Package 'NCmisc'

Description

A set of handy functions. Includes a versatile one line progress bar, one line function timer with detailed output, time delay function, text histogram, object preview, CRAN package search, simpler package installer, Linux command install check, a flexible Mode function, top function, simulation of correlated data, and more.

Details

A package of general purpose functions that might save time or help tidy up code. Some of these functions are similar to existing functions but are simpler to use or have more features (e.g, timeit and loop.tracker reduce an initialisation, 'during' and close three-line call structure, to a single function call. Also, some of these functions are useful for building packages and pipelines, for instance: Header(), to provide strong visual deliniation between procedures in console output, by an ascii bordered heading; loop.tracker() to track the progress of loops (called with only 1 line of code), with the option to periodically backup a key object during the loop; estimate.memory() to determine whether the object may exceed some threshold before creating it, timeit(), a one line wrapper for proftools which gives a detailed breakdown of time taken, and time within each function called during a procedure; and check.linux.install() to verify installation status of terminal commands before using system(), top() to examine current memory and CPU usage [using the system 'top' command]. prv() is useful for debugging as it allows a detailed preview of objects, and is as easy as placing print statements within loops/functions but gives more information, and gives compact output for large objects. For testing sim.cor() provides a simple way to simulate a correlated data matrix, as often this is more realistic than completely random data. Otherwise summarise.r.datasets gives a list of all available datasets and their structure and dimensionality.

List of key functions:

• check.linux.install Check whether a given system command is installed (e.g, bash)

• comma.list Nicely format output lists with comma separation and length control

• comify Function to add commas for large numbers

• cor.with simulate a variable with a specified correlation to an existing variable

• Dim same as dim() function but works for more objects, including vectors

• dup.pairs Obtain an ordered index of all instances of values with duplicates

• estimate.memory Estimate the memory required for an object

• exists.not.function same as exists() function but ignores functions

• extend.pc Extend an interval by percentage

• fakeLines Create randomized lines of text for testing

• force.percentage Force argument to be a decimal percentage

• force.scalar Force argument to be a scalar

• get.distinct.cols Return up to 22 distinct colours

• getRepositories Return list of available repositories

• has.method Determine whether a function can be applied to an S4 class/object

• headl A good way to preview large lists

• Header Print heading text with a border

• is.vec.logical Test whether vector is logical independent of type

• is.vec.numeric Test whether vector is numeric independent of type

• list.functions.in.file Show all functions used in an R script file, by package

• list.to.env Inserts new variables in current environment from a named list

• loess.scatter Draw a scatterplot with a fit line

• loop.tracker Creates a progess bar within a loop with only 1 line

• Mode Find the mode(s) of a vector

• must.use.package Do everything possible to load an R package

• narm Return an object with missing values removed

• nearest.to Similar to base match function but picks nearest instead of exact match

• Numerify Convert only suitable columns to numeric format in data.frame

• out.of Simplify outputting fractions/percentages

• p.to.Z Convert p-values to Z-scores

• packages.loaded quietly test whether packages are loaded without using require

• pad.left Print a vector with appropriate padding so each has equal char length

• pctile Find data thresholds corresponding to percentiles

• ppa Posterior probability for p-values

• preview same as prv, but enter arguments as strings

• prv.large tidy representation for large matrices/data.frames

• prv compact preview of objects (more complete than 'print')

• replace.missing.df replace missing values in data.frame automatically

• Rfile.index Create an index file for an R function file

• rmv.names Remove names from object

• rmv.spc Remove leading and trailing spaces (or other character)

• search.cran Search all CRAN packages for those containing keyword(s)

• sim.cor simulate a correlated dataset

• simple.date generate a string with compact summary of date/time

• spc Print a character a specified number of times

• standardize Convert a numeric vector to Z-scores

• Substitute multivariable version of substitute (base)

• summary2 Extension of base:summary that adds SD, SE and keeps names fixed and cleaner

• summarise.r.datasets show and summarise all available example datasets

• table2d Extension of base:table that forces fixed rows and columns

• textogram Make an ascii histogram in the console

• timeit Times an expression, with breakdown of time spent in functions

• toheader Return a string with each first letter of each word in upper case

• top report on CPU and memory usage, overall or by process

• Unlist Unlist a list, starting only from a set depth

• wait Wait for a period of time

• which.outlier Return indexes of univariate outliers

• Z.to.p Convert Z-scores to p-values

Author(s)

Nicholas Cooper

Maintainer: Nicholas Cooper <[email protected]>

See Also

reader ~~

Examples

#text histogram suited to working from a console without GUI graphics
textogram(rnorm(10000),range=c(-3,3))
# wait 0.2 seconds
wait(0.2,silent=FALSE) 
# see whether a system command is installed
check.linux.install("sed") 
# a nice progress bar
max <- 100; for (cc in 1:max) { loop.tracker(cc,max); wait(0.004,"s") }
# nice header
Header(c("SPACE","The final frontier"))
# memory req'd for proposed or actual object
estimate.memory(matrix(rnorm(100),nrow=10)) 
# a mode function (there isn't one included as part of base)
Mode(c(1,2,3,3,4,4,4))
# search for packages containing text, eg, 'misc'
search.cran("misc", repos="http://cran.ma.imperial.ac.uk/") 
# simulate a correlated dataset
corDat <- sim.cor(200,5)
cor(corDat) # show correlation matrix
prv(corDat) # show compact preview of matrix
# Dim() versus dim()
Dim(1:10); dim(1:10)
# find nearest match in a vector:
nearest.to(1:100, 50.5)

Description

Tests whether a command is installed and callable by system(). Will return a warning if run on windows when linux.more=TRUE

Usage

check.linux.install(cmd = c("plink", "perl", "sed"), linux.mode = FALSE)

Arguments

Value

returns true or false for each command in 'cmd'

Author(s)

Nicholas Cooper [email protected]

Examples

check.linux.install("R") # should be standard
check.linux.install(c("perl","sed","fake-cmd"))

Description

Often for nice presentation of genomic locations it is helpful to insert commas every 3 digits when numbers are large. This function makes it simple and allows specification of digits if a decimal number is in use.

Usage

comify(x, digits = 2)

Arguments

Value

returns a character vector with commas inserted every 3 digits

Examples

comify("23432")
comify(x=c(1,25,306,999,1000,43434,732454,65372345326))
comify(23432.123456)
comify(23432.123456,digits=0)

Description

Often for nice presentation of error messages you wish to display a list of values. This adds commas between entries and will truncate the list above a length of 50 items with an ellipsis. Very simple but convenient function.

