Random Number Generation
Usage
ssd_rburrIII3(n, shape1 = 1, shape2 = 1, scale = 1, chk = TRUE)
ssd_rgamma(n, shape = 1, scale = 1, chk = TRUE)
ssd_rgompertz(n, location = 1, shape = 1, chk = TRUE)
ssd_rinvpareto(n, shape = 3, scale = 1, chk = TRUE)
ssd_rlgumbel(n, locationlog = 0, scalelog = 1, chk = TRUE)
ssd_rllogis_llogis(
n,
locationlog1 = 0,
scalelog1 = 1,
locationlog2 = 1,
scalelog2 = 1,
pmix = 0.5,
chk = TRUE
)
ssd_rllogis(n, locationlog = 0, scalelog = 1, chk = TRUE)
ssd_rlnorm_lnorm(
n,
meanlog1 = 0,
sdlog1 = 1,
meanlog2 = 1,
sdlog2 = 1,
pmix = 0.5,
chk = TRUE
)
ssd_rlnorm(n, meanlog = 0, sdlog = 1, chk = TRUE)
ssd_rmulti(
n,
burrIII3.weight = 0,
burrIII3.shape1 = 1,
burrIII3.shape2 = 1,
burrIII3.scale = 1,
gamma.weight = 0,
gamma.shape = 1,
gamma.scale = 1,
gompertz.weight = 0,
gompertz.location = 1,
gompertz.shape = 1,
invpareto.weight = 0,
invpareto.shape = 3,
invpareto.scale = 1,
lgumbel.weight = 0,
lgumbel.locationlog = 0,
lgumbel.scalelog = 1,
llogis.weight = 0,
llogis.locationlog = 0,
llogis.scalelog = 1,
llogis_llogis.weight = 0,
llogis_llogis.locationlog1 = 0,
llogis_llogis.scalelog1 = 1,
llogis_llogis.locationlog2 = 1,
llogis_llogis.scalelog2 = 1,
llogis_llogis.pmix = 0.5,
lnorm.weight = 0,
lnorm.meanlog = 0,
lnorm.sdlog = 1,
lnorm_lnorm.weight = 0,
lnorm_lnorm.meanlog1 = 0,
lnorm_lnorm.sdlog1 = 1,
lnorm_lnorm.meanlog2 = 1,
lnorm_lnorm.sdlog2 = 1,
lnorm_lnorm.pmix = 0.5,
weibull.weight = 0,
weibull.shape = 1,
weibull.scale = 1,
chk = TRUE
)
ssd_rmulti_fitdists(n, fitdists, chk = TRUE)
ssd_rweibull(n, shape = 1, scale = 1, chk = TRUE)
Arguments
- n
positive number of observations.
- shape1
shape1 parameter.
- shape2
shape2 parameter.
- scale
scale parameter.
- chk
A flag specifying whether to check the arguments.
- shape
shape parameter.
- location
location parameter.
- locationlog
location on the log scale parameter.
- scalelog
scale on log scale parameter.
- locationlog1
locationlog1 parameter.
- scalelog1
scalelog1 parameter.
- locationlog2
locationlog2 parameter.
- scalelog2
scalelog2 parameter.
- pmix
Proportion mixture parameter.
- meanlog1
mean on log scale parameter.
- sdlog1
standard deviation on log scale parameter.
- meanlog2
mean on log scale parameter.
- sdlog2
standard deviation on log scale parameter.
- meanlog
mean on log scale parameter.
- sdlog
standard deviation on log scale parameter.
- burrIII3.weight
weight parameter for the Burr III distribution.
- burrIII3.shape1
shape1 parameter for the Burr III distribution.
- burrIII3.shape2
shape2 parameter for the Burr III distribution.
- burrIII3.scale
scale parameter for the Burr III distribution.
- gamma.weight
weight parameter for the gamma distribution.
- gamma.shape
shape parameter for the gamma distribution.
- gamma.scale
scale parameter for the gamma distribution.
- gompertz.weight
weight parameter for the Gompertz distribution.
- gompertz.location
location parameter for the Gompertz distribution.
- gompertz.shape
shape parameter for the Gompertz distribution.
- invpareto.weight
weight parameter for the inverse Pareto distribution.
- invpareto.shape
shape parameter for the inverse Pareto distribution.
