Package 'rvec'

Description

Tools for working with random draws from a distribution, eg draws from a posterior distribution in a Bayesian analysis.

Details

An rvec holds multiple draws, but wherever possible behaves like an ordinary R vector. For instance, if x is an rvec holding 1000 draws from a distribution, then 2 * x returns a new rvec where each draw has been multiplied by 2.

To summarise across draws, use a function starting with draws. For instance, to calculate a credible interval, use draws_ci().

Functions

Creating rvecs

• rvec() Class depends on input

• rvec_dbl() Doubles

• rvec_int() Integers

• rvec_lgl() Logical

• rvec_chr() Character

• collapse_to_rvec() Data in data frame

• new_rvec() Blanks

Manipulating rvecs

• if_else_rvec() if_else() where condition is rvec

• map_rvec() map() for rvecs

• extract_draw() Single draw from rvec

Probability distributions

• dbeta_rvec() Beta

• dbinom_rvec() Binomial

• dcauchy_rvec() Cauchy

• dchisq_rvec() Chi-square

• dexp_rvec() Exponential

• df_rvec() F

• dgamma_rvec() Gamma

• dgeom_rvec() Geometric

• dhyper_rvec() Hypergeometric

• dlnorm_rvec() Lognormal

• dmultinom() Multinomial

• dnbinom_rvec() Negative binomial

• dnorm_rvec() Normal

• dpois_rvec() Poisson

• dt_rvec() Student's T

• dunif_rvec() Uniform

• dweibull_rvec() Weibull

Summarizing across draws

• draws_all() All draws

• draws_any() Any draws

• draws_min() Minimum draw

• draws_max() Maximum draw

• draws_median() Median draw

• draws_mean() Mean draw

• draws_mode() Modal draw

• draws_ci() Credible intervals

• draws_quantile() Quantiles

• draws_fun() Arbitrary function

• n_draw() Number of draws

Coercion, classes

• as_list_col() Rvec or matrix to list

• expand_from_rvec() Inverse of collapse_to_rvec()

• is_rvec() Object an rvec?

Weighted summaries

• weighted_mad() Weighted mean absolute deviation

• weighted_mean() Weighted mean

• weighted_median() Weighted median

• weighted_sd() Weighted standard deviation

• weighted_var() Weighted variances

Datasets

• divorce() Divorce rates

• reg_post() Regression coefficients

Packages with similar functionality

• rv

• posterior

Author(s)

Maintainer: John Bryant john@bayesiandemography.com

Other contributors:

• Bayesian Demography Limited [copyright holder]

See Also

Useful links:

• https://bayesiandemography.github.io/rvec/

• https://github.com/bayesiandemography/rvec

• Report bugs at https://github.com/bayesiandemography/rvec/issues

Convert to List Column

Description

Convert an rvec or matrix to a list that can be used as a list column in a data frame.

Usage

as_list_col(x)

## S3 method for class 'rvec'
as_list_col(x)

## S3 method for class 'matrix'
as_list_col(x)

Arguments

Value

A list:

• If x is an rvec, then the list contains length(x) vectors, each of which has n_draw(x) elements.

• If x is a matrix, then the list contains nrow(x) vectors, each of which has ncol(x) elements.

See Also

• rvec() to construct an rvec.

• expand_from_rvec() to convert a data frame from using rvecs to using draw and value columns.

• as_rvar???

• converting rvecs to

• Functions for summarising and plotting distributions in package ggdist understand list columns.

Examples

l <- list(1:3,
          4:6)
r <- rvec(l)
as_list_col(r)

Convert a Data Frame Between 'Database' and 'Rvec' Formats

Description

collapse_to_rvec() converts a data frame from a 'database' format to an 'rvec' format. expand_from_rvec(), does the opposite, converting a data frame from an rvecs format to a database format.

Usage

collapse_to_rvec(data, draw = draw, values = value, by = NULL, type = NULL)

## S3 method for class 'data.frame'
collapse_to_rvec(data, draw = draw, values = value, by = NULL, type = NULL)

## S3 method for class 'grouped_df'
collapse_to_rvec(data, draw = draw, values = value, by = NULL, type = NULL)

expand_from_rvec(data, draw = "draw")

## S3 method for class 'data.frame'
expand_from_rvec(data, draw = "draw")

## S3 method for class 'grouped_df'
expand_from_rvec(data, draw = "draw")

Arguments

Details

In database format, each row represents one random draw. The data frame contains a 'draw' variable that distinguishes different draws within the same combination of 'by' variables. In rvec format, each row represents one combination of 'by' variables, and multiple draws are stored in an rvec. See below for examples.

Value

A data frame.

• collapse_to_rvec() reduces the number of rows by a factor of n_draw().

• expand_from_rvec() increases the number of rows by a factor of n_draw().

• collapse_to_rvec() silently drops all variables that are not draw, value or grouping variables if data is a grouped data frame.

by argument

The by argument is used to specify stratifying variables. For instance if by includes sex and age, then data frame produced by collapse_to_rvec() has separate rows for each combination of sex and age.

If data is a grouped data frame, then the grouping variables take precedence over by.

If no value for by is provided, and data is not a grouped data frame, then collapse_to_rvec() assumes that all variables in data that are not included in value and draw should be included in by.

type argument

By default, collapse_to_rvec() calls function rvec() on each values variable in data. rvec() chooses the class of the output (ie rvec_chr, rvec_dbl, rvec_int, or rvec_lgl) depending on the input. Types can instead be specified in advance, using the type argument. type is a string, each character of which specifies the class of the corresponding values variable. The characters have the following meanings:

• "c": rvec_chr

• "d": rvec_dbl

• "i": rvec_int

• "l": rvec_lgl

• "?": Depends on inputs.

The codes for type are modified from ones used by the readr package.

See Also

• rvec() to construct a single rvec.

• as_list_col() to convert an rvec to a list variable.

