Cumulative distribution function of Expomemtial-based model

Description

A cumulative distribution function(CDF) of Exponential-based model. To access parameters and coefficients in an object emodel of a class expmodel, use emodel$result[k, "lmd1"], emodel$coeffs[[k]] for some index k. This index appears in the leftmost column of estimation table generated by summary(emodel).

Usage

cdf_expmodel(coeff, lmd, x)

Arguments

Value

A numeric vector of CDF of an exponential-based model.

See Also

expmodel() summary.expmodel() estimate.expmodel() func.expmodel() cdf_expmodel()

Examples

## Create an object of `expmodel`
emodel <- expmodel(mixexpgamma$n200)
## Estimate with degree 4 and rate parameter 2.0
emodel <- estimate(emodel, 4, 2.0)
## Input vector
x <- seq(0, 12, 0.1)
## Output of PDF in above estimation
yv <- cdf_expmodel(emodel$coeffs[[1]], emodel$result[1, "lmd1"], x)

Cumulative distribution function of Gaussian-based model

Description

A cumulative distribution function(CDF) of Gaussian-based model. To access parameters and coefficients in an object gmodel of a class gaussmodel, use gmodel$result[k, "mu1"], gmodel$result[k, "sig1"], gmodel$coeffs[[k]] for some index k. This index appears in the leftmost column of estimation table generated by summary(gmodel).

Usage

cdf_gaussmodel(coeff, mu, sig, x)

Arguments

Value

A numeric vector of CDF of Gaussian-based model.

See Also

gaussmodel() summary.gaussmodel() estimate.gaussmodel() func.gaussmodel() pdf_gaussmodel()

Examples

## Create an object of `gaussmodel`
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate with a degree 6, a mean 0, and standard deviations 0.5
gmodel <- estimate(gmodel, 6, 0, 0.5)
## Input vector
x <- seq(-3, 3, 0.1)
## Output of PDF in above estimation
yv <- cdf_gaussmodel(gmodel$coeffs[[1]], gmodel$result[1, "mu1"],
 gmodel$result[1, "sig1"], x)

Reduce a data set to representatives of bins and their frequencies

Description

Reduce a data set to a named list of 'values' and 'freq', which are representatives of bins and their frequencies, respectively.

Usage

databinning(data, bins = 40)

Arguments

Value

A named list of values and freq whose length is bins.

See Also

base::rle()

Examples

rlst <- databinning(mix2gauss$n200)

Compute the mean and the standard deviation of a data set

Description

Compute the mean and the standard deviation of a data set represented by the pair of the numeric vectors data and optionally its frequency vector freq.

Usage

datastats(data, freq = NULL)

Arguments

Value

The mean and the standard deviation of a data set.

See Also

histmean()

Examples

## Without a frequency data
datastats(mix2gauss$n200)
## With a frequency data
datastats(mix2gaussHist$n200p, mix2gaussHist$n200f)

dsdp: Density Estimation using Semidefinite Programming

Description

Density estimation with Semidefinite Programming. The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, it computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficients of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See vignettes for tutorials and more information.

Generic Method for estimation

Description

This is a generic S3 method for estimation.

Usage

estimate(model, ...)

Arguments

Value

An instance of a model with estimated data.

Estimate Exponential-based model expmodel

Description

Estimates Exponential-based model expmodel among parameter vectors, deglist, lmdlist. Then it sorts the results by AIC.

Usage

## S3 method for class 'expmodel'
estimate(
  model,
  deglist = deglist,
  lmdlist = lmdlist,
  recompute = FALSE,
  stepsize = NULL,
  verbose = FALSE,
  ...
)

Arguments

Value

A expmodel object including the estimates. Those estimates are stored in model$result with data.frame format and model$coeffs in list format.

See Also

expmodel() summary.expmodel() plot.expmodel()

Examples

## Create an expmodel object
emodel <- expmodel(mixexpgamma$n200)
## Estimate a model with parameters
emodel <- estimate(emodel, deglist=c(4,5), lmdlist=c(0.5, 1, 2))

Estimate Gaussian-based model gaussmodel

Description

Estimates Gaussian-based model gaussmodel among parameter vectors, deglist, mulist, sdlist. Then it sorts the results by AIC.

