Type: Package
Title: Graph Probability Distributions with User Supplied Parameters and Statistics
Version: 4.5.0
Depends: R (≥ 4.0.0)
Description: Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities. Also provided on the graph is the mean and variance of the distribution.
License: MIT + file LICENSE
URL: https://github.com/coatless-rpkg/visualize, https://thecoatlessprofessor.com/projects/visualize/, https://r-pkg.thecoatlessprofessor.com/visualize/
BugReports: https://github.com/coatless-rpkg/visualize/issues
Encoding: UTF-8
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-11-13 09:26:55 UTC; ronin
Author: James Balamuta ORCID iD [aut, cph, cre]
Maintainer: James Balamuta <james.balamuta@gmail.com>
Repository: CRAN
Date/Publication: 2023-11-13 09:40:02 UTC

visualize: Graph Probability Distributions with User Supplied Parameters and Statistics

Description

Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities. Also provided on the graph is the mean and variance of the distribution.

Author(s)

Maintainer: James Balamuta james.balamuta@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

Examples


## visualize.it acts as the general wrapper.
## For guided application of visualize, see the visualize.distr_name list.
# Binomial distribution evaluated at lower tail.
visualize.it(dist = 'binom', stat = 2, params = list(size = 4,prob = .5),
             section ="lower", strict = TRUE)
visualize.binom(stat = 2, size = 4, prob =.5, section ="lower", strict = TRUE)

# Set to shade inbetween a bounded region.
visualize.it(dist = 'norm', stat = c(-1, 1), list(mu = 0, sd = 1), section="bounded")
visualize.norm(stat = c(-1, 1), mu = 0, sd = 1, section ="bounded")

# Gamma distribution evaluated at upper tail.
visualize.it(dist = 'gamma', stat = 2, params = list(alpha = 2, theta = 1), section="upper")
visualize.gamma(stat = 2, alpha = 2, theta = 1, section="upper")

Visualize Beta Distribution

Description

Generates a plot of the Beta distribution with user specified parameters.

Usage

visualize.beta(stat = 1, alpha = 3, beta = 2, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

alpha

alpha is considered to be shape1 by R's implementation of the beta distribution. alpha must be greater than 0.

beta

beta is considered to be shape2 by R's implementation of the beta distribution. beta must be greater than 0.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dbeta().

Examples


# Evaluates lower tail.
visualize.beta(stat = 1, alpha = 2, beta = 3, section = "lower") 

# Evaluates bounded region.
visualize.beta(stat = c(.5,1), alpha = 4, beta = 3, section = "bounded") 

# Evaluates upper tail.
visualize.beta(stat = 1, alpha = 2, beta = 3, section = "upper")

Visualize Binomial Distribution

Description

Generates a plot of the Binomial distribution with user specified parameters.

Usage

visualize.binom(
  stat = 1,
  size = 3,
  prob = 0.5,
  section = "lower",
  strict = FALSE
)

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

size

size of sample.

prob

probability of picking object.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or =FALSE for equal to OR values =1 or =TRUE for strict. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Author(s)

James Balamuta

See Also

visualize.it() , dbinom().

Examples


# Evaluates lower tail with equal to inequality.
visualize.binom(stat = 1, size = 3, prob = 0.5, section = "lower", strict = FALSE) 

# Evaluates bounded region with lower bound equal to and upper bound strict inequality.
visualize.binom(stat = c(1,2), size = 5, prob = 0.35, section = "bounded", strict = c(0,1))

# Evaluates upper tail with strict inequality.
visualize.binom(stat = 1, size = 3, prob = 0.5, section = "upper", strict = TRUE)

Visualize Cauchy Distribution

Description

Generates a plot of the Cauchy distribution with user specified parameters.

Usage

visualize.cauchy(stat = 1, location = 2, scale = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

location

location parameter

scale

scale parameter

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dcauchy().

Examples


# Evaluates lower tail.
visualize.cauchy(stat = 1, location = 4, scale = 2, section = "lower") 

# Evaluates bounded region.
visualize.cauchy(stat = c(3,5), location = 5, scale = 3, section = "bounded") 

# Evaluates upper tail.
visualize.cauchy(stat = 1, location = 4, scale = 2, section = "upper")

Visualize Chi-squared Distribution

Description

Generates a plot of the Chi-squared distribution with user specified parameters.

