Package 'mosaic'

Description

mosaic

Details

Data sets and utilities from Project MOSAIC (mosaic-web.org) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.

Author(s)

Randall Pruim ([email protected]), Daniel Kaplan ([email protected]), Nicholas Horton ([email protected])

References

http://www.mosaic-web.org

See Also

Useful links:

• https://github.com/ProjectMOSAIC/mosaic

• https://www.mosaic-web.org/mosaic/

• Report bugs at https://github.com/ProjectMOSAIC/mosaic/issues

Description

adapt_seq is similar to seq except that instead of selecting points equally spaced along an interval, it selects points such that the values of a function applied at those points are (very) roughly equally spaced. This can be useful for sampling a function in such a way that it can be plotted more smoothly, for example.

Usage

adapt_seq(
  from,
  to,
  length.out = 200,
  f = function(x, ...) {
     1
 },
  args = list(),
  quiet = FALSE
)

Arguments

Value

a numerical vector

Examples

adapt_seq(0, pi, 25, sin)

Description

aggregatinFuntion1 creates statistical summaries of one numerical vector that are formula aware.

Usage

aggregatingFunction1(
  fun,
  output.multiple = FALSE,
  envir = parent.frame(),
  na.rm = getOption("na.rm", FALSE),
  style = c("formula1st", "formula", "flexible")
)

Arguments

Details

The logic of the resulting function is this: 1) If the first argument is a formula, use that formula and data to create the necessary call(s) to fun; (2) Else simply pass everything to fun for evaluation.

Value

a function that generalizes fun to handle a formula/data frame interface.

Note

Earlier versions of this function supported a "bare name + data frame" interface. This functionality has been removed since it was (a) ambiguous in some cases, (b) unnecessary, and (c) difficult to maintain.

Examples

if (require(mosaicData)) {
  foo <- aggregatingFunction1(base::mean)
  foo( ~ length, data = KidsFeet)
  base::mean(KidsFeet$length)
  foo(length ~ sex, data = KidsFeet)
}

Description

aggregatingFunction1or2() creates statistical summaries for functions like var() that can have either 1 or 2 numeric vector inputs.

Usage

aggregatingFunction1or2(
  fun,
  output.multiple = FALSE,
  na.rm = getOption("na.rm", FALSE)
)

Arguments

Details

This was designed primarily to support var which can be used to compute either the variance of one variable or the covariance of two variables. The logic of the resulting function is this: 1) If the first two arguments are both formulas, then those formulas are evaluated (with data) to compute the covariance; (2) If the first argument is a formula, and the second is NULL, then the formula and data are used to create the necessary call(s) to fun; (3) Else everything is simply passed to fun for evaluation.

Note

Earlier versions of this function supported a "bare name + data frame" interface. This functionality has been removed since it was (a) ambiguous in some cases, (b) unnecessary, and (c) difficult to maintain.

Description

aggregatinFuntion2 creates statistical summaries of two numerical vectors that are formula aware.

Usage

aggregatingFunction2(fun)

Arguments

Details

This was designed to support functions like cov() which can be used to compute numerical summaries from two numeric vectors. The logic of the resulting function is this: 1) If the first two arguments are both formulas, then those formulas are evaluated (with data) to compute the covariance; (2) If the first argument is a formula, and the second is NULL, then the left and ride sides of the formula and data are used to create the vectors passed to fun; (3) Else everything is simply passed to fun for evaluation.

Value

a function that generalizes fun to handle a formula/data frame interface.

Note

Earlier versions of this function supported a "bare name + data frame" interface. This functionality has been removed since it was (a) ambiguous in some cases, (b) unnecessary, and (c) difficult to maintain.

Examples

if(require(mosaicData)) {
  foo <- aggregatingFunction2(stats::cor)
  foo(length ~ width, data = KidsFeet)
  stats::cor(KidsFeet$length, KidsFeet$width)
}

Description

Convert a data frame or a matrix into an xtabs object.

Usage

as.xtabs(x, ...)

## S3 method for class 'data.frame'
as.xtabs(x, rowvar = NULL, colvar = NULL, labels = 1, ...)

## S3 method for class 'matrix'
as.xtabs(x, rowvar = NULL, colvar = NULL, ...)

Arguments

Details

The intended use is to convert a two-way contingency table stored in a data frame or a matrix into an xtabs object.

Value

An xtabs object.

Examples

# example from example(fisher.test)
df <- data.frame( X=c('Tea','Milk'), Tea=c(3,1), Milk=c(1,3) )
xt <- as.xtabs(df, rowvar="Guess", colvar="Truth"); xt
if (require(vcd)) { mosaic(xt) }

Description

An ASH plot is the average over all histograms of a fixed bin width.

Usage

ashplot(
  x,
  data = data,
  ...,
  width = NULL,
  adjust = NULL,
  panel = panel.ashplot,
  prepanel = prepanel.default.ashplot
)

prepanel.default.ashplot(x, darg, groups = NULL, subscripts = TRUE, ...)

panel.ashplot(
  x,
  darg = list(),
  plot.points = FALSE,
  ref = FALSE,
  groups = NULL,
  jitter.amount = 0.01 * diff(current.panel.limits()$ylim),
  type = "p",
  ...,
  identifier = "ash"
)

Arguments

Examples

ashplot( ~age | substance, groups = sex, data = HELPrct)

Description

lattice::barchart() from the lattice package makes bar graphs from pre-tabulated data. Raw data can be tabulated using xtabs(), but the syntax is unusual compared to the other lattice plotting functions. bargraph provides an interface that is consistent with the other lattice functions.

Usage

bargraph(
  x,
  data = parent.frame(),
  groups = NULL,
  horizontal = FALSE,
  origin = 0,
  ylab = ifelse(horizontal, "", type),
  xlab = ifelse(horizontal, type, ""),
  type = c("count", "frequency", "proportion", "percent"),
  auto.key = TRUE,
  scales = list(),
  ...
)

Arguments

Details

bargraph(formula, data=data, ...) works by creating a new data frame from xtabs(formula, data=data) and then calling lattice::barchart() using modified version of the formula and this new data frame as inputs. This has implications on, for example, conditional plots where one desires to condition on some expression that will be evaluated in data. This typically does not work because the required variables do not exist in the output of xtabs. One solution is to first add a new variable to data first and then to condition using this new variable. See the examples.