• dplyr::group_vars() gives the names of the grouping variables in a grouped data frame.

collapse_to_rvec() and expand_from_rvec() are analogous to tidyr::nest() and tidyr::unnest() though collapse_to_rvec() and expand_from_rvec() move values into and out of rvecs, while tidyr::nest() and tidyr::unnest() move them in and out of data frames. (tidyr::nest() and tidyr::unnest() are also a lot more flexible.)

Examples

library(dplyr)
data_db <- tribble(
  ~occupation,    ~sim, ~pay,
  "Statistician", 1,    100,
  "Statistician", 2,    80,
  "Statistician", 3,    105,
  "Banker",       1,    400,
  "Banker",       2,    350,
  "Banker",       3,    420
)

## database format to rvec format
data_rv <- data_db |>
  collapse_to_rvec(draw = sim,
                   values = pay)
data_rv

## rvec format to database format
data_rv |>
  expand_from_rvec()

## provide a name for the draw variable
data_rv |>
  expand_from_rvec(draw = "sim")

## specify that rvec variable
## must be rvec_int
data_rv <- data_db |>
  collapse_to_rvec(draw = sim,
                   values = pay,
                   type = "i")

## specify stratifying variable explicitly,
## using 'by' argument
data_db |>
  collapse_to_rvec(draw = sim,
                   values = pay,
                   by = occupation)

## specify stratifying variable explicitly,
## using 'group_by'
library(dplyr)
data_db |>
  group_by(occupation) |>
  collapse_to_rvec(draw = sim,
                   values = pay)

The Beta Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the Beta distribution, modified to work with rvecs.

Usage

dbeta_rvec(x, shape1, shape2, ncp = 0, log = FALSE)

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

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

rbeta_rvec(n, shape1, shape2, ncp = 0, n_draw = NULL)

Arguments

Details

Functions dbeta_rvec(), pbeta_rvec(), pbeta_rvec() and rbeta_rvec() work like base R functions dbeta(), pbeta(), qbeta(), and rbeta(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rbeta_rvec() also returns an rvec if a value for n_draw is supplied.

dbeta_rvec(), pbeta_rvec(), pbeta_rvec() and rbeta_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dbeta()

• pbeta()

• qbeta()

• rbeta()

• stats::distributions.

Examples

x <- rvec(list(c(0, 0.25),
               c(0.5, 0.99)))
dbeta_rvec(x, shape1 = 1, shape2 = 1)
pbeta_rvec(x, shape1 = 1, shape2 = 1)

rbeta_rvec(n = 2,
           shape = 1:2,
           shape2 = 1,
           n_draw = 1000)

The Binomial Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the binomial distribution, modified to work with rvecs.

Usage

dbinom_rvec(x, size, prob, log = FALSE)

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

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

rbinom_rvec(n, size, prob, n_draw = NULL)

Arguments

Details

Functions dbinom_rvec(), pbinom_rvec(), pbinom_rvec() and rbinom_rvec() work like base R functions dbinom(), pbinom(), qbinom(), and rbinom(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rbinom_rvec() also returns an rvec if a value for n_draw is supplied.

dbinom_rvec(), pbinom_rvec(), pbinom_rvec() and rbinom_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

• rbinom_rvec() always returns doubles (not integers).

Warning

From version rvec version 0.7.4 onwards, rbinom_rvec() always returns doubles (not integers).

See Also

• dbinom()

• pbinom()

• qbinom()

• rbinom()

• stats::distributions.

Examples

x <- rvec(list(c(3, 8),
               c(0, 2)))
dbinom_rvec(x, size = 8, prob = 0.3)
pbinom_rvec(x, size = 8, prob = 0.3)

rbinom_rvec(n = 2,
            size = 10,
            prob = c(0.7, 0.3),
            n_draw = 1000)

The Cauchy Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the Cauchy distribution, modified to work with rvecs.

Usage

dcauchy_rvec(x, location = 0, scale = 1, log = FALSE)

pcauchy_rvec(q, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)

qcauchy_rvec(p, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)

rcauchy_rvec(n, location = 0, scale = 1, n_draw = NULL)

Arguments

Details

Functions dcauchy_rvec(), pcauchy_rvec(), pcauchy_rvec() and rcauchy_rvec() work like base R functions dcauchy(), pcauchy(), qcauchy(), and rcauchy(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rcauchy_rvec() also returns an rvec if a value for n_draw is supplied.

dcauchy_rvec(), pcauchy_rvec(), pcauchy_rvec() and rcauchy_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dcauchy()

• pcauchy()

• qcauchy()

• rcauchy()

• stats::distributions.

Examples

x <- rvec(list(c(3, -5.1),
               c(0, -2.3)))
dcauchy_rvec(x)
pcauchy_rvec(x)

rcauchy_rvec(n = 2,
             location = c(-5, 5),
             n_draw = 1000)

The Chi-Squared Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the chi-squared distribution, modified to work with rvecs.

Usage

dchisq_rvec(x, df, ncp = 0, log = FALSE)

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

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

rchisq_rvec(n, df, ncp = 0, n_draw = NULL)

Arguments

Details

Functions dchisq_rvec(), pchisq_rvec(), pchisq_rvec() and rchisq_rvec() work like base R functions dchisq(), pchisq(), qchisq(), and rchisq(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rchisq_rvec() also returns an rvec if a value for n_draw is supplied.

dchisq_rvec(), pchisq_rvec(), pchisq_rvec() and rchisq_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dchisq()

• pchisq()

• qchisq()

• rchisq()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5.1),
               c(0.1, 2.3)))
dchisq_rvec(x, df = 3)
pchisq_rvec(x, df = 3)

rchisq_rvec(n = 2,
            df = 3:4,
            n_draw = 1000)

The Exponential Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the exponential distribution, modified to work with rvecs.

Usage

dexp_rvec(x, rate = 1, log = FALSE)

pexp_rvec(q, rate = 1, lower.tail = TRUE, log.p = FALSE)

qexp_rvec(p, rate = 1, lower.tail = TRUE, log.p = FALSE)

rexp_rvec(n, rate = 1, n_draw = NULL)

Arguments

Details

Functions dexp_rvec(), pexp_rvec(), pexp_rvec() and rexp_rvec() work like base R functions dexp(), pexp(), qexp(), and rexp(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rexp_rvec() also returns an rvec if a value for n_draw is supplied.

dexp_rvec(), pexp_rvec(), pexp_rvec() and rexp_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dexp()

• pexp()

• qexp()

• rexp()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5.1),
               c(0.1, 2.3)))
dexp_rvec(x, rate = 1.5)
pexp_rvec(x, rate = 1.5)

rexp_rvec(n = 2,
          rate = c(1.5, 4),
          n_draw = 1000)

The F Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the F distribution, modified to work with rvecs.