Usage

visualize.chisq(stat = 1, df = 3, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

df

degrees of freedom of Chi-squared distribution.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dchisq().

Examples


# Evaluates lower tail.
visualize.chisq(stat = 1, df = 3, section = "lower")
# Evaluates bounded region.
visualize.chisq(stat = c(1,2), df = 6, section = "bounded")
# Evaluates upper tail.
visualize.chisq(stat = 1, df = 3, section = "upper")

Graphing function for Continuous Distributions.

Description

Handles how continuous distributions are graphed. Users should not use this function. Instead, users should use visualize.it().

Usage

visualize.continuous(dist, stat = c(0, 1), params, section = "lower")

Arguments

dist

contains a supported continuos distribution shortname.

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

params

A list that must contain the necessary parameters for each distribution. For example, params = list(mu = 1, sd = 1) would be for a normal distribution with mean 1 and standard deviation 1. If you are not aware of the parameters for the distribution, consider using the visualize.dist_name functions listed under the "See Also" section.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails"

Author(s)

James Balamuta

See Also

visualize.it(), visualize.beta(), visualize.chisq(), visualize.exp(), visualize.gamma(), visualize.norm(), visualize.unif(), visualize.cauchy(), visualize.f(), visualize.lnorm(), visualize.t(), visualize.wilcox(), visualize.logis().

Examples

# Function does not have dist look up, must go through visualize.it
visualize.it(dist='norm', stat = c(0,1), params = list(mu = 1, sd = 1), section = "bounded")

Graphing function for Discrete Distributions.

Description

Handles how discrete distributions are graphed. Users should not use this function. Instead, users should use link{visualize.it}.

Usage

visualize.discrete(dist, stat = c(0, 1), params, section = "lower", strict)

Arguments

dist

contains the distribution from link{visualize.distributions}.

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

params

A list that must contain the necessary parameters for each distribution. For example, params = list(n = 5, prob = .25) would be for a binomial distribution with size 5 and probability .75. If you are not aware of the parameters for the distribution, consider using the visualize.dist_name functions listed under the "See Also" section.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or =FALSE for equal to OR values =1 or =TRUE for strict. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Author(s)

James Balamuta

See Also

visualize.it(), visualize.binom(), visualize.geom(), visualize.hyper(), visualize.nbinom(), visualize.pois().

Examples


# Function does not have dist look up, must go through visualize.it
visualize.it(dist='geom', stat = c(2,4), params = list(prob = .75), section = "bounded",
          strict = c(0,1))

Visualize's Supported Distributions

Description

All of visualize's supported distributions with their density, probability, and quantile functions. In addition, mean and variance functions are present. Other descriptors also exist and are documented below.

Usage

visualize.distributions

Format

Distributions are loaded with the following format:

type: specify either "continuous" or "discrete"
to direct the query to the right graph handler.
name: specify the name of the distribution.
In example, "Poisson Distribution."
This is used in the main graph title.
variable: specify the variable in probability statement.
In example, P(z < 5).
This is used in the probability subtitle.
varsymbols: specify the variable symbols for distribution.
In example, mu = 1 sd = 2.
This is used in the distribution subtitle.
params: specify the amount of params required for distribution.
This is used in the first error handling check to ensure
the correct number of params is supplied.
init(params, ...): Function that generates the mean and variance
of a distribution.
density(x, params, ncp = 0, lower.tail = TRUE, log = FALSE, ...): Function that provides the density value using vectors of the
quantiles from the distribution.
This serves as a wrapper for ddistr_name.
probability(x, params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...) Function that provides the probability value
using vectors of quantiles from the distribution.
This serves as a wrapper for pdistr_name.
quantile(x, params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...) Function that provides the quantile value
using vectors of probabilities from the distribution.
This serves as a wrapper for qdistr_name.