Usage

comma.list(X)

Arguments

Value

string with entries separated by commas, and if long, entries skipped indicated by an ellipsis.

Examples

comma.list(1:100)
cat("The following entries were ignored: ", comma.list(c(1,7,10:14)), "\n")

Description

Simulate a variable correlated at level 'r' with cector x (of the same length). Can either 'preserve' the mean and standard-deviation, leave standardizeed, or select new mean 'mn' and standard deviation 'st'.

Usage

cor.with(x, r = 0.5, preserve = FALSE, mn = NA, st = NA)

Arguments

Value

return the new variable with an expected correlation of 'r' with x

Author(s)

Nicholas Cooper

References

http://www.uvm.edu/~dhowell/StatPages/More_Stuff/CorrGen.html

See Also

sim.cor

Examples

X <- rnorm(10,100,14)
cor.with(X,r=.5) # create a variable correlated .5 with X
cor(X,cor.with(X)) # check the actual correlation
# some variability in the actual correlation, so run 1000 times:
print(mean(replicate(1000,{cor(X,cor.with(X))})))
cor.with(X,preserve=TRUE) # preserve original mean and standard deviation
X[c(4,10)] <- NA # works fine with NAs, but new var will have same missing
cor.with(X,mn=50,st=2) # specify new mean and standard deviation

Description

A more general 'dim' function. For arrays simply calls the dim() function, but for other data types, tries to provide an equivalent, for instance will call length(x) for vectors, and will recursively report dims for lists, and will attempt something sensible for other datatypes.

Usage

Dim(x, cat.lists = TRUE)

Arguments

Value

dimension(s) of the object

See Also

prv, preview

Examples

# create variables of different types to show output styles #
Dim(193)
Dim(1:10)
testvar <- matrix(rnorm(100),nrow=25)
Dim(matrix(rnorm(100),nrow=25))
Dim(list(first="test",second=testvar,third=100:110))
Dim(list(first="test",second=testvar,third=100:110),FALSE)

Description

The standard 'duplicated' function, called with which(duplicated(x)) will only return the indexes of the extra values, not the first instances. For instance in the sequence: A,B,A,C,D,B,E; it would return: 3,6. This function will also return the first instances, so in this example would give: 1,3,2,6 [note it will also be ordered]. This index can be helpful for diagnosis if duplicates are unexpected, for instance in a data.frame, and you wish to compare the differences between the rows with the duplicate values occuring. Also, duplicate values are sorted to be together in the listing, which can help for manual troubleshooting of undesired duplicates.

Usage

dup.pairs(x)

Arguments

Value

vector of indices of which values in 'x' are duplicates (including the first observed value in pairs, or sets of >2), ordered by set, then by appearance in x.

Examples

set <- c(1,1,2,2,3,4,5,6,2,2,2,2,12,1,3,3,1)
dup.pairs(set) # shows the indexes (ordered) of duplicated values
set[dup.pairs(set)] # shows the values that were duplicated (only 1's, 2's and 3's)

Description

Can enter an existing object or just the dimensions or total length of a proposed object. The estimate is based on the object being of numeric type. Integers use half the space of numeric, raw() use 1/8th of the space. Factors and characters can vary, although factors will always use less than numeric, and character variables may easily use up to twice as much depending on the length [nchar()] of each element.

Usage

estimate.memory(
  dat,
  integer = FALSE,
  raw = FALSE,
  unit = c("gb", "mb", "kb", "b"),
  add.unit = FALSE
)

Arguments

Value

returns the minimum memory requirement to store and object of the specified size, as a numeric scalar, in gigabytes (default) or else using the units specified by 'unit', and if add.unit = TRUE, then the result will be character type instead of numeric, with the units appended.

Author(s)

Nicholas Cooper [email protected]

Examples

myMatrix <- matrix(rnorm(100),nrow=10)
myVec <- sample(1:1000)
estimate.memory(myMatrix,unit="bytes") # enter a matrix object
estimate.memory(myVec,unit="kb" ,add.unit=TRUE) # enter a vector object
estimate.memory(c(10,10,10,10,10),unit="kb") # 5 dimensional array
estimate.memory(c(10^6,10^4), add.unit=TRUE) # large matrix
estimate.memory(5.4*10^8, add.unit=TRUE)  # entering argument as # total cells, rather than dims
estimate.memory(5.4*10^8, integer=TRUE, add.unit=TRUE)
estimate.memory(5.4*10^8, raw=TRUE, add.unit=TRUE)
estimate.memory(5.4*10^8, TRUE, TRUE, add.unit=TRUE) #  'integer' overrides 'raw'

Description

Does object exist ignoring functions

The exists() function can tell you whether an object exists at all, or whether an object exists with a certain type, but it can be useful to know whether an object exists as genuine data (and not a function) which can be important when a variable or object is accidently or intentionally given the same name as a function. This function usually returns a logical value as to the existence of the object (ignoring functions) but can also be set to return the non-function type if the object exists.

Usage

exists.not.function(x, ret.type = FALSE)

Arguments

Value

logical, whether non-function object exists, or else the type if ret.type=TRUE

Author(s)

Nicholas Cooper

Examples

x <- "test"
# the standard exists function, for all modes, correct mode, and other modes:
exists("x")
exists("x",mode="character")
exists("x",mode="numeric")
# standard case for a non-function variable
exists.not.function("x",TRUE)
# compare results for a non-existent variable
exists("aVarNotSeen")
exists.not.function("aVarNotSeen")
# compare results for variable that is a function
exists("mean")
exists.not.function("mean")
# define a variable with same name as a function
mean <- 1.4
# exists.not.function returns the type of the variable ignoring the function of the same name
exists.not.function("mean",TRUE)
exists("mean",mode="function")
exists("mean",mode="numeric")

Description

For various reasons, such as applying windows, setting custom range limits for plots, it may be desirable to extend an interval by a certain percentage.

Usage

extend.pc(X, pc = 0.5, pos = TRUE, neg = TRUE, swap = FALSE)

Arguments

Examples

extend.pc(c(2,10),0.25) # extend X symmetrically
extend.pc(c(2:10),0.25) # extend the range of X
# the following 3 examples extend X by 1% only in the 'positive' direction
extend.pc(c(25000,55000),.01,neg=FALSE) # standard positive extension
extend.pc(c(55000,25000),.01,neg=FALSE) # ranges in reverse order, not swapped
extend.pc(c(55000,25000),.01,neg=FALSE,swap=TRUE) # ranges in reverse order, swapped

Description

Returns randomized input as if reading lines from a file, like 'readLines()' Can be used to test i/o functions, robustness.

