| Title: | Fit Univariate Distributions |
| Version: | 0.6.2 |
| Description: | Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages. |
| URL: | https://github.com/tomroh/fitur |
| BugReports: | https://github.com/tomroh/fitur/issues |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, fitdistrplus, actuar, e1071, ggplot2, goftest, shiny (≥ 0.13), miniUI (≥ 0.1.1), rstudioapi (≥ 0.5), DT |
| Suggests: | knitr, rmarkdown |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-10-06 16:07:23 UTC; troh |
| Author: | Thomas Roh [aut, cre] |
| Maintainer: | Thomas Roh <thomas@roh.engineering> |
| Repository: | CRAN |
| Date/Publication: | 2021-10-06 16:30:02 UTC |
The Discrete Uniform Distribution
Description
The Discrete Uniform Distribution
Usage
ddunif(x, min = 0, max = 1)
pdunif(q, min = 0, max = 1)
qdunif(p, min = 0, max = 1)
rdunif(n, min = 0L, max = 1)
Arguments
x |
vector of (non-negative integer) quantiles |
min |
minimum value of distribution (integer) |
max |
maximum value of distribution (integer) |
q |
vector of quantiles |
p |
vector of probabilities |
n |
number of random values to return |
Value
ddunif gives the density, pdunif gives the distribution function, qdunif gives the quantile function, rdunif generates random deviates
Examples
ddunif(0:1)
pdunif(1)
qdunif(.5)
rdunif(10)
Wrappers to compute goodness of fit test froms distfun objects
Description
Wrappers to compute goodness of fit test froms distfun objects
Usage
ks_test(distfun, x, ...)
## S3 method for class 'distfun'
ad_test(distfun, x)
ad_test(distfun, x)
## S3 method for class 'distfun'
cvm_test(distfun, x)
cvm_test(distfun, x)
Arguments
distfun |
a distfun object |
x |
numeric vector |
... |
arguments to be passed on to test function |
Value
goodness of fit object
Examples
x <- rgamma(100, 1, 1)
fit <- fit_univariate(x, 'gamma')
ks_test(fit, x)
ad_test(fit, x)
cvm_test(fit, x)
Find Mode
Description
Find Mode
Usage
Mode(x)
Arguments
x |
vector of data |
Value
mode of data
Build Distribution Functions
Description
A wrapper for building function families given a numeric vector and the distribution
Usage
build_dist(x, distribution)
Arguments
x |
numeric vector |
distribution |
distribution character name |
Value
list of distribution functions for d, p, q, r, and parameters
Examples
fittedDists <- build_dist(rpois(100,5), 'pois')
dpois(x = 5, lambda = 5)
fittedDists$dpois(5)
ppois(5, 5)
fittedDists$ppois(5)
qpois(.5, 5)
fittedDists$qpois(.5)
set.seed(8257)
rpois(100, 5)
set.seed(8257)
fittedDists$rpois(100)
fittedDists$parameters
Calculate moments of a numeric vector
Description
Calculate moments of a numeric vector
Usage
calc_moments(x)
Arguments
x |
a numeric vector |
Value
a named vector of descriptive statistics
Examples
x <- rexp(1000, 2)
calc_moments(x)
Fit Univariate Distributions Addin
Description
Interactively submit a numeric vector and choose what distributions that you want to run fit diagnostics. Click done to have the desired distribution code put into your cursor position.
Usage
fit_dist_addin()
Fit Empirical Distribution
Description
Fit Empirical Distribution
Usage
fit_empirical(x)
Arguments
x |
integer or double vector |
Value
if integer vector then list of family functions for d, p, q, r, and parameters based on each integer value. if it is a double vector then list of family functions for d, p, q, r, and parameters based on Freedman-Diaconis rule for optimal number of histogram bins.