The distributions currently available to use are:

Distribution r Name Distribution r Name
Beta beta Lognormal* lnorm
Binomial binom Negative Binomial nbinom
Cauchy* cauchy Normal norm
Chisquare chisq Poisson pois
Exponential exp Student t* t
F* f Uniform unif
Gamma gamma Geometric geom
Hypergeometric hyper Wilcoxon* wilcox
Logistic* logis

Author(s)

James Balamuta

Examples


visualize.distributions = list(
  'beta' = list(
    type = "continuous",
    name = "Beta Distribution",
    variable = "b",
    varsymbols = c("\u03B1","\u03B2"),
    params = 2,
    init  = function(params, ...) {
      shape1 = params[[1]]; shape2 = params[[2]]
      if(shape1 <= 0 || shape2 <= 0) stop("Error: Need alpha, beta  > 0")
      mean = shape1 / (shape1 + shape2)
      var = (shape1 * shape2)/((shape1 + shape2 + 1)*(shape1 + shape2)^2)
      c(mean, var)
    },
    density = function(x,params, ncp = 0, lower.tail = TRUE, log = FALSE, ...){
      if(params[[1]] <= 0 || params[[2]] <= 0) stop("Error: Need alpha, beta  > 0")
        dbeta(x,params[[1]], params[[2]], ncp = ncp, log = log)
    },
    probability = function(q,params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...){
      if(params[[1]] <= 0 || params[[2]] <= 0) stop("Error: Need alpha, beta  > 0")
      pbeta(q,params[[1]], params[[2]], ncp = ncp, lower.tail = lower.tail, log.p = log.p)
    },
    quantile = function(p,params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...){
      if(params[[1]] <= 0 || params[[2]] <= 0) stop("Error: Need alpha, beta  > 0")
      qbeta(p,params[[1]], params[[2]], ncp = ncp, lower.tail = lower.tail, log.p = log.p)
    }
  )
)

Visualize Exponential Distribution

Description

Generates a plot of the Exponential distribution with user specified parameters.

Usage

visualize.exp(stat = 1, theta = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

theta

vector of rates

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dexp().

Examples


# Evaluates lower tail.
visualize.exp(stat = .5, theta = 3, section = "lower")

# Evaluates bounded region.
visualize.exp(stat = c(1,2), theta = 3, section = "bounded")

# Evaluates upper tail.
visualize.exp(stat = .5, theta = 3, section = "upper")

Visualize F distribution

Description

Generates a plot of the F distribution with user specified parameters.

Usage

visualize.f(stat = 1, df1 = 5, df2 = 4, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

df1

First Degrees of Freedom

df2

Second Degrees of Freedom

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), df().

Examples


# Evaluates lower tail.
visualize.f(stat = 1, df1 = 5, df2 = 4, section = "lower") 

# Evaluates bounded region.
visualize.f(stat = c(3,5), df1 = 6, df2 = 3, section = "bounded") 

# Evaluates upper tail.
visualize.f(stat = 1, df1 = 5, df2 = 4, section = "upper")

Visualize Gamma Distribution

Description

Generates a plot of the Gamma distribution with user specified parameters.

Usage

visualize.gamma(stat = 1, alpha = 1, theta = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

alpha

alpha is considered to be shape by R's implementation of the gamma distribution. alpha must be greater than 0.

theta

theta is considered to be rate by R's implementation of the gamma distribution. theta must be greater than 0.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Author(s)

James Balamuta

See Also

visualize.it(), dgamma().

Examples


# Evaluate lower tail.
visualize.gamma(stat = 1, alpha = 3, theta = 1, section = "lower") 

# Evaluate bounded section.
visualize.gamma(stat = c(0.75,1), alpha = 3, theta = 1, section = "bounded") 

# Evaluate upper tail.
visualize.gamma(stat = 1, alpha = 3, theta = 1, section = "upper")

Visualize Geometric Distribution

Description

Generates a plot of the Geometric distribution with user specified parameters.

Usage

visualize.geom(stat = 1, prob = 0.3, section = "lower", strict = FALSE)

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

prob

probability of picking object.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or =FALSE for equal to OR values =1 or =TRUE for strict. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Author(s)

James Balamuta

See Also

visualize.it() , dgeom().

Examples


# Evaluates lower tail.
visualize.geom(stat = 1, prob = 0.5, section = "lower", strict = FALSE) 

# Evaluates bounded region.
visualize.geom(stat = c(1,3), prob = 0.35, section = "bounded", strict = c(0,1))

# Evaluates upper tail.
visualize.geom(stat = 1, prob = 0.5, section = "upper", strict = 1)

Visualize Hypergeometric Distribution

Description

Generates a plot of the Hypergeometric distribution with user specified parameters.