Value

a trellis object describing the plot

See Also

lattice::barchart()

Examples

if (require(mosaicData)) {
data(HELPrct)
bargraph( ~ substance, data = HELPrct)
bargraph( ~ substance, data = HELPrct, horizontal = TRUE)
bargraph( ~ substance | sex, groups = homeless, auto.key = TRUE, data = HELPrct)
bargraph( ~ substance, groups = homeless, auto.key=TRUE, 
            data = HELPrct |> filter(sex == "male"))
HELPrct2 <- mutate(HELPrct, older = age > 40)
bargraph( ~ substance | older, data = HELPrct2)
}

Description

The binom.test() function performs an exact test of a simple null hypothesis about the probability of success in a Bernoulli experiment from summarized data or from raw data. The mosaic binom.test provides wrapper functions around the function of the same name in stats. These wrappers provide an extended interface (including formulas).

Usage

binom.test(
  x,
  n = NULL,
  p = 0.5,
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95,
  ci.method = c("Clopper-Pearson", "binom.test", "Score", "Wilson", "prop.test", "Wald",
    "Agresti-Coull", "Plus4"),
  data = NULL,
  success = NULL,
  ...
)

Arguments

Details

binom.test() is a wrapper around stats::binom.test() from the stats package to simplify its use when the raw data are available, in which case an extended syntax for binom.test() is provided. See the examples.

Also, five confidence interval methods are provided: * ⁠"Clopper-Pearson", "binom.test"⁠: This is the interval produced when using stats::binom.test() from the stats package. It guarantees a coverage rate at least as large as the nominal coverage rate, but may produce wider intervals than some of the methods below, which may either under- or over-cover depending on the data.

• '"Score", "Wilson", "prop.test": This is the usual method used by stats::prop.test() and is computed by inverting p-values from score tests. It is often attributed to Edwin Wilson. If specified with "prop.test", the continuity correction is applied (as is the default in prop.test()), else the continuity correction is not applied.

  • "Wald" This is the interval traditionally taught in entry level statistics courses. It uses the sample proportion to estimate the standard error and uses normal theory to determine how many standard deviations to add and/or subtract from the sample proportion to determine an interval.

  • \"Agresti-Coull"' This is the Wald method after setting $n' = n + z^2$ and $p'= (x + z^2/2) / n$' and using $x' = n' p'$ and $n'$ in place of $x$ and $n$.

  • "Plus4" This is Wald after adding in two artificial success and two artificial failures. It is nearly the same as the Agresti-Coull method when the confidence level is 95%. since $z^2$ is approximately 4 and $z^2/2$ is approximately 2.

Value

an object of class htest

Note

When x is a 0-1 vector, 0 is treated as failure and 1 as success. Similarly, for a logical vector TRUE is treated as success and FALSE as failure.

See Also

prop.test(), stats::binom.test()

Examples

# Several ways to get a confidence interval for the proportion of Old Faithful
# eruptions lasting more than 3 minutes.
data(faithful)
binom.test(faithful$eruptions > 3)
binom.test(97, 272)
binom.test(c(97, 272-97))
faithful$long <- faithful$eruptions > 3
binom.test(faithful$long)
binom.test(resample(1:4, 400), p=.25)
binom.test(~ long, data = faithful)
binom.test(~ long, data = faithful, ci.method = "Wald")
binom.test(~ long, data = faithful, ci.method = "Plus4")
with(faithful, binom.test(~long))
with(faithful, binom.test(long))

Description

Implementation of Broyden's root finding function to numerically compute the root of a system of nonlinear equations

Usage

Broyden(system, vars, x = 0, tol = .Machine$double.eps^0.4, maxiters = 10000)

Arguments

Description

This function determines the critical values for isolating a central portion of a distribution with a specified probability. This is designed to work especially well for symmetric distributions, but it can be used with any distribution.

Usage

cdist(
  dist = "norm",
  p,
  plot = TRUE,
  verbose = FALSE,
  invisible = FALSE,
  digits = 3L,
  xlim = NULL,
  ylim = NULL,
  resolution = 500L,
  return = c("values", "plot"),
  pattern = c("rings", "stripes"),
  ...,
  refinements = list()
)

xcgamma(
  p,
  shape,
  rate = 1,
  scale = 1/rate,
  lower.tail = TRUE,
  log.p = FALSE,
  ...
)

xct(p, df, ncp, lower.tail = TRUE, log.p = FALSE, ...)

xcchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)

xcf(p, df1, df2, lower.tail = TRUE, log.p = FALSE, ...)

xcbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE, ...)

xcpois(p, lambda, lower.tail = TRUE, log.p = FALSE, ...)

xcgeom(p, prob, lower.tail = TRUE, log.p = FALSE, ...)

xcnbinom(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE, ...)

xcbeta(p, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)

Arguments

Value

a pair of numbers indicating the upper and lower bounds, unless verbose is TRUE, in which case a 1-row data frame is returned containing these bounds, the central probability, the tail probabilities, and the name of the distribution.

Note

This function is still experimental and changes the input or output formats are possible in future versions of the package.

Examples

cdist( "norm", .95)
cdist( "t", c(.90, .95, .99), df=5)
cdist( "t", c(.90, .95, .99), df=50)
# plotting doesn't work well when the parameters are not constant
cdist( "t", .95, df=c(3,5,10,20), plot = FALSE)
cdist( "norm", .95, mean=500, sd=100 )
cdist( "chisq", c(.90, .95), df=3 )
# CI
x <- rnorm(23, mean = 10, sd = 2)
cdist("t", p = 0.95, df=22)
mean(x) + cdist("t", p = 0.95, df=22) * sd(x) / sqrt(23)
confint(t.test(x))
cdist("t", p = 0.95, df=22, verbose = TRUE)

Description

Extract Chi-squared statistic

Usage

chisq(x, ...)

## S3 method for class 'htest'
chisq(x, ...)

## S3 method for class 'table'
chisq(x, correct = FALSE, ...)

## Default S3 method:
chisq(x, correct = FALSE, ...)

Arguments

See Also

after_stat()

Examples

if(require(mosaicData)) {
  Mites.table <- tally( ~ outcome + treatment, data=Mites )
  Mites.table 
  chisq.test(Mites.table)
  chisq(Mites.table)
  chisq(chisq.test(Mites.table))
  ## Randomization test.  Increase replications to decrease Monte Carlo error.
  do(3) * chisq( tally( ~ outcome + shuffle(treatment),  data=Mites ) )
  Mites.rand <- do(1000) * chisq( tally( ~ outcome + shuffle(treatment),  data=Mites ) )
  tally( ~(X.squared >= chisq(Mites.table)), data=Mites.rand, format="proportion")
}

Description

This function can be used in two different ways. Without an argument, it returns a reference table that includes information about all the CIA World Factbook tables that are available through this function. Note the Name column that indicates a unique name for each available dataset. If this name is passed as an argument to the function, the function will return the corresponding dataset.