Usage

df_rvec(x, df1, df2, ncp = 0, log = FALSE)

pf_rvec(q, df1, df2, ncp = 0, lower.tail = TRUE, log.p = FALSE)

qf_rvec(p, df1, df2, ncp = 0, lower.tail = TRUE, log.p = FALSE)

rf_rvec(n, df1, df2, ncp = 0, n_draw = NULL)

Arguments

Details

Functions df_rvec(), pf_rvec(), pf_rvec() and rf_rvec() work like base R functions df(), pf(), qf(), and rf(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rf_rvec() also returns an rvec if a value for n_draw is supplied.

df_rvec(), pf_rvec(), pf_rvec() and rf_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• df()

• pf()

• qf()

• rf()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5.1),
               c(0.1, 2.3)))
df_rvec(x, df1 = 1, df2 = 3)
pf_rvec(x, df1 = 1, df2 = 3)

rf_rvec(n = 2, df1 = 1,df2 = 2:3, n_draw = 1000)

The Gamma Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the gamma distribution, modified to work with rvecs.

Usage

dgamma_rvec(x, shape, rate = 1, scale = 1/rate, log = FALSE)

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

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

rgamma_rvec(n, shape, rate = 1, scale = 1/rate, n_draw = NULL)

Arguments

Details

Functions dgamma_rvec(), pgamma_rvec(), pgamma_rvec() and rgamma_rvec() work like base R functions dgamma(), pgamma(), qgamma(), and rgamma(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rgamma_rvec() also returns an rvec if a value for n_draw is supplied.

dgamma_rvec(), pgamma_rvec(), pgamma_rvec() and rgamma_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dgamma()

• pgamma()

• qgamma()

• rgamma()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5.1),
               c(0.1, 2.3)))
dgamma_rvec(x, shape = 1)
pgamma_rvec(x, shape = 1)

rgamma_rvec(n = 2,
            shape = 1,
            rate = c(0.5, 1),
            n_draw = 1000)

The Geometric Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the geometric distribution, modified to work with rvecs.

Usage

dgeom_rvec(x, prob, log = FALSE)

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

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

rgeom_rvec(n, prob, n_draw = NULL)

Arguments

Details

Functions dgeom_rvec(), pgeom_rvec(), pgeom_rvec() and rgeom_rvec() work like base R functions dgeom(), pgeom(), qgeom(), and rgeom(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rgeom_rvec() also returns an rvec if a value for n_draw is supplied.

dgeom_rvec(), pgeom_rvec(), pgeom_rvec() and rgeom_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

• rgeom_rvec() always returns doubles (not integers).

Warning

From version rvec version 0.7.4 onwards, rgeom_rvec() always returns doubles (not integers).

See Also

• dgeom()

• pgeom()

• qgeom()

• rgeom()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5),
               c(0, 2)))
dgeom_rvec(x, prob = 0.3)
pgeom_rvec(x, prob = 0.3)

rgeom_rvec(n = 2,
           prob = c(0.5, 0.8),
           n_draw = 1000)

The Hypergeometric Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the hypergeometric distribution, modified to work with rvecs.

Usage

dhyper_rvec(x, m, n, k, log = FALSE)

phyper_rvec(q, m, n, k, lower.tail = TRUE, log.p = FALSE)

qhyper_rvec(p, m, n, k, lower.tail = TRUE, log.p = FALSE)

rhyper_rvec(nn, m, n, k, n_draw = NULL)

Arguments

Details

Functions dhyper_rvec(), phyper_rvec(), phyper_rvec() and rhyper_rvec() work like base R functions dhyper(), phyper(), qhyper(), and rhyper(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rhyper_rvec() also returns an rvec if a value for n_draw is supplied.

dhyper_rvec(), phyper_rvec(), phyper_rvec() and rhyper_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

• rhyper_rvec() always returns doubles (not integers).

Warning

From version rvec version 0.7.4 onwards, rhyper_rvec() always returns doubles (not integers).

See Also

• dhyper()

• phyper()

• qhyper()

• rhyper()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5),
               c(0, 2)))
dhyper_rvec(x, m = 6, n = 6, k = 5)
phyper_rvec(x, m = 6, n = 6, k = 5)

rhyper_rvec(nn = 2,
            k = c(3, 5),
            m = 6,
            n = 6,
            n_draw = 1000)

Divorce Rates in New Zealand

Description

Posterior sample from a model of divorce rates in New Zealand.

Usage

divorce

Format

A tibble with 30,000 rows and the following variables:

• age: Age, in 5-year age groups, 15-19 to 65+.

• sex: "Female" or "Male".

• draw: Index for random draw.

• rate: Divorce rate, per 1000.

Source

Derived from data in tables "Age at divorces by sex (marriages and civil unions) (Annual-Dec)" and "Estimated Resident Population by Age and Sex (1991+) (Annual-Dec)" in the online database Infoshare on the Statistics New Zealand website, downloaded on 22 March 2023.

The Log-Normal Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the log-normal distribution, modified to work with rvecs.

Usage

dlnorm_rvec(x, meanlog = 0, sdlog = 1, log = FALSE)

plnorm_rvec(q, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE)

qlnorm_rvec(p, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE)

rlnorm_rvec(n, meanlog = 0, sdlog = 1, n_draw = NULL)

Arguments

Details

Functions dlnorm_rvec(), plnorm_rvec(), plnorm_rvec() and rlnorm_rvec() work like base R functions dlnorm(), plnorm(), qlnorm(), and rlnorm(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rlnorm_rvec() also returns an rvec if a value for n_draw is supplied.

dlnorm_rvec(), plnorm_rvec(), plnorm_rvec() and rlnorm_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dlnorm()

• plnorm()

• qlnorm()

• rlnorm()

• stats::distributions.