Usage

## S3 method for class 'gaussmodel'
estimate(
  model,
  deglist = deglist,
  mulist = mulist,
  sdlist = sdlist,
  scaling = FALSE,
  recompute = FALSE,
  stepsize = NULL,
  verbose = FALSE,
  ...
)

Arguments

Value

A gaussmodel object including the estimates. Those estimates are stored in model$result with data.frame format and model$coeffs in list format.

See Also

gaussmodel() summary.gaussmodel() plot.gaussmodel()

Examples

## Create an `gaussmodel` object
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate a model with parameters
gmodel <- estimate(gmodel, deglist=c(2, 4), mulist=c(0.0, 0.2),
             sdlist=c(0.75, 1.0))

Evaluate a polynomial

Description

Evaluate the polynomial whose coefficients are represented in coeff vector. The order of the coefficient is an increasing order, i.e., coeff[1] is a constant term, and coeff[2] is a coefficient of 1st degree term, etc. Evaluation is done using Horner's method.

Usage

eval_poly(coeff, x)

Arguments

Value

A vector of values of a polynomial whose coefficient is coeff.

See Also

pdf_gaussmodel() pdf_expmodel() cdf_gaussmodel() cdf_expmodel()

Examples

## Evaluate a polynomial x^2 - 2x + 2 with x = 1, 2, 3.
## 0th, 1st, 2nd degree of coefficients
coeff <- c(2, -2, 1)
x <- c(1, 2, 3)
eval_poly(coeff, x)

Estimate coefficients of a polynomial in Exponential-based Model

Description

Estimate coefficients of a polynomial in Exponential-based model:

\mathrm{poly}(x; \alpha) \mathrm{Exp}(x; \lambda)

, where \alpha is a coefficient vector, \lambda is a rate parameter of an exponential distribution:

\mathrm{Exp}(x; \lambda) := \lambda e^{-\lambda x}

.

Using data and optionally its frequencies freq, and a degree of a polynomial, a rate parameter lmd of an exponential distribution, it computes the coefficients of polynomial, along with Akaike Information Criterion(AIC) and an accuracy information from underlying SDP solver. In general, the smaller the AIC is, the better the model is. An accuracy around 1e-7 is a good indication for a computational result of coefficients estimation.

Usage

exp_est(deg, lmd, data, freq, verbose, stepvec)

Arguments

Value

A list of ⁠deg, lmd, aic, accuracy, coefficient vector⁠

See Also

estimate.expmodel()

Examples

rlst <- exp_est(3, 1.0, mixexpgamma$n200, NULL, FALSE, c(0.7, 0.4))

Constructor for S3 class expmodel

Description

This function is a constructor for S3 class expmodel, which represents Exponential-based model. It usually takes data and optionally freq as arguments and also optionally stepsize Members of interest in practice are result and coeffs, which maintain the information of estimates and coefficients of polynomials, respectively.

Usage

expmodel(data = data, freq = NULL, stepsize = c(0.5, 0.3))

Arguments

Value

An object of Exponential-based model expmodel.

See Also

summary.expmodel() plot.expmodel() estimate.expmodel()

Examples

## Create `expmodel` object from a data set `mixexpgamma$n200`.
 emodel <- expmodel(mixexpgamma$n200)
## Create `expmodel` object from a data set `mixExpGammaHist$n800p` and
## its frequencies `mixExpGammaHist$n800f`.
 emodel <- expmodel(mixExpGammaHist$n800p, mixExpGammaHist$n800f)

Generic Method for evaluate the estimate

Description

This is a generic S3 method for estimate.

Usage

func(model, x, ...)

Arguments

Value

An evaluation of x with model.

Return the evaluation of a vector with Exponential-based model

Description

Evaluate an input vector x with Exponential-based model and return its vector. By default, it evaluate with the best model and its density, but it can designate the model by index and also can evaluate with a cumulative distribution.

Usage

## S3 method for class 'expmodel'
func(model, x, cdf = FALSE, n = 1, ...)

Arguments

Value

A numeric vector of the evaluatio of input vector x with a model.