Usage

CIAdata(name = NULL)

Arguments

Examples

## Not run: 
head(CIAdata())
Population <- CIAdata("pop")
nrow(Population)
head(Population)

PopArea <- 
  CIAdata(c("pop","area")) |> 
  mutate(density = pop / area)
nrow(PopArea)
head(PopArea)
PopArea |> 
  filter(!is.na(density)) |>
  arrange(density) |> 
  tail()

## End(Not run)

Description

This function automates the calculation of coverage rates for exploring the robustness of confidence interval methods.

Usage

CIsim(
  n,
  samples = 100,
  rdist = rnorm,
  args = list(),
  plot = if (samples <= 200) "draw" else "none",
  estimand = 0,
  conf.level = 0.95,
  method = t.test,
  method.args = list(),
  interval = function(x) {
     do.call(method, c(list(x, conf.level = conf.level),
    method.args))$conf.int
 },
  estimate = function(x) {
     do.call(method, c(list(x, conf.level = conf.level),
    method.args))$estimate
 },
  verbose = TRUE
)

Arguments

Value

A data frame with variables lower, upper, estimate, cover ('Yes' or 'No'), and sample is returned invisibly. See the examples for a way to use this to display the intervals graphically.

Examples

# 1000 95% intervals using t.test; population is N(0,1)
CIsim(n = 10, samples = 1000)    
# this time population is Exp(1); fewer samples, so we get a plot 
CIsim(n = 10, samples = 100, rdist = rexp, estimand = 1) 
# Binomial treats 1 like success, 0 like failure
CIsim(n = 30, samples = 100, rdist = rbinom, args = list(size = 1, prob = .7), 
       estimand = .7, method = binom.test, method.args = list(ci = "Plus4"))

Description

These versions of the quantile functions take a vector of central probabilities as its first argument.

Usage

cnorm(p, mean = 0, sd = 1, log.p = FALSE, side = c("both", "upper", "lower"))

ct(p, df, ncp, log.p = FALSE, side = c("upper", "lower", "both"))

Arguments

See Also

stats::qnorm(), cdist()

Examples

qnorm(.975)
cnorm(.95)
xcnorm(.95)
xcnorm(.95, verbose = FALSE, return = "plot") |>
  gf_refine(
  scale_fill_manual( values = c("navy", "limegreen")),
  scale_color_manual(values = c("black", "black")))
cnorm(.95, mean = 100, sd = 10)
xcnorm(.95, mean = 100, sd = 10)

Description

The following functions were once a part of the mosaic pacakge but have been removed. In some cases, an alternative is available and is suggested if you attempt to execute the function.

Usage

compareMean(...)

compareProportion(...)

deltaMethod(...)

gwm(...)

r.squared(...)

mm(...)

perctable(...)

proptable(...)

xhistogram(...)

Arguments

Description

Methods for confint to compute confidence intervals on numerical vectors and numerical components of data frames.

Usage

## S3 method for class 'numeric'
confint(
  object,
  parm,
  level = 0.95,
  ...,
  method = "percentile",
  margin.of.error = "stderr" %in% method == "stderr"
)

## S3 method for class 'do.tbl_df'
confint(
  object,
  parm,
  level = 0.95,
  ...,
  method = "percentile",
  margin.of.error = "stderr" %in% method,
  df = NULL
)

## S3 method for class 'do.data.frame'
confint(
  object,
  parm,
  level = 0.95,
  ...,
  method = "percentile",
  margin.of.error = "stderr" %in% method,
  df = NULL
)

## S3 method for class 'data.frame'
confint(object, parm, level = 0.95, ...)

## S3 method for class 'summary.lm'
confint(object, parm, level = 0.95, ...)

Arguments

Details

The methods of producing confidence intervals from bootstrap distributions are currently quite naive. In particular, when using the standard error, assistance may be required with the degrees of freedom, and it may not be possible to provide a correct value in all situations. None of the methods include explicit bias correction. Let $q_a$ be the $a$ quantile of the bootstrap distribution, let $t_a, df$ be the $a$ quantile of the t distribution with $df$ degrees of freedom, let $SE_b$ be the standard deviation of the bootstrap distribution, and let $\hat{\theta}$ be the estimate computed from the original data. Then the confidence intervals with confidence level $1 - 2a$ are

quantile

$(q_a, q_{1-a})$

reverse

$( 2 \hat{\theta} - q_{1-a}, 2\hat{\theta} - q_{a} )$

stderr

$(\hat{\theta} - t_{1-a,df} SE_b, \hat{\theta} + t_{1-a,df} SE_b)$. When df is not provided, at attempt is made to determine an appropriate value, but this should be double checked. In particular, missing data an lead to unreliable results.

The bootstrap-t confidence interval is computed much like the reverse confidence interval but the bootstrap t distribution is used in place of a theoretical t distribution. This interval has much better properties than the reverse (or basic) method, which is here for comparison purposes only and is not recommended. The t-statistic is computed from a mean, a standard deviation, a sample size which much be named "mean", "sd", and "n" as they are when using favstats().

Value

When applied to a data frame, returns a data frame giving the confidence interval for each variable in the data frame using t.test or binom.test, unless the data frame was produced using do, in which case it is assumed that each variable contains resampled statistics that serve as an estimated sampling distribution from which a confidence interval can be computed using either a central proportion of this distribution or using the standard error as estimated by the standard deviation of the estimated sampling distribution. For the standard error method, the user must supply the correct degrees of freedom for the t distribution since this information is typically not available in the output of do().

When applied to a numerical vector, returns a vector.

References

Tim C. Hesterberg (2015): What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, The American Statistician, https://www.tandfonline.com/doi/full/10.1080/00031305.2015.1089789.