Usage

fakeLines(
  max.lines = 10,
  max.chars = 100,
  pc.space = 0.35,
  delim = " ",
  can.null = TRUE
)

Arguments

Value

a vector of character entries up 'max.chars' long, or sometimes only NULL if can.null=TRUE

Author(s)

Nicholas Cooper

Examples

fakeLines() # should produce between zero and ten lines of random text, 35% of which are spaces

Description

Wrapper for the bash command 'split' that can separate a text file into multiple roughly equal sized parts. This function removes the need to remember syntax and suffixes of the bash command

Usage

file.split(
  fn,
  size = 50000,
  same.dir = FALSE,
  verbose = TRUE,
  suf = "part",
  win = TRUE
)

Arguments

Value

returns the list of file names produced (including path)

Author(s)

Nicholas Cooper

Examples

orig.dir <- getwd(); setwd(tempdir()); # move to temporary dir
file.name <- "myfile.txt"
writeLines(fakeLines(max.lines=1000),con=file.name)
new.files <- file.split(file.name,size=50)
unlink(new.files); unlink(file.name)
setwd(orig.dir) # reset working dir to original

Description

Sometimes it is nice to be able to take a percentage as an argument and not have to specify whether it should be entered as a number between 0 and 100, e.g, 50 = 50 than 1 and less than 100 will be divided by 100. Anything outside 0,100 will be set to 0,100 respectively.

Usage

force.percentage(x, default = 0.5)

Arguments

Value

the object x if already legal, first element if a vector, the min or max value if x is outside the specified bounds, or the value of default otherwise

See Also

force.scalar

Examples

# create variables of different types to show output styles #
force.percentage(45)
force.percentage(450)
force.percentage(.45)
force.percentage(-45)
force.percentage("twenty")
force.percentage(NA,default=0.25)

Description

Sometimes arguments must be numeric, scalar and within a certain range. Rather than using many if statements, this will do everything possible to coerce input to a scalar, failing that will replace with a default value. Can also provide a maximum and minimum range that the result must lie within.

Usage

force.scalar(x, default = 1, min = -10^10, max = 10^10)

Arguments

Value

the object x if already legal, first element if a vector, the min or max value if x is outside the specified bounds, or the value of default otherwise

See Also

force.percentage

Examples

force.scalar(1.5)
force.scalar(NULL,default=.5)
force.scalar(NA,default=.4,min=5,max=10) # default is outside range!
force.scalar(rnorm(1000))
force.scalar(101,max=50)
force.scalar(list(0.4,1,2,3,4,"test"))
force.scalar(data.frame(test=c(1,2,3),name=c("test","me","few")))
force.scalar(Inf)

Description

Useful if you want to colour 22 autosomes, etc, because most R colour palettes only provide 12 or fewer colours, or else provide, a gradient which is not distinguishable for discrete categories. Manually curated so the most similar colours aren't side by side.

Usage

get.distinct.cols(n = 22)

Arguments

Value

returns vector of n colours

Author(s)

Nicholas Cooper [email protected]

Examples

get.distinct.cols(10)
plot(1:22,pch=19,col=get.distinct.cols(22))

Description

In addition to the default CRAN repository, there are other repositories such as R-Forge, Omegahat, and bioConductor (which is split in to software, annotation, experiments and extras). This function allows you to retrieve which are available. This function complements (and takes code from) utils::setRepositories(), which will just set, not return which are available, but see there for more information about how this works. Detecting the available repositories can be useful to precede a call to setRepositories, and allows you to utilise these repositories without calling setRepositories (which is hard to reverse). This function can be used to expand the search space of the function search.cran() to include bioconductor packages.

Usage

getRepositories(ind = NULL, table = FALSE)

Arguments

Value

list of repositories with URLS, note that it is the URL that works best for use for passing a value for 'repos' to various functions.

Author(s)

Nicholas Cooper [email protected]

Examples

repos <- "http://cran.ma.imperial.ac.uk/" # OR: repos <- getOption("repos")
getRepositories(table=TRUE) # shows all available
getRepositories(2:5,FALSE) # returns index for all bioconductor repositories (on my system at least)
# does not find this bioconductor package on CRAN
## not run # search.cran("genoset",repos=getRepositories(1))
# should now, because all repositories are used
## not run # search.cran("genoset",repos=getRepositories())

Description

Wrapper for 'showMethods', allows easy testing whether a function (can be specified as a string, or the actual function itself (FUN)) can be applied to a specific object or class of objects (CLASS)

Usage

has.method(FUN, CLASS, false.if.error = FALSE, ...)

Arguments

Value

returns logical (TRUE/FALSE), or if the function is not S4 will return an error, although this could potentially be because the function's package has not been loaded.

Examples

require(Matrix); require(methods)
has.method("t","dgeMatrix") # t() is the transpose method for a dgeMatrix object
has.method(t,"dgeMatrix") # also works without quotes for the method
m.example <- as(matrix(rnorm(100),ncol=5),"dgeMatrix")
has.method(t, m.example) # works with an instance of an object type too
has.method("band", m.example) # band is a function for a 'denseMatrix' but not 'dgeMatrix'
## not run # has.method("notAFunction","GRanges") # should return error
## not run # has.method("notAFunction","GRanges",TRUE) # should return FALSE and a warning

Description

Makes highly visible headings, can separately horizontal, vertical and corner characters

Usage

Header(txt, h = "=", v = h, corner = h, align = "center")

Arguments

Value

returns nothing, simply prints the heading to the console

Author(s)

Nicholas Cooper [email protected]

Examples

Header("Section 1")
Header("Section 1",h="-",v="|",corner="*")
Header(c("SPACE","The final frontier"))
Header(c("MY SCRIPT","Part 1"),align="left",h=".")

Description

An alternative to head(list) which allows limiting of large list components in the console display

Usage

headl(x, n = 6, skip = 20, skip2 = 10, ind = "", ind2 = "  ")

Arguments

Value

prints truncated preview of a large list

Author(s)

Nicholas Cooper [email protected]

Examples

sub1 <- list(list(1:100),list(2:101),list(101:200),list(201:300),list(301:400))
big.list <- list(sub1,sub1,sub1,sub1,sub1,sub1)
headl(sub1)
headl(big.list,skip=2)

Description

This is an improvement on base:is.logical because data may be encoded as a different type (e.g, string, "TRUE", "FALSE") especially if imported from a file. This does not include logical vectors coded as 0,1; such will return FALSE with this function.

Usage

is.vec.logical(x, thresh = 0.9)

Arguments

Value

returns a logical TRUE or FALSE for the logical status of x.