See Also

expmodel() summary.expmodel() plot.expmodel() estimate.expmodel() pdf_expmodel() cdf_expmodel()

Examples

## Create an `expmodel` object
emodel <- expmodel(mixexpgamma$n200)
## Estimate an model with parameters
emodel <- estimate(emodel, deglist=5, lmdlist=3.75)
## A vector for input
x <- seq(0, 14, by=0.1)
## Density function
y <- func(emodel, x)
## Cumulative distribution
y <- func(emodel, x, cdf=TRUE)

Return the evaluation of a vector with Gaussian-based model

Description

Evaluate an input vector x with Gaussian-based model and return its vector. By default, it evaluate with the best model and its density, but it can designate the model by index and also can evaluate with a cumulative distribution.

Usage

## S3 method for class 'gaussmodel'
func(model, x, cdf = FALSE, n = 1, scaling = FALSE, ...)

Arguments

Value

A numeric vector of the evaluatio of input vector x with a model.

See Also

gaussmodel() summary.gaussmodel() plot.gaussmodel() estimate.gaussmodel() pdf_gaussmodel() cdf_gaussmodel()

Examples

## Create an `gaussmodel` object
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate an model with parameters
gmodel <- estimate(gmodel, deglist=4, mulist=0.15, sdlist=0.73)
## A vector for input
x <- seq(-4, 4, by=0.1)
## Density function
y <- func(gmodel, x)
## Cumulative distribution
y <- func(gmodel, x, cdf=TRUE)

Estimate coefficients of a polynomial in Gaussian-based model

Description

Estimate coefficients of a polynomial in Gaussian-based model:

\mathrm{poly}(x, \alpha) N(x; \mu, \sigma^2)

, where \alpha is a coefficient vector, \mu and \sigma are a mean and a standard deviation of Gaussian distribution:

N(x; \mu, \sigma^2) :=\frac{1}{\sigma \sqrt{2\pi}} \exp\left(-\frac{(x-\mu)^2}{2\sigma^2}\right)

Using data and optionally its frequencies freq, and a degree of a polynomial, a mean mu and a standard deviation sig of Gausian distribution, it computes the coefficients of a polynomial, along with Akaike Information Criterion(AIC) and an accuracy information from an underlying SDP solver. In general, the smaller the AIC is, the better the model is. An accuracy around 1e-7 is a good indication for a computational result of coefficients estimation.

Usage

gauss_est(deg, mu, sig, data, freq, verbose, stepsize)

Arguments

Value

A list of deg, mu, sig, aic, accuracy, ⁠coefficient vector⁠.

See Also

estimate.gaussmodel()

Examples

rlst <- gauss_est(4, 0, 1, mix2gauss$n200, NULL, FALSE, c(0.7, 0.4))

Constructor for S3 class gaussmodel

Description

This function is a constructor for S3 class gaussmodel, which represents Gaussian-based model. It usually takes data and optionally freq as arguments and also optionally stepsize. Members of interest in practice are result and coeffs, which maintain the information of estimates and coefficients of polynomials, respectively.

Usage

gaussmodel(data = data, freq = NULL, stepsize = c(0.5, 0.3))

Arguments

Value

An object of Gaussian-based model gaussmodel.

See Also

summary.gaussmodel() plot.gaussmodel() estimate.gaussmodel()

Examples

## Create `gaussmodel` object from a data set `mix2gauss$n200`.
 gmodel <- gaussmodel(mix2gauss$n200)
## Create `gaussmodel` object from a data set `mix2gaussHist$n200p` and
## its frequencies `mix2gaussHist$n200f`.
 gmodel <- gaussmodel(mix2gaussHist$n200p, mix2gaussHist$n200f)

Compute the mean of a data set

Description

Compute the mean of a data set represented by the pair of the numeric vectors data and optionally its frequency vector freq.

Usage

histmean(data, freq = NULL)

Arguments

Value

The mean of a data set.

See Also

datastats()

Examples

## Without a frequency data
histmean(mix2gauss$n200)
## With a frequency data
histmean(mix2gaussHist$n200p, mix2gaussHist$n200f)

Incomplete Gamma Function

Description

Evaluate an incomplete gamma function:

\gamma(a, x) = \int_{0}^{x} t^{a-1} e^{-t}dt,

using SLATEC dgami in https://netlib.org/slatec/. When (x > 0 and a \ge 0) or (x \ge 0 and a > 0), compute the result, otherwise the value is NaN.

Usage

igamma(a, x)

Arguments

Value

A vector of values of an incomplete gamma function.