Examples

if (require(mosaicData)) {
  bootstrap <- do(500) * diffmean( age ~ sex, data = resample(HELPrct) )
  confint(bootstrap)
  confint(bootstrap, method = "percentile")
  confint(bootstrap, method = "boot")
  confint(bootstrap, method = "se", df = nrow(HELPrct) - 1)
  confint(bootstrap, margin.of.error = FALSE)
  confint(bootstrap, margin.of.error = TRUE, level = 0.99, 
    method = c("se", "perc") )
    
  # bootstrap t method requires both mean and sd
  bootstrap2 <- do(500) * favstats(resample(1:10)) 
  confint(bootstrap2, method = "boot")
}
lm(width ~ length * sex, data = KidsFeet) |>
  summary() |>
  confint()

Description

Extract confidence intervals, test statistics or p-values from an htest object.

Usage

## S3 method for class 'htest'
confint(object, parm, level, ...)

pval(x, ...)

## S3 method for class 'htest'
pval(x, digits = 4, verbose = FALSE, ...)

stat(x, ...)

## S3 method for class 'htest'
stat(x, ...)

## S3 method for class 'uneval'
stat(x, ...)

Arguments

Value

the extracted p-value, confidence interval, or test statistic

Examples

confint(t.test(rnorm(100)))
pval(t.test(rnorm(100)))
stat(t.test(rnorm(100)))
confint(var.test(rnorm(10,sd=1), rnorm(20, sd=2)))
pval(var.test(rnorm(10,sd=1), rnorm(20, sd=2)))
if (require(mosaicData)) {
data(HELPrct)
stat(t.test (age ~ shuffle(sex), data=HELPrct))
# Compare to test statistic computed with permuted values of sex.
do(10) * stat(t.test (age ~ shuffle(sex), data=HELPrct))
}

Description

stats::cor.test() in stats accepts formulas of the shape ~ y + x. The mosaic package allows the use of y ~ x as an alternative formula shape.

Usage

## S3 method for class 'formula'
cor_test(formula, ...)

cor.test(x, ...)

cor_test(x, ...)

## Default S3 method:
cor_test(x, y, ...)

Arguments

See Also

stats::cor.test() in the stats package.

Examples

# This is an example from example(stats::cor.test) done in old and new style
require(graphics)
cor.test(~ CONT + INTG, data = USJudgeRatings)
cor.test(CONT ~ INTG, data = USJudgeRatings)

Description

Construct a product of factors.

Usage

cross(..., sep = ":", drop.unused.levels = FALSE)

Arguments

Value

a factor

Examples

x <- letters[1:3]
y <- c(1,2,1,1,3,1,3)
cross(x, y)
cross(x, y, drop.unused.levels=TRUE)

Description

The do() function facilitates easy replication for randomization tests and bootstrapping (among other things). Part of what makes this particularly useful is the ability to cull from the objects produced those elements that are useful for subsequent analysis. cull_for_do does this culling. It is generic, and users can add new methods to either change behavior or to handle additional classes of objects.

Usage

cull_for_do(object, ...)

Arguments

Details

When do(n) * expression is evaluated, expression is evaluated n times to produce a list of n result objects. cull_for_do is then applied to each element of this list to extract from it the information that should be stored. For example, when applied to a object of class "lm", the default cull_for_do extracts the coefficients, coefficient of determinism, an the estimate for the variance, etc.

Examples

cull_for_do(lm(length ~ width, data = KidsFeet))
do(1) * lm(length ~ width, data = KidsFeet)

Description

Facilitates conversion between degrees and radians.

Usage

deg2rad(x)

rad2deg(x)

Arguments

Value

a numeric vector

See Also

latlon2xyz(), googleMap(), and rgeo().

Examples

deg2rad(180)
rad2deg(2*pi)

Description

Utility functions for creating new variables from logicals describing the levels

Usage

derivedVariable(
  ...,
  .ordered = FALSE,
  .method = c("unique", "first", "last"),
  .debug = c("default", "always", "never"),
  .sort = c("given", "alpha"),
  .default = NULL,
  .asFactor = FALSE
)

derivedFactor(..., .asFactor = TRUE)

Arguments

Details

Each logical "rule" corresponds to a level in the resulting variable. If .default is defined, an implicit rule is added that is TRUE whenever all other rules are FALSE. When there are multiple TRUE rules for a slot, the first or last such is used or an error is generated, depending on the value of method.

derivedVariable is designed to be used with transform() or dplyr::mutate() to add new variables to a data frame. derivedFactor() is the same but that the default value for .asFactor is TRUE. See the examples.

Examples

Kf <- mutate(KidsFeet, biggerfoot2 = derivedFactor(
                   dom = biggerfoot == domhand,
                   nondom = biggerfoot != domhand)
                   )
tally( ~ biggerfoot + biggerfoot2, data = Kf)
tally( ~ biggerfoot + domhand, data = Kf)

# Three equivalent ways to define a new variable
# Method 1: explicitly define all levels
modHELP <- mutate(HELPrct, drink_status = derivedFactor( 
  abstinent = i1 == 0,
  moderate = (i1>0 & i1<=1 & i2<=3 & sex=='female') |
     (i1>0 & i1<=2 & i2<=4 & sex=='male'),
  highrisk = ((i1>1 | i2>3) & sex=='female') | 
      ((i1>2 | i2>4) & sex=='male'),
  .ordered = TRUE)
)
tally( ~ drink_status, data = modHELP)

# Method 2: Use .default for last level
modHELP <- mutate(HELPrct, drink_status = derivedFactor( 
  abstinent = i1 == 0,
  moderate = (i1<=1 & i2<=3 & sex=='female') |
     (i1<=2 & i2<=4 & sex=='male'),
  .ordered = TRUE,
  .method = "first",
  .default = "highrisk")
)
tally( ~ drink_status, data = modHELP)

# Method 3: use TRUE to catch any fall through slots
modHELP <- mutate(HELPrct, drink_status = derivedFactor( 
  abstinent = i1 == 0,
  moderate = (i1<=1 & i2<=3 & sex=='female') |
     (i1<=2 & i2<=4 & sex=='male'),
  highrisk=TRUE,
  .ordered = TRUE,
  .method = "first"
  )
)
tally( ~ drink_status, data = modHELP)
is.factor(modHELP$drink_status)

modHELP <- mutate(HELPrct, drink_status = derivedVariable( 
  abstinent = i1 == 0,
  moderate = (i1<=1 & i2<=3 & sex=='female') |
     (i1<=2 & i2<=4 & sex=='male'),
  highrisk=TRUE,
  .ordered = TRUE,
  .method = "first"
  )
)
is.factor(modHELP$drink_status)

Description

Proves a simple interface to let users interactively design plots in ggformula, lattice, or ggplot2. An option is available to show the code used to create the plot. This can be copied and pasted elsewhere to (into an RMarkdown document, for example) to recreate the plot. Only works in RStudio. Requires the manipulate package.