Usage

visualize.hyper(
  stat = 1,
  m = 5,
  n = 4,
  k = 2,
  section = "lower",
  strict = FALSE
)

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

m

m white balls. m must be greater than 0.

n

n black balls. n must be greater than 0.

k

draw k balls without replacement.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or =FALSE for equal to OR values =1 or =TRUE for strict. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Author(s)

James Balamuta

See Also

visualize.it() , dhyper().

Examples


# Evaluates lower tail.
visualize.hyper(stat = 1, m=4, n=5, k=3, section = "lower", strict = 0) 

# Evaluates bounded region.
visualize.hyper(stat = c(2,4), m=14, n=5, k=2, section = "bounded", strict = c(0,1))

# Evaluates upper tail.
visualize.hyper(stat = 1, m=4, n=5, k=3, section = "upper", strict = 1)

Visualize's Processing Function

Description

Acts as a director of traffic and first line of error handling regarding submitted visualization requests. This function should only be used by advanced users.

Usage

visualize.it(
  dist = "norm",
  stat = c(0, 1),
  params = list(mu = 0, sd = 1),
  section = "lower",
  strict = c(0, 1)
)

Arguments

dist

a string that should be contain a supported probability distributions name in R. Supported continuous distributions: "beta", "chisq", "exp", "gamma", "norm", and "unif". Supported discrete distributions: "binom", "geom", "hyper", "nbinom", and "pois".

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

params

A list that must contain the necessary parameters for each distribution. For example, params = list(mu = 1, sd = 1) would be for a normal distribution with mean 1 and standard deviation 1. If you are not aware of the parameters for the distribution, consider using the visualize.dist functions listed under the "See Also" section.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or =FALSE for strict OR values =1 or =TRUE for equal to. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

References

http://cran.r-project.org/web/views/Distributions.html

See Also

visualize.beta(), visualize.chisq(), visualize.exp(), visualize.gamma(), visualize.norm(), visualize.unif(), visualize.binom(), visualize.geom(), visualize.hyper(), visualize.nbinom(), visualize.pois().

Examples


# Defaults to lower tail evaluation
visualize.it(dist = 'norm', stat = 1, list(mu = 3 , sd = 2), section = "lower")

# Set to evaluate the upper tail.
visualize.it(dist = 'norm', stat = 1, list(mu=3,sd=2),section="upper")

# Set to shade inbetween a bounded region.
visualize.it(dist = 'norm', stat = c(-1,1), list(mu=0,sd=1), section="bounded")

# Gamma distribution evaluated at upper tail.
visualize.it(dist = 'gamma', stat = 2, params = list(alpha=2,beta=1),section="upper")

# Binomial distribution evaluated at lower tail.
visualize.it('binom', stat = 2, params = list(n=4,p=.5))

Visualize Log Normal Distribution

Description

Generates a plot of the Log Normal distribution with user specified parameters.

Usage

visualize.lnorm(stat = 1, meanlog = 3, sdlog = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

meanlog

Mean of the distribution

sdlog

Standard deviation of the distribution

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dlnorm().

Examples


# Evaluates lower tail.
visualize.lnorm(stat = 1, meanlog = 3, sdlog = 1, section = "lower") 

# Evaluates bounded region.
visualize.lnorm(stat = c(3,5), meanlog = 3, sdlog = 3, section = "bounded") 

# Evaluates upper tail.
visualize.lnorm(stat = 1, meanlog = 3, sdlog = 1, section = "upper")

Visualize Logistic distribution

Description

Generates a plot of the Logistic distribution with user specified parameters.

Usage

visualize.logis(stat = 1, location = 3, scale = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

location

Location of the distribution.

scale

Scale of the distribution.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dlogis().

Examples


# Evaluates lower tail.
visualize.logis(stat = 1, location = 4, scale = 2, section = "lower") 

# Evaluates bounded region.
visualize.logis(stat = c(3,5), location = 4, scale = 2, section = "bounded") 

# Evaluates upper tail.
visualize.logis(stat = 1, location = 4, scale = 2, section = "upper")

Visualize Negative Binomial Distribution

Description

Generates a plot of the Negative Binomial distribution with user specified parameters.

Usage

visualize.nbinom(
  stat = 1,
  size = 6,
  prob = 0.5,
  section = "lower",
  strict = FALSE
)

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

size

number of objects.

prob

probability of picking object.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or = FALSE for equal to OR values =1 or =TRUE for strict. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Author(s)

James Balamuta

See Also

visualize.it() , dnbinom().