Author(s)

Nicholas Cooper

Examples

numeric <- 1:10
string <- paste("one", "two", "three", "four")
logic1 <- c(TRUE,FALSE,FALSE,TRUE,FALSE,NA)
logic2 <- c("TRUE", "FALSE", "TRUE", NA, "TRUE", NA, NA, NA)
logic3 <- c("True", "False", "True", "False")
numlogic <- c(0,1,0,0,0,1,1,1,0)
is.vec.logical(numeric)
is.vec.logical(string)
is.vec.logical(logic1)
is.vec.logical(logic2)
is.vec.logical(logic3)
is.vec.logical(numlogic)

Description

This is an improvement on base:is.numeric because data may be encoded as a different type (e.g, string) especially if imported from a file.

Usage

is.vec.numeric(x, logical.is.numeric = FALSE, thresh = 0.9)

Arguments

Value

returns a logical TRUE or FALSE for the numeric status of x.

Author(s)

Nicholas Cooper

Examples

numeric1 <- 1:10
numeric2 <- paste(1:10)
string <- paste("one", "two", "three", "four")
logic1 <- c(TRUE,FALSE,FALSE,TRUE,FALSE,NA)
numericish <- paste(c(NA, NA, 6:10, "5|6", "7|8", 1))
is.vec.numeric(numeric1)
is.vec.numeric(numeric2)
is.vec.numeric(string)
is.vec.numeric(logic1)
is.vec.numeric(logic1, logical.is.numeric=TRUE)
is.vec.numeric(numericish)
is.vec.numeric(numericish, thresh=0.7)

Description

Parses all functions called by an R script and then lists them by package. Wrapper for 'getParseData'. Inspired by 'hrbrmstr', on StackExchange 3/1/2015. May be of great use for those developing a package to help see what namespace 'importsFrom' calls will be required.

Usage

list.functions.in.file(filename, alphabetic = TRUE)

Arguments

Value

Returns a list. Parses all functions called by an R script and then lists them by package. Those from the script itself are listed under '.GlobalEnv' and any functions that may originate from multiple packages have all possibilities listed. Those listed under 'character(0)' are those for which a package could not be found- may be functions within functions, or from packages that aren't loaded.

Author(s)

Nicholas Cooper [email protected]

See Also

Rfile.index

Examples

# not run:  rfile <- file.choose() # choose an R script file with functions
# not run:  list.functions.in.file(rfile)

Description

Places named objects in a list into the working environment as individual variables. Can be particularly helpful when you want to call a function that produces a list of multiple return variables; this gives a way to access them all at once in the environment from which the function was called.

Usage

list.to.env(list)

Arguments

Value

New variables will be added to the current environment. Use with care as any already existing with the same name will be overwritten.

See Also

base::list2env

Examples

list.to.env(list(myChar="a string", myNum=1234, myList=list("list within a list",c(1,2,3))))
print(myChar)
print(myNum)
print(myList)
two.arg.return <- function(X) { return(list(Y=X+1,Z=X*10)) } 
result <- two.arg.return(11) # function returns list with 2 variables
list.to.env(result)
print(Y); print(Z)

Description

Drawing a fit line usually requires some manual steps requiring several lines of code, such as ensuring the data is sorted by x, and for some functions doesn't contain missing values. This function takes care of these steps and automatically adds a loess fitline, or non-linear fitline. The type of scatter defaults to 'plot', but other scatter plot functions can be specified, such as graphics::smoothScatter(), for example. If 'file' is specifed, will automatically plot to a pdf of that name.

Usage

loess.scatter(
  x,
  y,
  file = NULL,
  loess = TRUE,
  span = 0.75,
  scatter = plot,
  ...,
  ylim = NULL,
  return.vectors = FALSE,
  fit.col = "red",
  fit.lwd = 2,
  fit.lty = "solid",
  fit.leg = TRUE,
  fit.r2 = TRUE,
  fast.loess = FALSE
)

Arguments

Value

if file is a character argument, plots data x,y to a file, else will generate a plot to the current plotting environment/GUI. The display of the x,y points defaults to 'plot', but alternate scatter plot functions can be specified, such as graphics::smoothScatter() which used density smoothing, for example. Also, another option is to set return.vectors=TRUE, and then the coordinates of the fit line will be returned, and no plot will be produced.

Examples

library(NCmisc)
require(KernSmooth)
DD <- sim.cor(1000,4) # create a simulated, correlated dataset
loess.scatter(DD[,3],DD[,4],loess=FALSE,bty="n",pch=".",cex=2)
loess.scatter(DD[,3],DD[,4],scatter=smoothScatter)
xy <- loess.scatter(DD[,3],DD[,4],return.vectors=TRUE)
prv(xy) # preview the vectors produced

Description

Only requires a single line within a loop to run, in contrast with the built-in tracker which requires a line to initialise, and a line to close. Also has option to backup objects during long loops. Ideal for a loop with a counter such as a for loop. Tracks progress as either percentage of time remaining or by intermittently displaying the estimated number of minutes to go

Usage

loop.tracker(
  cc,
  max,
  st.time = NULL,
  sav.obj = NULL,
  sav.fn = NA,
  sav.freq = 10,
  unit = c("m", "s", "h")[1]
)

Arguments

Value

returns nothing, simply prints progress to the console

Author(s)

Nicholas Cooper [email protected]

Examples

# simple example with a for-loop
max <- 100; for (cc in 1:max) { loop.tracker(cc,max); wait(0.004,"s") }
#example using the 'time to go' with a while loop
cc <- 0; max <- 10; start <- proc.time()
while(cc < max) { cc <- cc + 1; wait(0.05,"s"); loop.tracker(cc,max,start,unit="s") }
# example with saving an object, and restoring after a crash
X <- matrix(rnorm(5000),nrow=50); max <- nrow(X); sums <- numeric(max)
for (cc in 1:max) { 
  sums[cc] <- sum(X[cc,])
  wait(.05) # just so this trivial loop doesn't finish so quickly
  loop.tracker(cc,max, sav.obj=sums, sav.fn="temp.rda", sav.freq=5);
  if(cc==29) { warning("faked a crash at iteration 29!"); rm(sums); break }
}
cat("\nloaded latest backup from iteration 28:",paste(load("temp.rda")),"\n")
print(sav.obj); unlink("temp.rda")

Description

Summary of RAM footprint for all R objects in the current session. Not my function, but taken from an R-Help response by Elizabeth Purdom, at Berkeley. Simply applies the function 'object.size' to the objects in ls(). Also very similar to an example in the 'Help' for the utils::object.size() function.