Usage

design_plot(
  data,
  format,
  default = format,
  system = system_choices()[1],
  show = FALSE,
  title = "",
  data_text = rlang::expr_deparse(substitute(data)),
  ...
)

Arguments

Details

Currently maps are only supported in ggplot2 and not in lattice.

Due to an unresolved issue with RStudio, the first time this function is called, and additional plot is created to correctily initialize the mainipulate frameowrk.

Value

Nothing. Used for side effects.

Examples

## Not run: 
  mtcars2 <- 
    mtcars |> 
      mutate(
        cyl2 = factor(cyl), 
        carb2 = factor(carb),
        shape = c("V-shaped", "straight")[1 + vs], 
        gear2 = factor(gear), 
        transmission = c("automatic", "manual")[1 + am])
  design_plot(mtcars2)

## End(Not run)

Description

Wrappers around diff(mean(...)) and diff(prop(...)) that facilitate better naming of the result

Usage

diffmean(x, ..., data = parent.frame(), only.2 = TRUE)

diffprop(x, ..., data = parent.frame(), only.2 = TRUE)

Arguments

Examples

if (require(mosaicData)) {
diffprop( homeless ~ sex , data=HELPrct)
do(3) * diffprop( homeless ~ shuffle(sex) , data=HELPrct)
diffmean( age ~ substance, data=HELPrct, only.2=FALSE)
do(3) * diffmean(age ~ shuffle(substance), data=HELPrct, only.2=FALSE)
diffmean( age ~ sex, data=HELPrct)
do(3) * diffmean(age ~ shuffle(sex), data=HELPrct)
}

Description

do() provides a natural syntax for repetition tuned to assist with replication and resampling methods.

Usage

do(object, ...)

## S3 method for class 'numeric'
do(object, ...)

## Default S3 method:
do(object, ...)

Do(n = 1L, cull = NULL, mode = "default", algorithm = 1, parallel = TRUE)

## S3 method for class 'repeater'
print(x, ...)

## S4 method for signature 'repeater,ANY'
e1 * e2

Arguments

Value

do returns an object of class repeater which is only useful in the context of the operator *. See the examples.

Naming

The names used in the object returned from do() are inferred from the objects created in each replication. Roughly, this the strategy employed.

• If the objects have names, those names are inherited, if possible.

• If the objects do not have names, but do() is used with a simple function call, the name of that function is used. Example: do(3) * mean(~height, data = Galton) produces a data frame with a variable named mean.

• In cases where names are not easily inferred and a single result is produced, it is named result.

To get different names, one can rename the objects as they are created, or rename the result returned from do(). Example of the former: do(3) * c(mean_height = mean(~height, data = resample(Galton))).

Note

do is a thin wrapper around Do to avoid collision with dplyr::do() from the dplyr package.

Author(s)

Daniel Kaplan ([email protected]) and Randall Pruim ([email protected])

See Also

replicate(), set.rseed()

Examples

do(3) * rnorm(1)
do(3) * "hello"
do(3) * 1:4
do(3) * mean(rnorm(25))
do(3) * lm(shuffle(height) ~ sex + mother, Galton)
do(3) * anova(lm(shuffle(height) ~ sex + mother, Galton))
do(3) * c(sample.mean = mean(rnorm(25)))
# change the names on the fly
do(3) * mean(~height, data = resample(Galton))
do(3) * c(mean_height = mean(~height, data = resample(Galton)))
set.rseed(1234)
do(3) * tally( ~sex|treat, data=resample(HELPrct))
set.rseed(1234)  # re-using seed gives same results again
do(3) * tally( ~sex|treat, data=resample(HELPrct))

Description

Return the path to a documentation file in a package

Usage

docFile(file, package = "mosaic", character.only = FALSE)

Arguments

Value

a character vector specifying the path to the file on the user's system.

Description

A high level function and panel function for producing a variant of a histogram called a dotplot.

Usage

dotPlot(x, breaks, ..., panel = panel.dotPlot)

panel.dotPlot(
  x,
  breaks,
  equal.widths = TRUE,
  groups = NULL,
  nint = if (is.factor(x)) nlevels(x) else round(1.3 * log2(length(x)) + 4),
  pch,
  col,
  lty = trellis.par.get("dot.line")$lty,
  lwd = trellis.par.get("dot.line")$lwd,
  col.line = trellis.par.get("dot.line")$col,
  alpha = trellis.par.get("dot.symbol")$alpha,
  cex = 1,
  type = "count",
  ...
)

Arguments

Value

a trellis object

See Also

histogram()

Examples

if (require(mosaicData)) {
dotPlot( ~ age, data = HELPrct)
dotPlot( ~ age, nint=42, data = HELPrct)
dotPlot( ~ height | voice.part, data = singer, nint = 17,
          endpoints = c(59.5, 76.5), layout = c(4,2), aspect = 1,
          xlab = "Height (inches)")
}

Description

Utility function wrapping up the d/p/q/r distribution functions

Usage

dpqrdist(dist, type = c("d", "p", "q", "r"), ...)

Arguments

Examples

# 3 random draws from N(1,2)
dpqrdist("norm", "r", n = 3, mean = 1, sd = 2)
# These should all be the same
dpqrdist("norm", "d", x = 0) == dnorm(x = 0)
dpqrdist("norm", "p", q = 0, mean = 1, sd = 2) == pnorm(q = 0, mean = 1, sd = 2)
dpqrdist("norm", "q", p = 0.5, mean = 1, sd = 2) == qnorm(p = 0.5, mean = 1, sd = 2)

Description

Expands the contents of functions used in a formula.

Usage

expandFun(formula, ...)

Arguments

Value

A list with the new expanded formula and the combined formals

Examples

f=makeFun(x^2~x)
expandFun(f(z)~z) #Returns z^2~z

Description

A generic function and several instances for creating factors from other sorts of data. The primary use case is for vectors that contain few unique values and might be better considered as factors. When applied to a data frame, this is applied to each variable in the data frame.

Usage

factorize(x, ...)

## Default S3 method:
factorize(x, ...)

## S3 method for class 'numeric'
factorize(x, max.levels = 5L, ...)

## S3 method for class 'character'
factorize(x, max.levels = 5L, ...)

## S3 method for class 'data.frame'
factorize(x, max.levels = 5L, ...)

factorise(x, ...)