See Also

igammac()

Examples

igamma(1, 1)

Complementary Incomplete Gamma Function

Description

Evaluate an complementary incomplete gamma function:

\gamma^{*}(a, x) = \int_{x}^{\infty} t^{a-1} e^{-t}dt,

using SLATEC dgamic in https://netlib.org/slatec/. When (x > 0 and a \ge 0) or (x \ge 0 and a > 0), compute the result, otherwise the value is NaN.

Usage

igammac(a, x)

Arguments

Value

A vector of values of a complementary incomplete gamma function.

See Also

igamma()

Examples

igammac(1, 1)

Datasets of Mixture of 2 Gaussian Distributions

Description

Dataset generated by mixture of 2 Gaussian Distributions whose density is:

\frac{0.3}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x+1)^2}{2 \cdot 0.5^2}\right) + \frac{0.7}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x-1)^2}{2 \cdot 0.5^2}\right)

.

Usage

mix2gauss

Format

A list of numeric vectors of Bimodal Gaussian Mixture Model.

n200

A numeric vector with 200 elements.

n400

A numeric vector with 400 elements.

n600

A numeric vector with 600 elements.

n800

A numeric vector with 800 elements.

n1000

A numeric vector with 1000 elements.

n1200

A numeric vector with 1200 elements.

Source

mix2gauss_gen()

Dataset of Mixture of 2 Gaussian Distributions: Histogram version

Description

Dataset of Mixture of 2 Gaussian Distributions, histogram version, whose density is:

\frac{0.3}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x+1)^2}{2 \cdot 0.5^2}\right) + \frac{0.7}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x-1)^2}{2 \cdot 0.5^2}\right)

.

Usage

mix2gaussHist

Format

A list of numeric vectors of Bimodal Gaussian Mixture Model.

n200

A numeric vector with 200 elements.

n200p

Histogram sample data with 25 bins.

n200f

Histogram frequency data with 25 bins.

n400

A numeric vector with 400 elements.

n400p

Histogram sample data with 50 bins.

n400f

Histogram frequency data with 50 bins.

n800

A numeric vector with 800 elements.

n800p

Histogram sample data with 100 bins.

n800f

Histogram frequency data with 100 bins.

Source

mix2gauss_gen() databinning()

A density function of mixed Gaussian distributions

Description

A density function of mixed Gaussian distributions whose density is:

\frac{0.3}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x+1)^2}{2 \cdot 0.5^2}\right) + \frac{0.7}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x-1)^2}{2 \cdot 0.5^2}\right)

.

Usage

mix2gauss_fun(x)

Arguments

Value

A numeric vector of probabilities for a given argument x.

See Also

mix2gauss_gen()

Generate mixed Gaussian random numbers

Description

Generate mix gaussian random numbers whose density is:

\frac{0.3}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x+1)^2}{2 \cdot 0.5^2}\right) + \frac{0.7}{\sqrt{2 \pi 0.5^2}} \exp\left(\frac{(x-1)^2}{2 \cdot 0.5^2}\right)

.

Usage

mix2gauss_gen(n = 100, seed = NULL)

Arguments

Value

A numeric vector of random numbers whose a density is described in Description.

See Also

mix2gauss_fun()

Datasets of Mixture of 3 Gaussian Distributions

Description

Datasets generated by Mixture of 3 Gaussian Distributions whose density is proportional to:

\exp(\frac{x^2}{2}) + 5\exp(\frac{(x-1)^2}{0.2}) + 3\exp(\frac{(x-1)^2}{0.5}).

Usage

mix3gauss

Format

A list of numeric vectors of Unimodal Gaussian Asymmetric Mixture Model.

n200

A numeric vector with 200 elements.

n400

A numeric vector with 400 elements.

n600

A numeric vector with 600 elements.

n800

A numeric vector with 800 elements.

n1000

A numeric vector with 1000 elements.

n1200

A numeric vector with 1200 elements.

Source

mix3gauss_gen()

A density function of mixed gaussian distribution

Description

A density function proportional to:

\exp(\frac{x^2}{2}) + 5\exp(\frac{(x-1)^2}{0.2}) + 3\exp(\frac{(x-1)^2}{0.5}).

Usage

mix3gauss_fun(x)

Arguments

Value

A numeric vector of probabilities for a given argument x.

See Also

mix3gauss_gen()

Generate Mixed Gaussian Random Numbers

Description

A random number generator whose density function is proportional to:

\exp(\frac{x^2}{2}) + 5\exp(\frac{(x-1)^2}{0.2}) + 3\exp(\frac{(x-1)^2}{0.5}).