Examples


# Evaluates lower tail.
visualize.nbinom(stat = 1, size = 5, prob = 0.5, section = "lower", strict = 0) 

# Evaluates bounded region.
visualize.nbinom(stat = c(1,3), size = 10, prob = 0.35, section = "bounded",
                    strict = c(TRUE, FALSE))

# Evaluates upper tail.
visualize.nbinom(stat = 1, size = 5, prob = 0.5, section = "upper", strict = 1)

Visualize Normal Distribution

Description

Generates a plot of the Normal distribution with user specified parameters.

Usage

visualize.norm(stat = 1, mu = 0, sd = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

mu

mean of the Normal Distribution.

sd

standard deviation of the Normal Distribution.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

See Also

visualize.it() , dnorm().

Examples


# Evaluates lower tail.
visualize.norm(stat = 1, mu = 4, sd = 5, section = "lower") 

# Evaluates bounded region.
visualize.norm(stat = c(3,6), mu = 5, sd = 3, section = "bounded")

# Evaluates upper tail.
visualize.norm(stat = 1, mu = 3, sd = 2, section = "upper")

Visualize Poisson Distribution

Description

Generates a plot of the Poisson distribution with user specified parameters.

Usage

visualize.pois(stat = 1, lambda = 3.5, section = "lower", strict = FALSE)

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

lambda

lambda value of the Poisson Distribution.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

strict

Determines whether the probability will be generated as a strict (<, >) or equal to (<=, >=) inequality. ⁠strict=⁠ requires either values = 0 or =FALSE for equal to OR values =1 or =TRUE for strict. For bounded condition use: strict=c(0,1) or strict=c(FALSE,TRUE).

Author(s)

James Balamuta

See Also

visualize.it() , dpois().

Examples


# Evaluates lower tail.
visualize.pois(stat = 1, lambda = 2, section = "lower", strict = FALSE) 

# Evaluates bounded region.
visualize.pois(stat = c(1,3), lambda = 3, section = "bounded", strict = c(0,1))

# Evaluates upper tail.
visualize.pois(stat = 1, lambda = 2, section = "upper", strict = 1)

Visualize Student's t distribution

Description

Generates a plot of the Student's t distribution with user specified parameters.

Usage

visualize.t(stat = 1, df = 3, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

df

Degrees of freedom

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dt().

Examples


# Evaluates lower tail.
visualize.t(stat = 1, df = 4, section = "lower") 

# Evaluates bounded region.
visualize.t(stat = c(3,5), df = 6, section = "bounded") 

# Evaluates upper tail.
visualize.t(stat = 1, df = 4, section = "upper")

Visualize Uniform Distribution

Description

Generates a plot of the Uniform distribution with user specified parameters.

Usage

visualize.unif(stat = 1, a = 0, b = 1, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

a

starting point. Note: a<b

b

end point. Note: b > a

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Author(s)

James Balamuta

See Also

visualize.it() , dunif().

Examples


# Evaluates lower tail.
visualize.unif(stat = 8.75, a = 7, b = 10, section = "lower") 

# Evaluates bounded region.
visualize.unif(stat = c(3,6), a = 1, b = 7, section = "bounded")

# Evaluates upper tail.
visualize.unif(stat = 2, a = 1, b = 5, section = "upper")

Visualize Cauchy Distribution

Description

Generates a plot of the Wilcoxon Rank Sum distribution with user specified parameters.

Usage

visualize.wilcox(stat = 1, m = 7, n = 3, section = "lower")

Arguments

stat

a statistic to obtain the probability from. When using the "bounded" condition, you must supply the parameter as stat = c(lower_bound, upper_bound). Otherwise, a simple stat = desired_point will suffice.

m

Sample size from group 1.

n

Sample size from group 2.

section

Select how you want the statistic(s) evaluated via ⁠section=⁠ either "lower","bounded", "upper", or"tails".

Value

Returns a plot of the distribution according to the conditions supplied.

Author(s)

James Balamuta

See Also

visualize.it(), dwilcox().

Examples


# Evaluates lower tail.
visualize.wilcox(stat = 1, m = 7, n = 3, section = "lower") 

# Evaluates bounded region.
visualize.wilcox(stat = c(2,3), m = 5, n = 4, section = "bounded") 

# Evaluates upper tail.
visualize.wilcox(stat = 1, m = 7, n = 3, section = "upper")