,i,j) is like x[i,j,drop=F], except that it allows x[,1] (for example) for atomic vectors as well, and it always returns an object of the same class as x (that is, it does not support a drop argument).

See Also

as.data.frame, matrix.

Examples

x <- 1:10
subscript2d(x,3,1)
subscript2d(data.frame(x),3,1)
subscript2d(matrix(x),3,1)

p-norm of a vector

Description

Computes the p-norm of a vector

Usage

vecnorm(x, p=2)

Arguments

Value

requested p-norm of input vector.

See Also

rnorm.

Examples

## compare 2-norm calculations 
x <- rnorm(100)
sqrt(sum(x*x))
vecnorm(x)

## compare 2-norm of series which sums to Inf. The 
## vecnorm returns a finite value in this case. 
x <- rep(sqrt(.Machine$double.xmax), 4)
sqrt(sum(x*x))
vecnorm(x)

## 1-norm comparison 
sum(abs(x))
vecnorm(x, p=1)

## L-infinity norm comparison 
max(abs(x))
vecnorm(x, p=Inf)

Determine Which Values are Missing Values

Description

Returns an integer vector describing which values in the input vector, if any, are missing.

Usage

which.na(x)

Arguments

Value

an integer vector describing which values in the input vector, if any, are missing.

See Also

is.na.

Examples

## A non-zero number divided by zero creates 
## infinity, zero over zero creates a NaN 
weird.values <- c(1/0, -20.9/0, 0/0, NA)

## Produces: 3 4. In this example, the which.na 
## expression and the subscript expression 
## involving is.na should return the same value 
which.na(weird.values)
seq(along=weird.values)[is.na(weird.values)]