Examples

x <- rvec(list(c(3.1, 5.7),
               c(0.2, 2.3)))
dlnorm_rvec(x)
plnorm_rvec(x)

rlnorm_rvec(n = 2,
            meanlog = c(1, 3),
            n_draw = 1000)

The Multinomial Distribution, Using Multiple Draws

Description

Density function random generation for the multinomial distribution, modified to work with rvecs.

Usage

dmultinom_rvec(x, size = NULL, prob, log = FALSE)

rmultinom_rvec(n, size, prob, n_draw = NULL)

Arguments

Details

Functions dmultinom_rvec()and rmultinom_rvec() work like base R functions dmultinom() and rmultinom(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rmultinom_rvec() also returns an rvec if a value for n_draw is supplied.

Like the base R functions dmultinom() and [rmultinom(), dmultinom_rvec() and rmultinom_rvec() do not recycle their arguments.

Value

• dmultinom()

  • If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

  • Otherwise an ordinary R vector.

• rmultinom()

  • If n is 1, an rvec or ordinary R vector.

  • If n is greater than 1, a list of rvecs or ordinary R vectors

• rmultinom_rvec() always returns doubles (not integers).

Warning

From version rvec version 0.7.4 onwards, rmultinom_rvec() always returns doubles (not integers).

See Also

• dmultinom()

• rmultinom()

• stats::distributions.

Examples

x <- rvec(list(c(1, 4, 0),
               c(1, 0, 0),
               c(1, 0, 0),
               c(1, 0, 4)))
prob <- c(1/4, 1/4, 1/4, 1/4)
dmultinom_rvec(x = x, prob = prob)
rmultinom_rvec(n = 1,
               size = 100,
               prob = c(0.1, 0.4, 0.2, 0.3),
               n_draw = 1000)

The Negative Binomial Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the negative binomial distribution, modified to work with rvecs.

Usage

dnbinom_rvec(x, size, prob, mu, log = FALSE)

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

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

rnbinom_rvec(n, size, prob, mu, n_draw = NULL)

Arguments

Details

Functions dnbinom_rvec(), pnbinom_rvec(), pnbinom_rvec() and rnbinom_rvec() work like base R functions dnbinom(), pnbinom(), qnbinom(), and rnbinom(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rnbinom_rvec() also returns an rvec if a value for n_draw is supplied.

dnbinom_rvec(), pnbinom_rvec(), pnbinom_rvec() and rnbinom_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

• rnbinom_rvec() always returns doubles (not integers).

Warning

From version rvec version 0.7.4 onwards, rnbinom_rvec() always returns doubles (not integers).

See Also

• dnbinom()

• pnbinom()

• qnbinom()

• rnbinom()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5),
               c(0, 2)))
dnbinom_rvec(x, size = 6, prob = 0.2)
pnbinom_rvec(x, size = 6, prob = 0.2)

rnbinom_rvec(n = 2,
             size = 2,
             mu = c(4, 8),
             n_draw = 1000)

The Normal Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the normal distribution, modified to work with rvecs.

Usage

dnorm_rvec(x, mean = 0, sd = 1, log = FALSE)

pnorm_rvec(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)

qnorm_rvec(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)

rnorm_rvec(n, mean = 0, sd = 1, n_draw = NULL)

Arguments

Details

Functions dnorm_rvec(), pnorm_rvec(), pnorm_rvec() and rnorm_rvec() work like base R functions dnorm(), pnorm(), qnorm(), and rnorm(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rnorm_rvec() also returns an rvec if a value for n_draw is supplied.

dnorm_rvec(), pnorm_rvec(), pnorm_rvec() and rnorm_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dnorm()

• pnorm()

• qnorm()

• rnorm()

• stats::distributions.

Examples

x <- rvec(list(c(3.1, -5.4),
               c(0.1, 2.3)))
dnorm_rvec(x)
pnorm_rvec(x)

rnorm_rvec(n = 2,
           mean = c(-3, 3),
           sd = c(2, 4),
           n_draw = 1000)

The Poisson Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the Poisson distribution, modified to work with rvecs.

Usage

dpois_rvec(x, lambda, log = FALSE)

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

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

rpois_rvec(n, lambda, n_draw = NULL)

Arguments

Details

Functions dpois_rvec(), ppois_rvec(), ppois_rvec() and rpois_rvec() work like base R functions dpois(), ppois(), qpois(), and rpois(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rpois_rvec() also returns an rvec if a value for n_draw is supplied.

dpois_rvec(), ppois_rvec(), ppois_rvec() and rpois_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

• rpois_rvec() always returns doubles (not integers).

Warning

From version rvec version 0.7.4 onwards, rpois_rvec() always returns doubles (not integers).

See Also

• dpois()

• ppois()

• qpois()

• rpois()

• stats::distributions.

Examples

x <- rvec(list(c(3, 5),
               c(1, 2)))
dpois_rvec(x, lambda = 3)
ppois_rvec(x, lambda = 3)

rpois_rvec(n = 2,
           lambda = c(5, 10),
           n_draw = 1000)

Logical Operations Across Random Draws

Description

Apply all or any logical summaries across random draws.

Usage

draws_all(x, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_all(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_all(x, na_rm = FALSE)

draws_any(x, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_any(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_any(x, na_rm = FALSE)

Arguments

Value

A vector.

See Also

Apply pre-specified functions across draws:

• draws_min()

• draws_max()

• draws_median()

• draws_mean()

• draws_mode()

• draws_ci()

• draws_quantile()

Apply arbitrary function across draws:

• draws_fun()

For additional functions for summarising random draws, see tidybayes and ggdist. Function as_list_col() converts rvecs into a format that tidybayes and ggdist can work with.

Examples

m <- rbind(a = c(TRUE,  FALSE,  TRUE),
           b = c(TRUE,  TRUE,   TRUE),
           c = c(FALSE, FALSE,  FALSE))
x <- rvec(m)
x
draws_all(x)
draws_any(x)

Credible Intervals from Random Draws

Description

Summarise the distribution of random draws in an rvec, using credible intervals.