Usage

memory.summary(unit = c("kb", "mb", "gb", "b"))

Arguments

Value

a list of object names with memory usage in bytes

Examples

memory.summary() # shows memory used by all objects in the current session in kb
memory.summary("mb") # change units to megabytes

Description

The mode is the most common value in a series. This function can return multiple values if there are equally most frequent values, and can also work with non-numeric types.

Usage

Mode(x, multi = FALSE, warn = FALSE)

Arguments

Value

The most frequent value, or sorted set of most frequent values if multi==TRUE and there are more than one. Numeric if x is numeric, else as strings

Author(s)

Nicholas Cooper [email protected]

Examples

Mode(c(1,2,3,3,4,4)) # 2 values are most common, as multi=FALSE, 
# selects the last value (after sort)
Mode(c(1,2,3,3,4,4),multi=TRUE) # same test with multi=T, 
# returns both most frequent
Mode(matrix(1:16,ncol=4),warn=TRUE) # takes mode of the entire
# matrix treating as a vector, but all values occur once
Mode(c("Tom","Dick","Harry"),multi=FALSE,warn=TRUE) # selects last
# sorted value, but warns there are multiple modes
Mode(c("Tom","Dick","Harry"),multi=TRUE,warn=TRUE) # multi==TRUE so
# warning is negated

Description

Like 'require()' except it will attempt to install a package if necessary. Installation of bioconductor packages is deprecated. Useful if you wish to share code with people who may not have the same libraries as you, you can include a call to this function which will simply load the library if present, or else install, then load, if they do not have it.

Usage

must.use.package(
  pcknms,
  ask = FALSE,
  reload = FALSE,
  avail = FALSE,
  quietly = FALSE
)

Arguments

Value

nothing, simply loads the packages specified if possible

Author(s)

Nicholas Cooper [email protected]

Examples

# not run : run if you are ok to install/already have these packages
# must.use.package(c("MASS","nlme","lme4"),ask=FALSE)
# search() # show packages have loaded, then detach them again:
# sapply(paste("package",c("MASS","nlme","lme4"),sep=":"),detach,character.only=TRUE)

Description

Convenience function, removes NAs from most standard objects. Uses function na.exclude for matrices and dataframes. Main difference to na.exlude is that it simply performs the transformation, without adding attributes For unknown types, leaves unchanged with a warning.

Usage

narm(X)

Arguments

Value

Vector minus NA's, or the matrix/data.frame minus NA rows. If it's a character vector then values of "NA" will also be excluded in addition to values = NA, so be careful if "NA" is a valid value of your character vector. Note that "NA" values occur when 'paste(...,NA,...)' is applied to a vector of any type, whereas 'as.character(...,NA,...)' avoids this.

Author(s)

Nicholas Cooper [email protected]

Examples

narm(c(1,2,4,NA,5))
DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA))
DF; narm(DF)
# if a list, will only completely remove NA from the lowest levels
# empty places will be left at top levels
print(narm(list(1,2,3,NA,list(1,2,3,NA))))

Description

Similar to the base function match() but allows for data where you won't find an exact match. Selects the nearest value from 'array' to the value 'point'. Sometimes there are multiple points with equal distance in which case choose from 3 possible 'dispute.method's for choosing which of the equidistant array values to index. returns the index of 'array' to which 'point' is nearest.

Usage

nearest.to(array, point, dispute.method = c("first", "last", "random"))

Arguments

Value

index value of the nearest point in 'array'.

Author(s)

Nicholas Cooper [email protected]

Examples

myArray <- 1:100
nearest.to(myArray, 7.7)
nearest.to(myArray, 50.5)
nearest.to(myArray, 50.5, dispute.method="last")

Description

Importing data from csv files can often lead to numeric variables being coded as factors or strings. This will not work well with many R functions. This function provides a quick way to deal with this across a whole data frame while attempting to leave columns untouched that are not genuinely numeric data. In edge cases you might need to adjust 'threshold' to get the correct result, usually an issue if mostly numeric columns often have strings amongst them, for instance a column with mostly numbers, but occassionally pipe-separated values like '4.4|5.0|6.1', etc.

Usage

Numerify(df, except = NULL, force = FALSE, digits = NA, thresh = 0.9)

Arguments

Value

data.frame with numeric type for any applicable columns

Author(s)

Nicholas Cooper

Examples

df <- data.frame(first=c(1:5),
 second=paste(6:10),
 third=c("jake", "fred", "cathy", "sandra", "mike"))
sapply(sapply(df, is), "[", 1) # check type of each column
dfN <- Numerify(df)
sapply(sapply(dfN, is), "[", 1) # now second column is numeric
df2 <- data.frame(first=c(1:10),
 second=paste(c(NA, NA, 6:10, "5|6", "7|8", 1)),
 third=rep(c("jake", "fred", "cathy", "sandra", "mike"),2))
sapply(sapply(df2, is), "[", 1)
df2N1 <- Numerify(df2, thresh=0.7)
df2N2 <- Numerify(df2, thresh=0.8)
sapply(sapply(df2N1, is), "[", 1) # at this threshold second column goes to numeric
sapply(sapply(df2N2, is), "[", 1) # second column stays a string at this threshold

Description

For a subset 'n' and total 'N', nicely prints text n/N and/or percentage Often we want to display proportions and this simple function reduces the required amount of code for fraction and percentage reporting. If insufficient digits are provided small percentage may truncate to zero.

Usage

out.of(n, N = 100, digits = 2, pc = TRUE, oo = TRUE, use.sci = FALSE)

Arguments

Value

A string showing the fraction n/N and percentage (or just one of these)

Examples

out.of(345,12144)
out.of(345,12144,pc=FALSE)
out.of(3,10^6,digits=6,oo=FALSE)
out.of(3,10^6,digits=6,oo=FALSE,use.sci=TRUE)

Description

Simple conversion of two-tailed p-values to Z-scores. Written in a way that allows maximum precision for small p-values.

Usage

p.to.Z(p)

Arguments

Value

Z scores with the same dimension as the input

Author(s)

Nicholas Cooper [email protected]

See Also

Z.to.p

Examples

p.to.Z(0.0001)
p.to.Z("5E-8")
p.to.Z(c(".05",".01",".005"))
p.to.Z(matrix(runif(16),nrow=4))

Description

Returns TRUE if the whole set of packages entered has been loaded, or FALSE otherwise. This can be useful when developing a package where there is optional functionality depending if another package is in use (but the other package is not part of 'depends' because it is not essential). Because 'require' cannot be used within functions submitted as part of a CRAN package.