Arguments

Examples

data(KidsFeet, package="mosaicData")
str(KidsFeet)
factorize(KidsFeet$birthyear)
str(factorize(KidsFeet))
# alternative spelling
str(factorise(KidsFeet))

Description

Likely you mean to be using favstats(). Each of these computes the mean, standard deviation, quartiles, sample size and number of missing values for a numeric vector, but favstats() can take a formula describing how these summary statistics should be aggregated across various subsets of the data.

Usage

fav_stats(x, ..., na.rm = TRUE, type = 7)

Arguments

Value

A vector of statistical summaries

Examples

fav_stats(1:10)
fav_stats(faithful$eruptions)
data(penguins, package = "palmerpenguins")

# Note: this is favstats() rather than fav_stats()
favstats(bill_length_mm ~ species, data = penguins)

Description

These functions have been moved to the fetch package.

Usage

fetchData(...)

fetchGapminder1(...)

fetchGapminder(...)

fetchGoogle(...)

Arguments

Description

Compute numerically zeros of a function or simultaneous zeros of multiple functions.

Usage

findZeros(
  expr,
  ...,
  xlim = c(near - within, near + within),
  near = 0,
  within = Inf,
  nearest = 10,
  npts = 1000,
  iterate = 1,
  sortBy = c("byx", "byy", "radial")
)

## S3 method for class 'formula'
solve(
  form,
  ...,
  near = 0,
  within = Inf,
  nearest = 10,
  npts = 1000,
  iterate = 1,
  sortBy = c("byx", "byy", "radial")
)

Arguments

Details

Searches numerically using uniroot.

Uses findZerosMult of findZeros to solve the given expression

Value

A dataframe of zero or more numerical values. Plugging these into the expression on the left side of the formula should result in values near zero.

a dataframe with solutions to the expression.

Author(s)

Daniel Kaplan ([email protected])

Cecylia Bocovich

Examples

findZeros( sin(t) ~ t, xlim=c(-10,10) )
# Can use tlim or t.lim instead of xlim if we prefer
findZeros( sin(t) ~ t, tlim=c(-10,10) )
findZeros( sin(theta) ~ theta, near=0, nearest=20)
findZeros( A*sin(2*pi*t/P) ~ t, xlim=c(0,100), P=50, A=2)
# Interval of a normal at half its maximum height.
findZeros( dnorm(x,mean=0,sd=10) - 0.5*dnorm(0,mean=0,sd=10) ~ x )
# A pathological example
# There are no "neareset" zeros for this function.  Each iteration finds new zeros.
f <- function(x) { if (x==0) 0 else sin(1/x) }
findZeros( f(x) ~ x, near=0 )
# Better to look nearer to 0
findZeros( f(x) ~ x, near=0, within=100 )
findZeros( f(x) ~ x, near=0, within=100, iterate=0 )
findZeros( f(x) ~ x, near=0, within=100, iterate=3 )
# Zeros in multiple dimensions (not run: these take a long time)
# findZeros(x^2+y^2+z^2-5~x&y&z, nearest=3000, within = 5)
# findZeros(x*y+z^2~z&y&z, z+y~x&y&z, npts=10)
solve(3*x==3~x)

# plot out sphere (not run)
# sphere = solve(x^2+y^2+z^2==5~x&y&z, within=5, nearest=1000)
# cloud(z~x+y, data=sphere)

Description

Compute numerically zeros of a function of two or more variables. All free variables (all but the variable on the right side) named in the expression must be assigned a value via ⁠\ldots⁠

Usage

findZerosMult(..., npts = 10, rad = 5, near = 0, sortBy = "byx")

Arguments

Details

sorts points in the domain according to the sign of the function value at respective points. Use continuity and uniroot to find zeros between points of opposite signs. Returns any number of points which may be sorted and plotted according to x, y, or radial values.

Value

A data frame of numerical values which should all result in a value of zero when input into original function

Author(s)

Cecylia Bocovich

Examples

findZerosMult(a*x^2-8~a&x, npts = 50)
findZerosMult(a^2+x^2-8~a&x, npts = 100, sortBy='radial')
## Not run: findZerosMult(a^2+x^2-8~a&x, npts = 1000, sortBy='radial')

Description

Allows you to specify a formula with parameters, along with starting guesses for the parameters. Refines those guesses to find the least-squares fit.

Usage

fitModel(formula, data = parent.frame(), start = list(), ...)

model(object, ...)

## S3 method for class 'nlsfunction'
model(object, ...)

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

## S3 method for class 'nlsfunction'
coef(object, ...)

Arguments

Details

Fits a nonlinear least squares model to data. In contrast to linear models, all the parameters (including linear ones) need to be named in the formula. The function returned simply contains the formula together with pre-assigned arguments setting the parameter value. Variables used in the fitting (as opposed to parameters) are unassigned arguments to the returned function.

Value

a function

Note

This doesn't work with categorical explanatory variables. Also, this does not work with synthetic data that fit the model perfectly. See link{nls} for details.

See Also

linearModel(), nls()

Examples

if (require(mosaicData)) {
f <- fitModel(temp ~ A+B*exp(-k*time), data=CoolingWater, start=list(A=50,B=50,k=1/20))
f(time=50)
coef(f)
summary(f)
model(f)
}

Description

These functions create mathematical functions from data, using splines.

Usage

fitSpline(
  formula,
  data = parent.frame(),
  df = NULL,
  knots = NULL,
  degree = 3,
  type = c("natural", "linear", "cubic", "polynomial"),
  ...
)

Arguments

Value

a function of the explanatory variable

See Also

splines::bs() and splines::ns() for the bases used to generate the splines.