Usage

draws_ci(x, width = 0.95, prefix = NULL, na_rm = FALSE)

## S3 method for class 'rvec'
draws_ci(x, width = 0.95, prefix = NULL, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_ci(x, width = 0.95, prefix = NULL, na_rm = FALSE)

Arguments

Value

A tibble with three columns.

Warning

It is tempting to assign the results of a call to draws_ci() to a column in a data frame, as in

my_df$ci <- draws_ci(my_rvec)

However, creating columns in this way can corrupt an ordinary data frames. For safer options, see the examples below.

See Also

draws_quantile() gives more options for forming quantiles.

Other ways of applying pre-specified functions across draws are:

• draws_all()

• draws_any

• draws_min()

• draws_max()

• draws_median()

• draws_mean()

• draws_mode()

• draws_quantile()

Apply arbitrary function across draws:

• draws_fun()

For additional functions for summarising random draws, see tidybayes and ggdist. Function as_list_col() converts rvecs into a format that tidybayes and ggdist can work with.

Examples

set.seed(0)
m <- rbind(a = rnorm(100, mean = 5, sd = 2),
           b = rnorm(100, mean = -3, sd = 3),
           c = rnorm(100, mean = 0, sd = 20))
x <- rvec(m)
x
draws_ci(x)
draws_ci(x, width = c(0.5, 0.99))
draws_ci(x, prefix = "results")

## results from 'draws_ci'
## assigned to a data frame
library(dplyr)
df <- data.frame(x)

## base R approach
cbind(df, draws_ci(x))

## a tidyverse alternative:
## mutate with no '='
df |> mutate(draws_ci(x))

Apply Summary Function Across Random Draws

Description

Summarise the distribution of random draws in an rvec, using a function.

Usage

draws_fun(x, fun, ...)

## S3 method for class 'rvec'
draws_fun(x, fun, ...)

Arguments

Value

The results from calls to fun, combined using vctrs::vec_c().

See Also

Apply pre-specified functions across draws:

• draws_all()

• draws_any()

• draws_ci()

• draws_min()

• draws_max()

• draws_median()

• draws_mean()

• draws_mode()

• draws_quantile()

Examples

set.seed(0)
m <- rbind(a = rnorm(100, mean = 5, sd = 2),
           b = rnorm(100, mean = -3, sd = 3),
           c = rnorm(100, mean = 0, sd = 20))
x <- rvec(m)
x
draws_fun(x, fun = mad)
draws_fun(x, fun = range)
draws_fun(x, weighted.mean, wt = runif(100))
draws_fun(x, function(x) sd(x) / mean(x))

Medians, Means, and Modes Across Random Draws

Description

Summarise the distribution of random draws in an rvec, using means, medians, or modes.

Usage

draws_median(x, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_median(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_median(x, na_rm = FALSE)

draws_mean(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_mean(x, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_mean(x, na_rm = FALSE)

draws_mode(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_mode(x, na_rm = FALSE)

Arguments

Details

When method is "mode", reduce_rvec() returns the most common value for each observation. When there is a tie, it returns NA.

Value

A vector.

See Also

Apply pre-specified functions across draws:

• draws_all()

• draws_any()

• draws_min()

• draws_max()

• draws_ci()

• draws_quantile()

Apply arbitrary function across draws:

• draws_fun()

For additional functions for summarising random draws, see tidybayes and ggdist. Function as_list_col() converts rvecs into a format that tidybayes and ggdist can work with.

Examples

m <- rbind(a = c(1, 1, 1, 2, 3),
           b = c(2, 4, 0, 2, 3),
           c = c(0, 0, 1, 0, 100))
x <- rvec(m)
x
draws_median(x)
draws_mean(x)
draws_mode(x)

Minima and Maxima Across Random Draws

Description

Apply min or max across random draws.

Usage

draws_min(x, na_rm = FALSE)

draws_max(x, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_min(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_min(x, na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_max(x, na_rm = FALSE)

## S3 method for class 'rvec'
draws_max(x, na_rm = FALSE)

Arguments

Value

A vector.

See Also

Apply pre-specified functions across draws:

• draws_all()

• draws_any()

• draws_median()

• draws_mean()

• draws_mode()

• draws_ci()

• draws_quantile()

Apply arbitrary function across draws:

• draws_fun()

For additional functions for summarising random draws, see tidybayes and ggdist. Function as_list_col() converts rvecs into a format that tidybayes and ggdist can work with.

Examples

m <- rbind(a = c(1,  -3,  2),
           b = c(Inf,  0,   -Inf),
           c = c(0.2, 0.3,  0.1))
x <- rvec(m)
x
draws_min(x)
draws_max(x)

Quantiles Across Random Draws

Description

Summarise the distribution of random draws in an rvec, using quantiles.

Usage

draws_quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975), na_rm = FALSE)

## S3 method for class 'rvec'
draws_quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975), na_rm = FALSE)

## S3 method for class 'rvec_chr'
draws_quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975), na_rm = FALSE)

Arguments

Details

The probs argument defaults to c(0.025, 0.25, 0.5, 0.75, 0.975), the values needed for a median, a 50% credible intervals, and a 95% credible interval.

Value

A tibble.

Warning

It is tempting to assign the results of a call to draws_quantile() to a column in a data frame, as in

my_df$quantile <- draws_quantile(my_rvec)

However, creating data frame columns in this way can corrupt data frames. For safer options, see the examples below.

See Also

draws_ci() creates simple credible intervals.

Other functions for applying pre-specified functions across draws are:

• draws_all()

• draws_any()

• draws_ci()

• draws_min()

• draws_max()

• draws_median()

• draws_mean()

• draws_mode()

Apply arbitrary function across draws:

• draws_fun()

For additional functions for summarising random draws, see tidybayes and ggdist. Function as_list_col() converts rvecs into a format that tidybayes and ggdist can work with.