Usage

packages.loaded(pcks = "", ..., cran.check = FALSE, repos = getRepositories())

Arguments

Value

logical TRUE or FALSE whether the whole list of packages are available

Author(s)

Nicholas Cooper

Examples

packages.loaded("NCmisc","reader")
packages.loaded() # no argument means all loaded packages are listed

Description

Print a vector with appropriate padding so each has equal char length.

Usage

pad.left(X, char = " ", numdigits = NA)

Arguments

Value

returns the vector in character format with equal nchar()

Author(s)

Nicholas Cooper [email protected]

Examples

pad.left(1:10)
phone.numbers <- c("07429719234","7876345123","7123543765")
pad.left(phone.numbers,"0")
pad.left(rnorm(10),numdigits=3)

Description

Finds the top and bottom bounds corresponding to percentile 'pc' of the data 'dat'.

Usage

pctile(dat, pc = 0.01)

Arguments

Value

returns the upper and lower threshold

Author(s)

Nicholas Cooper [email protected]

Examples

pctile(rnorm(100000),.025)
pctile(sample(100),.9)

Description

Estimate the probability of your hypothesis being true, given the observed p-value and a prior probability of the hypothesis being true.

Usage

ppa(p = 0.05, prior = 0.5, BF = NULL, quiet = TRUE)

Arguments

Value

prints calculations, then returns the posterior probability of association given the observed p-value under the specified prior

References

Equations 1, 2 from http://www.readcube.com/articles/10.1038/nrg2615 Equations 2, 3 from http://www.tandfonline.com/doi/pdf/10.1198/000313001300339950

Examples

ps <- rep(c(.05,.01),3)
prs <- rep(c(.05,.50,.90),each=2)
mapply(ps,prs,FUN=ppa)  # replicate Nuzzo 2014 table
# try with bayes factors
ppa(BF=3,prior=.9)
ppa(BF=10,prior=.5)

Description

A versatile function to compactly display most common R objects. Will return the object name, type, dimension, and a compact representation of object contents, for instance using prv.large() to display matrices, so as to not overload the console for large objects. Useful for debugging, can be placed inside loops and functions to track values, dimensions, and data types. Particularly when debugging complex code, the automatic display of the variable name prevents confusion versus using regular print statements. By listing variables to track as character(), provides 'cat()' output of compact and informative variable state information, e.g, variable name, value, datatype and dimension. Can also specify array or list elements, or custom labels. prv() is the same as preview() except it can take objects without using double quotes and has no 'labels' command (and doesn't need one).

Usage

preview(
  varlist,
  labels = NULL,
  counts = NULL,
  assume.char = FALSE,
  prv.call = FALSE
)

Arguments

See Also

Dim

Examples

# create variables of different types to show output styles #
testvar1 <- 193
testvar2 <- "Atol"
testvar3 <- c(1:10)
testvar4 <- matrix(rnorm(100),nrow=25)
testvar5 <- list(first="test",second=testvar4,third=100:110)
preview("testvar1")
preview("testvar4")
preview(paste("testvar",1:5,sep=""))
preview(testvar1,"myvarname")
preview(testvar1)
# examples with loops and multiple dimensions / lists
for (cc in 1:4) {
 for (dd in 1:4) { preview("testvar4",counts=list(cc,dd)) }}

for (dd in 1:3) { preview("testvar5",counts=list(dd=dd)) }

Description

Same as preview but no labels command, and input is without quotes and should be plain variable names of existing variables (no indices, args, etc) A versatile function to compactly display most common R objects. Will return the object name, type, dimension, and a compact representation of object contents, for instance using prv.large() to display matrices, so as to not overload the console for large objects. Useful for debugging, can be placed inside loops and functions to track values, dimensions, and data types. Particularly when debugging complex code, the automatic display of the variable name prevents confusion versus using regular print statements. By listing variables to track as character(), provides 'cat()' output of compact and informative variable state information, e.g, variable name, value, datatype and dimension. Can also specify array or list elements, or custom labels. prv() is the same as preview() except it can take objects without using double quotes and has no 'labels' command (and doesn't need one). If expressions are entered rather than variable names, then prv() will attempt to pass the arguments to preview(). prv() assumes that the variable(s) to report originate from the environment calling prv(), and if not found there, then it will search through all accessible environments starting with the global environment, and then will report the first instance found, which in exceptional circumstances (be warned) may not be the instance you intended to retrieve.

Usage

prv(..., counts = NULL)

Arguments

See Also

Dim

Examples

# create variables of different types to show output styles #
testvar1 <- 193
testvar2 <- "Atol"
testvar3 <- c(1:10)
testvar4 <- matrix(rnorm(100),nrow=25)
testvar5 <- list(first="test",second=testvar4,third=100:110)
preview("testvar1"); prv(testvar1)
prv(testvar1,testvar2,testvar3,testvar4)
prv(matrix(rnorm(100),nrow=25)) # expression sent to preview() with no label
prv(193) # fails as there are no object names involved

Description

This function prints the first and last columns and rows of a matrix, and more, if desired. Allows previewing of a matrix without overloading the console. Most useful when data has row and column names.

Usage

prv.large(
  largeMat,
  rows = 3,
  cols = 2,
  digits = 4,
  rL = "Row#",
  rlab = "rownames",
  clab = "colnames",
  rownums = T,
  ret = FALSE,
  warn = TRUE
)

Arguments

Examples

mat <- matrix(rnorm(1000),nrow=50)
rownames(mat) <- paste("ID",1:50,sep="")
colnames(mat) <- paste("Var",1:20,sep="")
prv.large(mat)
prv.large(mat,rows=9,cols=4,digits=1,rlab="samples",clab="variables",rownums=FALSE)

Description

To simple replace missing data without changing column means. This will also use criteria to decide whether each column is numeric, so that illegal operations aren't performed on strings, etc. Also adjusting the 'error' parameter allows adding variance to the missing observations to help to reduce bias associated with inserting many of the same replacement value.

Usage

replace.missing.df(
  X,
  repl.fun = mean,
  error = 0,
  thresh = 0.9,
  digits = 99,
  force = FALSE
)

Arguments

Value

returns a data.frame with the same dimensions with missing values for numeric values imputed using the repl.fun function, optionally with noise added.