Examples

f <- fitSpline( weight ~ height, data=women, df=5 )
xyplot( weight ~ height, data=women )
plotFun(f(height) ~ height, add=TRUE)

g <- fitSpline( length ~ width, data = KidsFeet, type='natural', df=5 )
h <- fitSpline( length ~ width, data = KidsFeet, type='linear', df=5 )
xyplot( length ~ width, data = KidsFeet, col='gray70', pch=16)
plotFun(g, add=TRUE, col='navy')
plotFun(h, add=TRUE, col='red')

Description

mosaic tools for clustering

Usage

## S3 method for class 'hclust'
fortify(
  model,
  data,
  which = c("segments", "heatmap", "leaves", "labels", "data"),
  k = 1,
  ...
)

## S3 method for class 'hclust'
mplot(
  object,
  data,
  colorize = TRUE,
  k = 1,
  labels = FALSE,
  heatmap = 0,
  enumerate = "white",
  ...
)

Arguments

Examples

KidsFeet |> select(-name, -birthmonth) |> rescale() -> KidsFeet2
  M <- dist(KidsFeet2)
  Cl <- hclust(M)
  fortify(Cl, k=5) |> head(3)
  fortify(Cl, which="heatmap", data=KidsFeet2) |> head(3)
  fortify(Cl, which="data", data=KidsFeet2) |> head(3)
  fortify(Cl, which="labels") |> head(3)
  mplot(Cl, data=KidsFeet2, k=4, heatmap=2)
  mplot(Cl, data=KidsFeet2, k=4, heatmap=0.5, enumerate="transparent")
  mplot(Cl, data=KidsFeet2, k=4, heatmap=2, type="triangle")
  mplot(Cl, data=KidsFeet2, k=4, heatmap=0, type="triangle")

Description

Extract data from R objects

Usage

## S3 method for class 'summary.lm'
fortify(model, data = NULL, level = 0.95, ...)

## S3 method for class 'summary.glm'
fortify(model, data = NULL, level = 0.95, ...)

## S3 method for class 'TukeyHSD'
fortify(model, data, order = c("asis", "pval", "difference"), ...)

Arguments

Description

Turn histograms into frequency polygons

Usage

freqpoly(x, plot = TRUE, ...)

hist2freqpolygon(hist)

## S3 method for class 'freqpolygon'
plot(
  x,
  freq = equidist,
  col = graphics::par("fg"),
  lty = NULL,
  lwd = 1,
  main = paste("Frequency polygon of", paste(x$xname, collapse = "\n")),
  sub = NULL,
  xlab = x$xname,
  ylab,
  xlim = range(x$x),
  ylim = NULL,
  axes = TRUE,
  labels = FALSE,
  add = FALSE,
  ann = TRUE,
  ...
)

Arguments

Value

An object of class "freqpoly" (invisibly). Additionally, if plot is TRUE, a plot is generated.

Examples

freqpoly(faithful$eruptions)
bks <- c(0, 1, 1.5, 2, 3, 3.5, 4, 4.5, 5, 7)
hist(faithful$eruptions, breaks = bks)
freqpoly(faithful$eruptions, col = rgb(0,0,1,.5), lwd = 5, breaks = bks, add = TRUE)

Description

Frequency polygons are an alternative to histograms that make it simpler to overlay multiple distributions.

Usage

freqpolygon(
  x,
  ...,
  panel = "panel.freqpolygon",
  prepanel = "prepanel.default.freqpolygon"
)

prepanel.default.freqpolygon(
  x,
  darg = list(),
  plot.points = FALSE,
  ref = FALSE,
  groups = NULL,
  subscripts = TRUE,
  jitter.amount = 0.01 * diff(current.panel.limits()$ylim),
  center = NULL,
  nint = NULL,
  breaks = NULL,
  width = darg$width,
  type = "density",
  ...
)

panel.freqpolygon(
  x,
  darg = list(),
  plot.points = FALSE,
  ref = FALSE,
  groups = NULL,
  weights = NULL,
  jitter.amount = 0.01 * diff(current.panel.limits()$ylim),
  type = "density",
  breaks = NULL,
  nint = NULL,
  center = NULL,
  width = darg$width,
  gcol = trellis.par.get("reference.line")$col,
  glwd = trellis.par.get("reference.line")$lwd,
  h,
  v,
  ...,
  identifier = "freqpoly"
)

Arguments

Value

a trellis object

Note

This function make use of histogram to determine overall layout. Often this works reasonably well but sometimes it does not. In particular, when groups is used to overlay multiple frequency polygons, there is often too little head room. In the latter cases, it may be necessary to use ylim to determine an appropriate viewing rectangle for the plot.

Examples

freqpolygon(~age | substance, data=HELPrct, v=35)
freqpolygon(~age, data=HELPrct, labels=TRUE, type='count')
freqpolygon(~age | substance, data=HELPrct, groups=sex)
freqpolygon(~age | substance, data=HELPrct, groups=sex, ylim=c(0,0.11))
## comparison of histogram and frequency polygon
histogram(~eruptions, faithful, type='density', width=.5)
ladd( panel.freqpolygon(faithful$eruptions, width=.5 ))

Description

These functions create mathematical functions from data, by smoothing, splining, or linear combination (fitting). Each of them takes a formula and a data frame as an argument

Usage

spliner(formula, data = NULL, method = "fmm", monotonic = FALSE)

connector(formula, data = NULL, method = "linear")

smoother(formula, data, span = 0.5, degree = 2, ...)

linearModel(formula, data, ...)

Arguments

Details

These functions use data to create a mathematical, single-valued function of the inputs. All return a function whose arguments are the variables used on the right-hand side of the formula. If the formula involves a transformation, e.g. sqrt(age) or log(income), only the variable itself, e.g. age or income, is an argument to the function.

linearModel takes a linear combination of the vectors specified on the right-hand side. It differs from project in that linearModel returns a function whereas project returns the coefficients. NOTE: An intercept term is not included unless that is explicitly part of the formula with +1. This conflicts with the standard usage of formulas as found in lm. Another option for creating such functions is to combine lm() and makeFun().

spliner and connector currently work for only one input variable.

See Also

project() method for formulas

Examples

if (require(mosaicData)) {
data(CPS85)
f <- smoother(wage ~ age, span=.9, data=CPS85)
f(40)
g <- linearModel(log(wage) ~ age + educ + 1, data=CPS85)
g(age=40, educ=12)
# an alternative way to define g (Note: + 1 is the default for lm().)
g2 <- makeFun(lm(log(wage) ~ age + educ, data=CPS85))
g2(age=40, educ=12)
x<-1:5; y=c(1, 2, 4, 8, 8.2)
f1 <- spliner(y ~ x)
f1(x=8:10)
f2 <- connector(x~y)
}

Description

Uses the full model syntax.

Usage

getVarFormula(formula, data = parent.frame(), intercept = FALSE)

Arguments

Examples

getVarFormula( ~ wt + mpg, data = mtcars)

Description

Creates a URL for Google Maps for a particular latitude and longitude position. This function has been deprecated due to changes in Google's access policies. Give leaflet_map() a try as an alternative.