Examples

set.seed(0)
m <- rbind(a = rnorm(100, mean = 5, sd = 2),
           b = rnorm(100, mean = -3, sd = 3),
           c = rnorm(100, mean = 0, sd = 20))
x <- rvec(m)
x
draws_quantile(x)

## results from 'draws_quantile'
## assigned to a data frame
library(dplyr)
df <- data.frame(x)

## base R approach
cbind(df, draws_quantile(x))

## a tidyverse alternative:
## mutate with no '='
df |>
  mutate(draws_quantile(x))

Student t Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the t distribution, modified to work with rvecs.

Usage

dt_rvec(x, df, ncp = 0, log = FALSE)

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

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

rt_rvec(n, df, ncp = 0, n_draw = NULL)

Arguments

Details

Functions dt_rvec(), pt_rvec(), pt_rvec() and rt_rvec() work like base R functions dt(), pt(), qt(), and rt(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rt_rvec() also returns an rvec if a value for n_draw is supplied.

dt_rvec(), pt_rvec(), pt_rvec() and rt_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dt()

• pt()

• qt()

• rt()

• stats::distributions.

Examples

x <- rvec(list(c(-3.2, 5.3),
               c(-1.6, 2)))
dt_rvec(x, df = 4)
pt_rvec(x, df = 4)

rt_rvec(n = 2,
        df = c(3, 5),
        n_draw = 1000)

Uniform Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the uniform distribution, modified to work with rvecs.

Usage

dunif_rvec(x, min = 0, max = 1, log = FALSE)

punif_rvec(q, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)

qunif_rvec(p, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)

runif_rvec(n, min = 0, max = 1, n_draw = NULL)

Arguments

Details

Functions dunif_rvec(), punif_rvec(), punif_rvec() and runif_rvec() work like base R functions dt(), pt(), qt(), and rt(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function runif_rvec() also returns an rvec if a value for n_draw is supplied.

dunif_rvec(), punif_rvec(), punif_rvec() and runif_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dunif()

• punif()

• qunif()

• runif()

• stats::distributions.

Examples

x <- rvec(list(c(0.2, 0.5),
               c(0.6, 0.7)))
dunif_rvec(x)
punif_rvec(x)

runif_rvec(n = 2,
           min = c(0, 0.5),
           n_draw = 1000)

Weibull Distribution, Using Multiple Draws

Description

Density, distribution function, quantile function and random generation for the Weibull distribution, modified to work with rvecs.

Usage

dweibull_rvec(x, shape, scale = 1, log = FALSE)

pweibull_rvec(q, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)

qweibull_rvec(p, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)

rweibull_rvec(n, shape, scale = 1, n_draw = NULL)

Arguments

Details

Functions dweibull_rvec(), pweibull_rvec(), pweibull_rvec() and rweibull_rvec() work like base R functions dt(), pt(), qt(), and rt(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rweibull_rvec() also returns an rvec if a value for n_draw is supplied.

dweibull_rvec(), pweibull_rvec(), pweibull_rvec() and rweibull_rvec() use tidyverse vector recycling rules:

• Vectors of length 1 are recycled

• All other vectors must have the same size

Value

• If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec

• Otherwise an ordinary R vector.

See Also

• dweibull()

• pweibull()

• qweibull()

• rweibull()

• stats::distributions.

Examples

x <- rvec(list(c(3.2, 4.5),
               c(0.6, 0.7)))
dweibull_rvec(x, shape = 2)
pweibull_rvec(x, shape = 2)

rweibull_rvec(n = 2,
              shape = c(2, 3),
              n_draw = 1000)

Extract a Single Draw From an Rvec

Description

Extract a single draw from x. If a value is supplied for i, extract the ith draw; otherwise extract a random draw.

Usage

extract_draw(x, i = NULL)

Arguments

Value

A vector, with type

• double, if x has class "rvec_dbl",

• integer, if x has class "rvec_int",

• character, if x has class "rvec_chr",

• logical, if x has class "rvec_lgl".

See Also

n_draw() Number of draws

Examples

x <- rvec(matrix(1:50, ncol = 5))
extract_draw(x, i = 1)
extract_draw(x)

Vectorised If-Else, When Condition is an Rvec

Description

A version of if_else for the situation where condition is an rvec.

Usage

if_else_rvec(condition, true, false, missing = NULL, size = NULL)

Arguments

Value

An rvec with the same number of draws as condition.

See Also

• base R function ifelse() does not not work correctly if any of the inputs are rvecs.

• dplyr function if_else works correctly if arguments true, false or missing are rvecs, but not if argument condition is an rvec.

Examples

x <- rvec(list(c(1, 11),
               c(2, 5),
               c(22, 6)))

x > 10 ## rvec_lgl

## if_else_rvec needed when
## 'condition' is an rvec
if_else_rvec(x > 10, 10, x)

## dplyr::if_else works when
## 'true', 'false', or 'missing'
## (but not 'condition') are rvecs
library(dplyr)
if_else(c(TRUE, FALSE, TRUE), x, 100)

Is an Object an Rvec

Description

Test whether x inherits from class "rvec".

Usage

is_rvec(x)

Arguments

Value

TRUE or FALSE.

See Also

• rvec() to create an rvec

• as.matrix(), as_list_col(), to convert an rvec into other formats

Examples

x <- rvec_dbl()
is_rvec(x)

Apply a Function and Put Results in an Rvec

Description

Apply function .f to each element of .x, and then combine the results into an rvec with the same length as .x.

Usage

map_rvec(.x, .f, ...)

Arguments

Details

Each call to function .f should produce an rvec with length 1.

Value

An rvec with the same length as .x.

See Also

map_rvec() is based on the map functions in package purrr, though the internal implementation is different.

Base R functions sapply() and vapply() do not work properly with rvecs. [lapply() works, but to combine the results into a single rvec, functions such as c() or vctrs::vec_c() are needed.

Examples

l <- list(a = rvec(matrix(1:2, 1)),
          b = rvec(matrix(1:4, 2)),
          c = rvec(matrix(1:6, 3)))
l
map_rvec(l, sum)

## sapply does not work with rvecs
sapply(l, sum)

Matrix Multiplication with Rvecs

Description

Matrix multiplication %*% can be used with rvecs. However, in constrast to standard R vectors, multiplying an rvec by a matrix does not produce a row or column vector. Instead it produces an ordinary rvec, with no dimensions.