Author(s)

Nicholas Cooper

Examples

df <- data.frame(first=c(1,2,NA,4,5),
  second=paste(c(6,7,8,NA,10)),
  third=c("jake", "fred", "cathy", "sandra", "mike"))
df
replace.missing.df(df)
replace.missing.df(df, force=TRUE)
df2 <- data.frame(first=c(1:5, NA, NA, NA,9, 10),
  second=paste(c(NA, NA, 6:10, "5|6", "7|8", 1)),
  third=rep(c("jake", "fred", "cathy", "sandra", "mike"),2))
df2
replace.missing.df(df2)
replace.missing.df(df2, thresh=0.7)
replace.missing.df(df2, error = 1, thresh=0.7, digits=4)

Description

Create a html index for an R function file by looking for functions, add descriptions using comments directly next to the function() command. Note that if too much code other than well-formatted functions is in the file then the result is likely not to be a nicely formatted index.

Usage

Rfile.index(fn, below = TRUE, fn.out = "out.htm", skip.indent = TRUE)

Arguments

Value

creates an html file with name and description of each function

Author(s)

Nicholas Cooper [email protected]

See Also

list.functions.in.file

Examples

# not run:  rfile <- file.choose() # choose an R script file with functions
# not run:  out <- Rfile.index(rfile,fn.out="temp.htm")
# unlink("temp.htm") # run once you've inspected this file in a browser

Description

Convenience function, it's very easy to set names to NULL, but this requires a dedicated line of code. Using this function can make your code simpler.

Usage

rmv.names(X)

Arguments

Value

the original object but without names

Author(s)

Nicholas Cooper [email protected]

Examples

x <- c(boo=1, hiss=2)
rmv.names(x)
X <- list(testing=c(1,2,3), thankyou=TRUE)
rmv.names(X)

Description

Remove leading and trailing spaces (or other character).

Usage

rmv.spc(str, before = TRUE, after = TRUE, char = " ")

Arguments

Value

returns vectors without the leading/trailing characters

Author(s)

Nicholas Cooper [email protected]

See Also

spc

Examples

rmv.spc("  mid sentence  ")
rmv.spc("0012300",after=FALSE,char="0")
rmv.spc(" change nothing ",after=FALSE,before=FALSE)

Description

Can be useful for trying to find new packages for a particular purpose. No need for these packages to be installed or loaded. Further searching can be done using utils::RSiteSearch()

Usage

search.cran(txt, repos = "", all.repos = FALSE)

Arguments

Value

list of hits for each keyword (txt)

Author(s)

Nicholas Cooper [email protected]

Examples

## not run # repos <- "http://cran.ma.imperial.ac.uk/" # OR: repos <- getOption("repos")
## not run # search.cran("draw")
## not run # search.cran(c("hmm", "markov", "hidden"))

Description

Simulate a dataset with correlated measures (normal simulation with e.g, rnorm() usually only gives small randomly distributed correlations between variables). This is a quick and unsophisticated method, but should be able to provide a dataset with slightly more realistic structure than simple rnorm() type functions. Varying the last three parameters gives some control on the way the data is generated. It starts with a seed random variable, then creates 'k' random variables with an expected correlation of r=genr() with that seed variable. Then after this, one of the variables in the set (including the seed) is randomly selected to run through the same process of generating 'k' new variables; this is repeated until columns are full up. 'mix.order' then randomizes the column order destroying the relationship between column number and correlation structure, although in some cases, such relationships might be desired as representative of some real life datasets.

Usage

sim.cor(
  nrow = 100,
  ncol = 100,
  genx = rnorm,
  genr = runif,
  k = 3,
  mix.order = TRUE
)

Arguments

Author(s)

Nicholas Cooper

See Also

cor.with

Examples

corDat <- sim.cor(200,5)
prv(corDat) # preview of simulated normal data with r uniformly varying
cor(corDat) # correlation matrix
corDat <- sim.cor(500,4,genx=runif,genr=function(x) { 0.5 },mix.order=FALSE)
prv(corDat) # preview of simulated uniform data with r fixed at 0.5
cor(corDat) # correlation matrix

Description

Retrieve a simple representation of date_time or just date, for generating day/time specific file names, etc.

Usage

simple.date(sep = "_", long = FALSE, time = TRUE)

Arguments

Value

A string containing the date: MMMDD and optionally time HRam/pm. Or if long=TRUE, a longer representation: DAY MM DD HH.MM.SS YYYY.

Examples

simple.date()
simple.date(" ",long=TRUE)
simple.date(time=FALSE)

Description

Returns 'char' X_i number of times for each element i of X. Useful for padding for alignment purposes.

Usage

spc(X, char = " ")

Arguments

Value

returns vectors of strings of char, lengths X

Author(s)

Nicholas Cooper [email protected]

See Also

rmv.spc

Examples

cat(paste(spc(9),"123\n"))
cat(paste(spc(8),"1234\n"))
spc(c(1:5),".")

Description

Transform a vector to z scores by subtracting its mean and dividing by its standard deviation

Usage

standardize(X)

Arguments

Value

vector of the same length in standardised form

Author(s)

Nicholas Cooper [email protected]

Examples

x1 <- rnorm(10,100,15); x2 <- sample(10)
print(x1) ;  standardize(x1)
print(x2) ;  standardize(x2)

Description

Equivalent to the base function substitute() but can do any length of arguments instead of just one. Converts the objects in parentheses into text arguments as if they had been entered with double quote strings. The objects must exist and be accessible in the environment the function is called from for the function to work (same as for substitute()). One application for this is to be able to create functions where object arguments can be entered without quotation marks (simpler), or where you want to use the name of the object as well as the data in the object.

Usage

Substitute(x = NULL, ...)

Arguments

Value

character list of x,... object names

Author(s)

Nicholas Cooper

See Also

prv, preview

Examples

myvar <- list(test=c(1,2,3)); var2 <- "testme"; var3 <- 10:14
print(myvar)
# single variable case, equivalent to base::substitute()
print(substitute(myvar))
print(Substitute(myvar))
# multi variable case, substitute won't work
Substitute(myvar,var2,var3)
# prv() is a wrapper for preview() allowing arguments without parentheses
# which is achieved internally by passing the arguments to Substitute()
preview(c("myvar","var2","var3"))
prv(myvar,var2,var3)

Description

This function will parse the current workspace to see what R datasets are available. Using the toHTML function from the 'tools' package to interpret the data() call, each dataset is examined in turn for type and dimensionality. Can also use a filter for dataset types, to only show, for instance, matrix datasets. Also you can specify whether to only look for base datasets, or to search for datasets in all available packages. Result is a printout to the console of the available datasets and their characteristics.

Usage

summarise.r.datasets(
  filter = FALSE,
  types = c("data.frame", "matrix"),
  all = FALSE,
  ...
)

Arguments

Author(s)

Nicholas Cooper

Examples

summarise.r.datasets()
summarise.r.datasets(filter=TRUE,"matrix")

Description

Wrapper for the base function summary() but adds standard deviation, standard error, and an 'N' and missing 'NA' count that are consistent.