Usage

googleMap(
  latitude,
  longitude,
  position = NULL,
  zoom = 12,
  maptype = c("roadmap", "satellite", "terrain", "hybrid"),
  mark = FALSE,
  radius = 0,
  browse = TRUE,
  ...
)

Arguments

Value

a string containing a URL. Optionally, as a side-effect, the URL is visited in a browser

See Also

leaflet_map(), deg2rad(), latlon2xyz() and rgeo().

Examples

## Not run: 
googleMap(40.7566, -73.9863, radius=1)   # Times Square
googleMap(position=rgeo(2), radius=1)    # 2 random locations

## End(Not run)

Description

The primary purpose is for inferring argument settings from names derived from variables occurring in a formula. For example, the default use is to infer limits for variables without having to call them xlim and ylim when the variables in the formula have other names. Other uses could easily be devised by specifying different variants.

Usage

inferArgs(
  vars,
  dots,
  defaults = alist(xlim = , ylim = , zlim = ),
  variants = c(".lim", "lim")
)

Arguments

Value

a named list or alist of limits. The names are determined by the names in defaults.

If multiple variants are matched, the first is used.

Examples

inferArgs(c('x','u','t'), list(t=c(1,3), x.lim=c(1,10), u=c(1,3), u.lim=c(2,4)))
inferArgs(c('x','u'), list(u=c(1,3)), defaults=list(xlim=c(0,1), ylim=NULL))

Description

Unlike is.integer(), which checks the type of argument is integer, this function checks whether the value of the argument is an integer (within a specified tolerance).

Usage

is.wholenumber(x, tol = .Machine$double.eps^0.5)

Arguments

Details

This function is borrowed from the examples for is.integer()

Value

a logical vector indicating whether x has a whole number value

Examples

is.wholenumber(1)
all(is.wholenumber(rbinom(100,10,.5)))
is.wholenumber((1:10)/2)

Description

Simplified lattice plotting by adding additional elements to existing plots.

Usage

ladd(x, data = NULL, ..., plot = trellis.last.object())

Arguments

Details

ladd is a wrapper around latticeExtra::layer() that simplifies certain common plotting additions. The same caveats that apply to that function apply here as well. In particular, ladd uses non-standard evaluation. For this reason care must be taken if trying to use ladd within other functions and the use of data may be required to pass information into the environment in which x will be evaluated.

Value

a trellis object

Author(s)

Randall Pruim ([email protected])

See Also

latticeExtra::layer()

Examples

p <- xyplot(rnorm(100) ~rnorm(100))
print(p)
ladd(panel.abline(a=0,b=1))
ladd(panel.abline(h=0,col='blue'))
ladd(grid.text('Hello'))
ladd(grid.text(x=.95,y=.05,'text here',just=c('right','bottom')))
q <- xyplot(rnorm(100) ~rnorm(100)|factor(rbinom(100,4,.5)))
q <- update(q, layout=c(3,2))
ladd(panel.abline(a=0,b=1), plot=q)
ladd(panel.abline(h=0,col='blue'))
ladd( grid.text("(2,1)",gp=gpar(cex=3,alpha=.5)), columns=2, rows=1)
ladd( grid.text("p5",gp=gpar(cex=3,alpha=.5)), packets=5)
q
ladd( grid.text(paste(current.column(), current.row(),sep=','), gp=gpar(cex=3,alpha=.5)) )
histogram( ~eruptions, data=faithful )
# over would probably be better here, but the demonstrates what under=TRUE does.
ladd(panel.densityplot(faithful$eruptions, lwd=4), under=TRUE)

Description

Primarily designed to work with rgeo() to display randomly sampled points on the globe.

Usage

leaflet_map(
  latitude = NULL,
  longitude = NULL,
  position = NULL,
  zoom = 12,
  mark = FALSE,
  radius = 0,
  units = c("km", "miles", "meters", "feet"),
  ...
)

Arguments

Value

a leaflet map

See Also

deg2rad(), latlon2xyz() and rgeo().

Examples

# the leaflet package is required
if (require(leaflet)) {
  # Times Square
  leaflet_map(40.7566, -73.9863, radius = 1, units = "miles")  
  # 3 random locations; 5 km circles
  leaflet_map(position = rgeo(3), radius = 5, mark = TRUE, color = "red") 
  # using pipes
  rgeo(4, latlim = c(25,50), lonlim = c(-65, -125)) |>
    leaflet_map(radius = 5, mark = TRUE, color = "purple")
}

Description

These functions provide a formula based interface to the construction of matrices from data and for fitting. You can use them both for numerical vectors and for functions of variables in data frames. These functions are intended to support teaching basic linear algebra with a particular connection to statistics.

Usage

mat(formula, data = parent.frame(), A = formula)

singvals(formula, data = parent.frame(), A = formula)

Arguments

Value

mat returns a matrix

singvals gives singular values for each column in the model matrix

See Also

project()

linearModel(), which returns a function.

Examples

a <- c(1,0,0); b <- c(1,2,3); c <- c(4,5,6); x <- rnorm(3)
# Formula interface
mat(~a+b)
mat(~a+b+1)
if (require(mosaicData)) {
mat(~length+sex, data=KidsFeet)
singvals(~length*sex*width, data=KidsFeet)
}

Description

The functions compute the sum or mean of all pairwise absolute differences. This differs from stats::mad(), which computes the median absolute difference of each value from the median of all the values. See the ISIwithR package (and the textbook it accompanies) for examples using these functions in the context of simulation-based inference.

Usage

MAD(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))

SAD(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))

Arguments

Value

the mean or sum of the absolute differences between each pair of values in c(x,...).

See Also

mad(), MAD_()

Examples

SAD(1:3)
MAD(1:3)
MAD(~eruptions, data = faithful)

Description

All pairs mean and sum of absolute differences

Usage

MAD_(x, ..., na.rm = getOption("na.omit", FALSE))

SAD_(x, ..., na.rm = getOption("na.omit", FALSE))

Arguments

Value

the mean or sum of the absolute differences between each pair of values in c(x,...).

See Also

mad()

Description

Compute function on subsets of a variable in a data frame.

Usage

maggregate(
  formula,
  data = parent.frame(),
  FUN,
  groups = NULL,
  subset,
  drop = FALSE,
  ...,
  .format = c("default", "table", "flat"),
  .overall = mosaic.par.get("aggregate.overall"),
  .multiple = FALSE,
  .name = deparse(substitute(FUN)),
  .envir = parent.frame()
)

Arguments