Usage

## S3 method for class 'rvec'
matrixOps(x, y)

Arguments

Value

An rvec, if x or y is an rvec.

Examples

A <- matrix(c(10, 10, 10,
              11, 11, 11),
            nrow = 2, byrow = TRUE)
x <- rvec(list(c(1, 2),
               c(3, 4),
               c(5, 6)))
A %*% x

## matrix multiplication with an
## ordinary R matrix produces
## a row or column vector
y <- c(1, 3, 5)
A %*% y

Missing, Finite, and Infinite Values in Rvecs

Description

Detect or remove missing and infinite values in rvecs. Operations are done independently on each draw, though na.omit(), na.exclude(), and na.fail() also look across draws.

Usage

## S3 method for class 'rvec'
anyNA(x, recursive = FALSE)

## S3 method for class 'rvec'
is.na(x)

## S3 method for class 'rvec'
na.exclude(object, ...)

## S3 method for class 'rvec'
na.omit(object, ...)

Arguments

Details

The behavior of the rvec methods for is.na(), is.nan(), is.finite(), and is.infinite() differs from the standard vctrs behavior, which is to return a logical vector with length equal to length(x). With rvecs, the standard vctrs behavior would entail summarising across draws, which is the job of the draws_* functions.

Value

• anyNA() - A logical rvec with length 1.

• is.na(), is.nan(), is.finite(), is.infinite() - A logical rvec with the same length as the original rvec.

• na.omit(), na.exclude() - An rvec with the same class as the original rvec, minus any elements that have NAs in any draws.

• na.fail() - The original rvec, or an error.

See Also

• if_else_rvec() for modifying individual values within draws.

• Base R functions is.na(), is.nan(), is.finite(), is.infinite(), anyNA(), na.omit(), na.exclude()

• vctrs::vec_detect_missing() to test whether all draws for an observation are missing.

• vctrs::vec_detect_complete() to test whether any draws for an observation are missing.

• draws_any(), draws_all() to summarise across draws.

Examples

x <- rvec(list(c(1.2, NA),
               c(Inf, 3),
               c(-1, NaN)))

## return a logical rvec
is.na(x)
is.nan(x)
is.finite(x)
is.infinite(x)

## return a logical rvec with length 1
anyNA(x)

## summarise across draws
draws_any(anyNA(x))

## return an NA-free version of 'x'
na.omit(x)
na.exclude(x)

## use 'if_else_rvec' to modify values
## within rvec
if_else_rvec(is.na(x), 999, x)

## vctrs functions
library(vctrs, warn.conflicts = FALSE)
## all draws missing
vec_detect_missing(x)
## any draws missing
vec_detect_complete(x)

Query Number of Draws

Description

Get a count of the random draws held by x. If x does not hold random draws, then n_draw() throws an error.

Usage

n_draw(x)

## Default S3 method:
n_draw(x)

## S3 method for class 'rvec'
n_draw(x)

Arguments

Value

An integer scalar.

See Also

• is_rvec() to test if an object is an rvec.

Examples

m <- matrix(1:40, nrow = 4, ncol = 10)
x <- rvec(m)
n_draw(x)

Create a Blank Rvec

Description

Create an rvec, consisting entirely of NAs, with a given length and number of draws.

Usage

new_rvec(x = double(), length = 0, n_draw = 1000)

Arguments

Details

The type of the object is taken from x. If typeof(x) is "integer", for instance, then new_rvec() returns an object of class "rvec_int".

Value

An rvec.

See Also

• rvec() rvec_chr(), rvec_dbl(), rvec_int(), rvec_lgl() Create an rvec from data.

• n_draw() Query number of draws.

Examples

new_rvec()
new_rvec(TRUE, length = 3, n_draw = 100)

x <- new_rvec(length = 2)
x[1] <- rnorm_rvec(n = 1, n_draw = 1000)
x[2] <- runif_rvec(n = 1, n_draw = 1000)

Calculate Probabilities from Random Draws

Description

Convert an rvec of logical values (an rvec_lgl) into a vector of probabilities.

Usage

prob(x, na_rm = FALSE)

## S3 method for class 'rvec_lgl'
prob(x, na_rm = FALSE)

## S3 method for class 'logical'
prob(x, na_rm = FALSE)

Arguments

Details

prob() is essentially just draws_mean() with a different name. The proportion of draws that are TRUE is used as an estimate of the underlying probability. The different name can make the intent of the code clearer.

Value

A logical vector with the same length as x.

See Also

• draws_mean() Means across draws. Gives the same result as prob when applied to logical rvecs.

Examples

m <- rbind(c(FALSE,  TRUE),
           c(TRUE,   TRUE),
           c(FALSE,  FALSE))
x <- rvec(m)
x
prob(x)

## logical rvec created on the fly
## through operations such as '>'
m <- rbind(c(-1,  1.3, 2),
           c(2, 0.1, -1),
           c(Inf, 0, -0.5))
y <- rvec(m)
y
prob(y > 0)
prob(y >= 0)
prob(y^2 > 0)

Sample Ranks, Including Rvecs

Description

Calculate sample ranks for ordinary vectors or for rvecs. In the case of rvecs, ranks are calculated independently for each draw.

Usage

rank(
  x,
  na.last = TRUE,
  ties.method = c("average", "first", "last", "random", "max", "min")
)

Arguments

Details

To enable different behavior for rvecs and for ordinary vectors, the base R function base::rank() is turned into a generic, with base::rank() as the default.

For details on the calculations, see the documentation for base::rank().

Value

An object of class rvec_int() if x is an rvec. Otherwise an ordinary integer vector.

Examples

x <- rvec(list(c(3, 30),
               c(0, 100)))
rank(x)

Posterior Sample from Linear Regression

Description

Posterior sample for parameters from a linear regression model.