Examples
set.seed(562)
x <- rpois(100, 5)
empDis <- fit_empirical(x)
# probability density function
plot(empDis$dempDis(0:10),
xlab = 'x',
ylab = 'dempDis')
# cumulative distribution function
plot(x = 0:10,
y = empDis$pempDis(0:10),
#type = 'l',
xlab = 'x',
ylab = 'pempDis')
# quantile function
plot(x = seq(.1, 1, .1),
y = empDis$qempDis(seq(.1, 1, .1)),
type = 'p',
xlab = 'x',
ylab = 'qempDis')
# random sample from fitted distribution
summary(empDis$r(100))
empDis$parameters
set.seed(562)
x <- rexp(100, 1/5)
empCont <- fit_empirical(x)
# probability density function
plot(x = 0:10,
y = empCont$dempCont(0:10),
xlab = 'x',
ylab = 'dempCont')
# cumulative distribution function
plot(x = 0:10,
y = empCont$pempCont(0:10),
#type = 'l',
xlab = 'x',
ylab = 'pempCont')
# quantile function
plot(x = seq(.5, 1, .1),
y = empCont$qempCont(seq(.5, 1, .1)),
type = 'p',
xlab = 'x',
ylab = 'qempCont')
# random sample from fitted distribution
summary(empCont$r(100))
empCont$parameters
Fit Univariate Distribution
Description
Fit Univariate Distribution
Usage
fit_univariate(x, distribution, type = "continuous")
Arguments
x |
numeric vector |
distribution |
character name of distribution |
type |
discrete or continuous data |
Value
a fitted list object of d, p, q, r distribution functions and parameters, MLE for probability distributions, custom fit for empirical
Examples
# Fit Discrete Distribution
set.seed(42)
x <- rpois(1000, 3)
fitted <- fit_univariate(x, 'pois', type = 'discrete')
# density function
plot(fitted$dpois(x=0:10),
xlab = 'x',
ylab = 'dpois')
# distribution function
plot(fitted$ppois(seq(0, 10, 1)),
xlab= 'x',
ylab = 'ppois')
# quantile function
plot(fitted$qpois,
xlab= 'x',
ylab = 'qpois')
# sample from theoretical distribution
summary(fitted$rpois(100))
# estimated parameters from MLE
fitted$parameters
set.seed(24)
x <- rweibull(1000, shape = .5, scale = 2)
fitted <- fit_univariate(x, 'weibull')
# density function
plot(fitted$dweibull,
xlab = 'x',
ylab = 'dweibull')
# distribution function
plot(fitted$pweibull,
xlab = 'x',
ylab = 'pweibull')
# quantile function
plot(fitted$qweibull,
xlab = 'x',
ylab = 'qweibull')
# sample from theoretical distribution
summary(fitted$rweibull(100))
# estimated parameters from MLE
fitted$parameters
Fit Univariate Distributions by Specifying Parameters
Description
Fit Univariate Distributions by Specifying Parameters
Usage
fit_univariate_man(distribution, parameters)
Arguments
distribution |
distribution character name |
parameters |
named vector of parameters to set |
Value
list of distribution functions for d, p, q, r, and parameters
Examples
manFun <- fit_univariate_man('norm', c(mean = 2, sd = 5))
set.seed(5)
m1 <- mean(manFun$rnorm(100000))
set.seed(5)
m2 <- mean(rnorm(100000, 2, 5))
identical(m1, m2)
Generate Single Distribution Function
Description
Generate Single Distribution Function
Usage
gen_dist_fun(f, parameters, ...)
Arguments
f |
one of distribution functions |
parameters |
new parameters for distribution |
... |
arguments to pass on to distribution function |
Value
one of parameterized distribution functions in d, p, q, r
Goodness of Fit Testing
Description
Apply all goodness of fit tests and return a data.frame with the results
Usage
gof_tests(fits, x)
Arguments
fits |
a list object produced from fit_univariate, fit_empirical, or fit_univariate_man |
x |
numeric vector of sample data |
Value
a data.frame of test statistic results for each distribution
Examples
set.seed(84)
x <- rgamma(100, 1, 1)
dists <- c('gamma', 'lnorm', 'weibull')
multipleFits <- lapply(dists, fit_univariate, x = x)
gof_tests(multipleFits, x)
Test if object is a distfun object
Description
Test if object is a distfun object
Usage
is.distfun(x)
Arguments
x |
an R object to be tested |
Value
TRUE if x is a disfun object, FALSE otherwise
Density Comparison Plot
Description
Density Comparison Plot
Usage
plot_density(x, fits, nbins)
Arguments
x |
numeric vector of sample data |
fits |
a list object produced from fit_univariate, fit_empirical, or fit_univariate_man |
nbins |
number of bins for histogram |
Value
ggplot of empirical histogram of x compared to theoretical density distributions
Examples
library(ggplot2)
set.seed(37)
x <- rgamma(10000, 5)
dists <- c('gamma', 'lnorm', 'weibull')
fits <- lapply(dists, fit_univariate, x = x)
plot_density(x, fits, 30) +
theme_bw()
P-P Plot
Description
P-P Plot
Usage
plot_pp(x, fits)
Arguments
x |
numeric vector of sample data |
fits |
a list object produced from fit_univariate, fit_empirical, or fit_univariate_man |
Value
ggplot of percentile-percentile comparison of theoretical distribution
Examples
library(ggplot2)
set.seed(37)
x <- rgamma(10000, 5)
dists <- c('gamma', 'lnorm', 'weibull')
fits <- lapply(dists, fit_univariate, x = x)
plot_pp(x, fits) +
theme_bw()
Q-Q Plot
Description
Q-Q Plot
Usage
plot_qq(x, fits)
Arguments
x |
numeric vector of sample data |
fits |
a list object produced from fit_univariate, fit_empirical, or fit_univariate_man |
Value
ggplot of quantile-quantile comparison of theoretical distribution
Examples
library(ggplot2)
set.seed(37)
x <- rgamma(10000, 5)
dists <- c('gamma', 'lnorm', 'weibull')
fits <- lapply(dists, fit_univariate, x = x)
plot_qq(x, fits) +
theme_bw()