- invpareto.scale
scale parameter for the inverse Pareto distribution.
- lgumbel.weight
weight parameter for the log-Gumbel distribution.
- lgumbel.locationlog
location parameter for the log-Gumbel distribution.
- lgumbel.scalelog
scale parameter for the log-Gumbel distribution.
- llogis.weight
weight parameter for the log-logistic distribution.
- llogis.locationlog
location parameter for the log-logistic distribution.
- llogis.scalelog
scale parameter for the log-logistic distribution.
- llogis_llogis.weight
weight parameter for the log-logistic log-logistic mixture distribution.
- llogis_llogis.locationlog1
locationlog1 parameter for the log-logistic log-logistic mixture distribution.
- llogis_llogis.scalelog1
scalelog1 parameter for the log-logistic log-logistic mixture distribution.
- llogis_llogis.locationlog2
locationlog2 parameter for the log-logistic log-logistic mixture distribution.
- llogis_llogis.scalelog2
scalelog2 parameter for the log-logistic log-logistic mixture distribution.
- llogis_llogis.pmix
pmix parameter for the log-logistic log-logistic mixture distribution.
- lnorm.weight
weight parameter for the log-normal distribution.
- lnorm.meanlog
meanlog parameter for the log-normal distribution.
- lnorm.sdlog
sdlog parameter for the log-normal distribution.
- lnorm_lnorm.weight
weight parameter for the log-normal log-normal mixture distribution.
- lnorm_lnorm.meanlog1
meanlog1 parameter for the log-normal log-normal mixture distribution.
- lnorm_lnorm.sdlog1
sdlog1 parameter for the log-normal log-normal mixture distribution.
- lnorm_lnorm.meanlog2
meanlog2 parameter for the log-normal log-normal mixture distribution.
- lnorm_lnorm.sdlog2
sdlog2 parameter for the log-normal log-normal mixture distribution.
- lnorm_lnorm.pmix
pmix parameter for the log-normal log-normal mixture distribution.
- weibull.weight
weight parameter for the Weibull distribution.
- weibull.shape
shape parameter for the Weibull distribution.
- weibull.scale
scale parameter for the Weibull distribution.
- fitdists
An object of class fitdists.
Functions
ssd_rburrIII3()
: Random Generation for BurrIII Distributionssd_rgamma()
: Random Generation for Gamma Distributionssd_rgompertz()
: Random Generation for Gompertz Distributionssd_rinvpareto()
: Random Generation for Inverse Pareto Distributionssd_rlgumbel()
: Random Generation for log-Gumbel Distributionssd_rllogis_llogis()
: Random Generation for Log-Logistic/Log-Logistic Mixture Distributionssd_rllogis()
: Random Generation for Log-Logistic Distributionssd_rlnorm_lnorm()
: Random Generation for Log-Normal/Log-Normal Mixture Distributionssd_rlnorm()
: Random Generation for Log-Normal Distributionssd_rmulti()
: Random Generation for Multiple Distributionsssd_rmulti_fitdists()
: Random Generation for Multiple Distributionsssd_rweibull()
: Random Generation for Weibull Distribution
Examples
set.seed(50)
hist(ssd_rburrIII3(10000), breaks = 1000)
set.seed(50)
hist(ssd_rgamma(10000), breaks = 1000)
set.seed(50)
hist(ssd_rgompertz(10000), breaks = 1000)
set.seed(50)
hist(ssd_rinvpareto(10000), breaks = 1000)
set.seed(50)
hist(ssd_rlgumbel(10000), breaks = 1000)
set.seed(50)
hist(ssd_rllogis_llogis(10000), breaks = 1000)
set.seed(50)
hist(ssd_rllogis(10000), breaks = 1000)
set.seed(50)
hist(ssd_rlnorm_lnorm(10000), breaks = 1000)
set.seed(50)
hist(ssd_rlnorm(10000), breaks = 1000)
# multi
set.seed(50)
hist(ssd_rmulti(1000, gamma.weight = 0.5, lnorm.weight = 0.5), breaks = 100)
# multi fitdists
fit <- ssd_fit_dists(ssddata::ccme_boron)
ssd_rmulti_fitdists(2, fit)
#> [1] 17.30462 16.36277
set.seed(50)
hist(ssd_rweibull(10000), breaks = 1000)