Usage

reg_post

Format

A matrix with 200 columns and the following rows:

• alpha: Intercept parameter

• beta: Slope parameter

• sigma: Standard deviation of error term

Source

reg_post contains values from the second half of the line dataset in package coda. The line dataset draws on the BUGS manual: Spiegelhalter, D.J., Thomas, A., Best, N.G. and Gilks, W.R. (1995) BUGS: Bayesian inference using Gibbs Sampling, Version 0.5, MRC Biostatistics Unit, Cambridge.

Create an Rvec from Data

Description

Create an object of class "rvec", based on input data.

Usage

rvec(x)

rvec_chr(x = NULL)

rvec_dbl(x = NULL)

rvec_int(x = NULL)

rvec_lgl(x = NULL)

Arguments

Details

Class "rvec" has four subclasses, each dealing with a diffent type:

• "rvec_dbl" doubles

• "rvec_int" integers

• "rvec_lgl" logical

• "rvec_chr" character

These subclasses are analogous to double(), integer(), logical(), and character() vectors.

Function rvec() chooses the subclass, based on x. Functions rvec_dbl(), rvec_int(), rvec_lgl(), and rvec_chr() each create objects of a particular subclass.

x can be

• a matrix, where each row is a set of draws for an unknown quantity;

• a list, where each element is a set of draws;

• an atomic vector, which is treated as a single-column matrix; or

• an rvec.

Value

An rvec with the following class:

• rvec_dbl(): "rvec_dbl"

• rvec_int(): "rvec_int"

• rvec_lgl(): "rvec_lgl"

• rvec_chr(): "rvec_chr"

• rvec(): "rvec_chr", "rvec_dbl" "rvec_int", or "rvec_lgl"

See Also

• new_rvec() Create a blank rvec.

• collapse_to_rvec() Create rvecs within a data frame.

• rnorm_rvec(), rbinom_rvec(), etc. Create rvecs representing probability distributions.

Examples

m <- rbind(c(-1.5, 2, 0.2),
           c(-2.3, 3, 1.2))
rvec_dbl(m)

l <- list(rpois(100, lambda = 10.2),
          rpois(100, lambda = 5.5))
rvec(l)

rvec(letters[1:5])

l <- list(a = c(TRUE, FALSE),
          b = c(FALSE, TRUE))
rvec(l)

Standard Deviation, Including Rvecs

Description

Calculate standard deviation of x, where x can be an rvec. If x is an rvec, separate standard deviations are calculated for each draw.

Usage

sd(x, na.rm = FALSE)

Arguments

Details

To enable different behavior for rvecs and for ordinary vectors, the base R function stats::sd() is turned into a generic, with stats::sd() as the default.

For details on the calculations, see the documentation for stats::sd().

Value

An rvec, if x is an rvec. Otherwise typically a numeric vector.

See Also

var()

Examples

x <- rvec(cbind(rnorm(10), rnorm(10, sd = 20)))
x
sd(x)

Correlation, Variance and Covariance (Matrices), Including Rvecs

Description

Calculate correlations and variances, including when x or y is an rvec.

Usage

var(x, y = NULL, na.rm = FALSE, use)

Arguments

Details

To enable different behavior for rvecs and for ordinary vectors, the base R function stats::var() is turned into a generic, with stats::var() as the default.

For details on the calculations, see the documentation for stats::var().

Value

An rvec, if x or y is an rvec. Otherwise typically a numeric vector or matrix.

See Also

sd()

Examples

x <- rvec(cbind(rnorm(10), rnorm(10, sd = 20)))
x
var(x)

Calculate Weighted Summaries

Description

Calculate weighted

• means

• medians

• MADs (mean absolute deviations)

• variances

• standard deviations.

These functions all work with ordinary vectors and with rvecs.

Usage

weighted_mean(x, wt = NULL, na_rm = FALSE)

## Default S3 method:
weighted_mean(x, wt = NULL, na_rm = FALSE)

## S3 method for class 'rvec'
weighted_mean(x, wt = NULL, na_rm = FALSE)

weighted_mad(x, wt = NULL, na_rm = FALSE)

## Default S3 method:
weighted_mad(x, wt = NULL, na_rm = FALSE)

## S3 method for class 'rvec'
weighted_mad(x, wt = NULL, na_rm = FALSE)

weighted_median(x, wt = NULL, na_rm = FALSE)

## Default S3 method:
weighted_median(x, wt = NULL, na_rm = FALSE)

## S3 method for class 'rvec'
weighted_median(x, wt = NULL, na_rm = FALSE)

weighted_sd(x, wt = NULL, na_rm = FALSE)

## Default S3 method:
weighted_sd(x, wt = NULL, na_rm = FALSE)

## S3 method for class 'rvec'
weighted_sd(x, wt = NULL, na_rm = FALSE)

weighted_var(x, wt = NULL, na_rm = FALSE)

## Default S3 method:
weighted_var(x, wt = NULL, na_rm = FALSE)

## S3 method for class 'rvec'
weighted_var(x, wt = NULL, na_rm = FALSE)

Arguments

Details

x and wt must have the same length.

Internally the calculations are done by matrixStats functions such as matrixStats::weightedMean() and matrixStats::colWeightedMeans().

Value

If x or wt or is rvec, then an rvec of length 1. Otherwise, a scalar.

See Also

• Functions mean(), median(), mad(), var(), sd() for unweighted data all have methods for rvecs

• The original matrixStats weighted summary functions have additional options not implemented in the functions here.

• weighted.mean() is a base R function for weighted data

• For numeric summaries of draws in an rvec, use draws_median(), draws_mean, draws_quantile(), draws_fun().

Examples

## 'x' is rvec, 'wt' is ordinary vector
v <- rvec(list(c(1, 11),
               c(2, 12),
               c(7, 17)))
weights <- c(40, 80, 72)
weighted_mean(v, wt = weights)

## 'x' is ordinary vector, 'wt' is rvec
y <- c(1, 2, 3)
w <- rvec(list(c(100, 200),
               c(210, 889),
               c(200, 200)))
weighted_mean(y, wt = w)
weighted_mean(y, wt = w, na_rm = TRUE)