Usage

summary2(x, digits = NULL, neaten.names = TRUE)

Arguments

Value

array of descriptive statistics for x

Author(s)

Nicholas Cooper [email protected]

Examples

x <- 1:100
summary2(x, digits=2)
summary2(c(x, NA, NA), digits=2)

Description

Wrapper for the base table() function that includes zero counts - useful to get consistent dimensions across multiple runs with different responding patterns Forces a 2d table with every possible cell (allow zero counts) Only for tables where there are two vectors entered, while the base function allows for more, or also allows just 1. If the wrong arguments are entered, attempts to pass the input to the base version of 'table' instead.

Usage

table2d(
  ...,
  col,
  row,
  rn = NULL,
  cn = NULL,
  use.order = TRUE,
  remove.na = FALSE
)

Arguments

Value

returns a table, just like the base:table() function but the row and column names are fixed regardless of count

Author(s)

Nicholas Cooper

Examples

nm <- c("Mike", "Anna", "John", "Tony")
vec_r <- sample(tolower(nm)[c(1,3,4)], 50, replace=TRUE)
vec_c <- sample(c(1,2,4,5), 50, replace=TRUE)
table(vec_r, vec_c)
table2d(vec_r, vec_c, row=tolower(nm), col=paste(1:5))
table2d(vec_r, vec_c, row=tolower(nm), col=paste(1:5), use.order = FALSE)
table2d(vec_r, vec_c, row=tolower(nm), col=paste(1:5), rn=nm, cn=c("I", "II", "III", "IV", "V"))

Description

Uses a call to base::hist(...) and uses the densities to make a a text histogram in the console Particularly useful when working in the terminal without graphics.

Usage

textogram(X, range = NA, ...)

Arguments

Value

outputs an ascii histogram to the console

Author(s)

Nicholas Cooper [email protected]

Examples

textogram(runif(100000))
textogram(rnorm(10000),range=c(-3,3))

Description

A wrapper for the proftools package Rprof() function. It is to Rprof() as system.time() is to proc.time() (base) Useful for identifying which functions are taking the most time. This procedure will return an error unless expr takes more than ~0.1 seconds to evaluate. I could not see any simple way to avoid this limitation. Occassionally other errors are produced for no apparent reason which are due to issues within the proftools package that are out of my control.

Usage

timeit(expr, suppressResult = F, total.time = TRUE)

Arguments

Value

returns matrix where rows are function names, and columns are self.time and total.time. total.time is total time spent in that function, including function calls made by that function. self.time doesn't count other functions within a function

Author(s)

Nicholas Cooper [email protected]

Examples

# this function writes and removes a temporary file
# run only if ok to do this in your temporary directory
#not run# timeit(wait(0.1,"s") ,total.time=TRUE)
#not run# timeit(wait(0.1,"s") ,total.time=FALSE)

Description

Return a string with each first letter of each word in upper case.

Usage

toheader(txt, strict = FALSE)

Arguments

Value

Vector minus NA's, or the matrix/data.frame minus NA rows

Author(s)

via R Core

Examples

toheader(c("using AIC for model selection"))
toheader(c("using AIC", "for MODEL selection"), strict=TRUE)

Description

This function runs the unix 'top' command and returns the overall CPU and RAM usage, and optionally the table of processes and resource use for each. Works only with unix-based systems such as Mac OS X and Linux, where 'top' is installed. Default is to return CPU and RAM overall stats, to get detailed stats instead, set Table=TRUE.

Usage

top(
  CPU = !Table,
  RAM = !Table,
  Table = FALSE,
  procs = 20,
  mem.key = NULL,
  cpu.key = NULL
)

Arguments

Value

a list containing CPU and RAM usage, or with alternate parameters can return stats for each process

Author(s)

Nicholas Cooper

Examples

# not run #  top()
# not run #  top(Table=TRUE,proc=5)

Description

Allows unlisting preserving the top levels of a list. Can specify the number of list depth levels to skip before running unlist()

Usage

Unlist(obj, depth = 1)

Arguments

Value

returns vectors of strings of char, lengths X

Author(s)

Nicholas Cooper [email protected]

Examples

complex.list <- list(1,1:3,list(2,2:4,list(3,3:4,list(10))),list(4,5:7,list(3)))
Unlist(complex.list,0) # equivalent to unlist()
Unlist(complex.list,1) # unlist within the top level lists
Unlist(complex.list,2) # unlist within the second level lists
Unlist(complex.list,10) # once depth >= list-depth, no difference
unlist(complex.list,recursive=FALSE) # not the same as any of the above

Description

Waits a number of hours minutes or seconds (doing nothing). Note that this 'waiting' will use 100

Usage

wait(dur, unit = "s", silent = TRUE)

Arguments

Value

no return value

Author(s)

Nicholas Cooper [email protected]

Examples

wait(.25,silent=FALSE) # wait 0.25 seconds
wait(0.005, "m")
wait(0.0001, "Hours", silent=FALSE)

Description

Performs simplistic outlier detection and returns indexes for outliers. Acts like the which() function, return indices of elements of a vector satisfying the condition, which by default are outliers exceeding 2 SD above or below the mean. However, the threshold can be specified, only high or low values can be considered outliers, and percentile and interquartile range thresholds can also be used.

Usage

which.outlier(
  x,
  thr = 2,
  method = c("sd", "iq", "pc"),
  high = TRUE,
  low = TRUE
)

Arguments

Value

indexes of the vector x that are outliers according to either a SD cutoff, interquartile range, or percentile threshold, above (high) and/or below (low) the mean/median.

Examples

test.vec <- rnorm(200)
summary(test.vec)
ii <- which.outlier(test.vec) # 2 SD outliers
prv(ii); vals <- test.vec[ii]; prv(vals)
ii <- which.outlier(test.vec,1.5,"iq") # e.g, 'stars' on a box-plot
prv(ii)
ii <- which.outlier(test.vec,5,"pc",low=FALSE) # only outliers >mean
prv(ii)

Description

Simple conversion of Z-scores to two-tailed p-values. Written in a way that allows maximum precision for small p-values.

Usage

Z.to.p(Z, warn = FALSE)

Arguments

Value

p-valuues with the same dimension as the input

Author(s)

Nicholas Cooper [email protected]

See Also

p.to.Z

Examples

Z.to.p("1.96")
Z.to.p(p.to.Z(0.0001))
Z.to.p(37, TRUE)
Z.to.p(39, TRUE) # maximum precision exceeded, warnings on
Z.to.p(39) # maximum precision exceeded, warnings off