Usage

mix3gauss_gen(n = 100, seed = NULL)

Arguments

Value

A numeric vector of probabilities for a given argument x.

See Also

mix3gauss_fun()

Dataset of Mixture of Exponential Distribution and Gamma Distribution: Histogram Version

Description

Dataset of mixture of exponential distribution and gamma distribution, histogram version, whose density is:

0.2(2 e^{-2x}) + 0.8 \frac{x^3}{3!}e^{-x}.

Usage

mixExpGammaHist

Format

A list of numeric vectors of Mixture of Exponential and Gamma distribution Model.

n200

A numeric vector with 200 elements.

n200p

Histogram sample data with 25 bins.

n200f

Histogram frequency data with 25 bins.

n400

A numeric vector with 400 elements.

n400p

Histogram sample data with 50 bins.

n400f

Histogram frequency data with 50 bins.

n800

A numeric vector with 800 elements.

n800p

Histogram sample data with 100 bins.

n800f

Histogram frequency data with 100 bins.

Source

mix2gauss_gen() databinning()

Dataset of Mixture of Exponential Distribution and Gamma Distribution

Description

Dataset of mixture of exponential distribution and gamma distribution whose density is:

0.2(2 e^{-2x}) + 0.8 \frac{x^3}{3!}e^{-x}.

Usage

mixexpgamma

Format

A list of numeric vectors of Mixture of Exponential and Gamma distribution Model.

n200

A numeric vector with 200 elements.

n400

A numeric vector with 400 elements.

n600

A numeric vector with 600 elements.

n800

A numeric vector with 800 elements.

n1000

A numeric vector with 1000 elements.

n1200

A numeric vector with 1200 elements.

Source

mixexpgamma_gen()

A density function of Mixed Exponential and Gamma Distributions

Description

A density function of

0.2(2 e^{-2x}) + 0.8 \frac{x^3}{3!}e^{-x}.

Usage

mixexpgamma_fun(x)

Arguments

Value

A numeric vector of probabilities for a given argument x.

See Also

mixexpgamma_gen()

Generate random numbers of Mixed Exponential and Gamma Distributions

Description

Generate random numbers whose density function:

0.2(2 e^{-2x}) + 0.8 \frac{x^3}{3!}e^{-x}.

Usage

mixexpgamma_gen(n = 100, seed = NULL)

Arguments

Value

A numeric vector of probabilities for a given argument x.

See Also

mixexpgamma_fun()

Probability density function of Exponential-based model

Description

A probability density function(PDF) of Exponential-based model. It is an underlying routine for plot.expmodel to compute the values of PDF. To access parameters and coefficients in an object emodel of a class expmodel, use emodel$result[k, "lmd1"], emodel$coeffs[[k]] for some index k. This index appears in the leftmost column of estimation table generated by summary(emodel).

Usage

pdf_expmodel(coeff, lmd, x)

Arguments

Value

A numeric vector of PDF of an exponential-based model.

See Also

expmodel() summary.expmodel() estimate.expmodel() func.expmodel() plot.expmodel() cdf_expmodel()

Examples

## Create an object of `expmodel`
emodel <- expmodel(mixexpgamma$n200)
## Estimate with degree 4 and rate parameter 2.0
emodel <- estimate(emodel, 4, 2.0)
## Input vector
x <- seq(0, 12, 0.1)
## Output of PDF in above estimation
yv <- pdf_expmodel(emodel$coeffs[[1]], emodel$result[1, "lmd1"], x)

Probability density function of Gaussian-based model

Description

A probability density function(PDF) of a Gaussian model. It is an underlying routine for plot.gaussmodel to compute the values of PDF. To access parameters and coefficients in an object gmodel of a class gaussmodel, use gmodel$result[k, "mu1"], gmodel$result[k, "sig1"], gmodel$coeffs[[k]] for some index k. This index appears in the leftmost column of estimation table generated by summary(gmodel).

Usage

pdf_gaussmodel(coeff, mu, sig, x)

Arguments

Value

A numeric vector of PDF of Gaussian-based distribution.

See Also

gaussmodel() summary.gaussmodel() estimate.gaussmodel() func.gaussmodel() plot.gaussmodel() cdf_gaussmodel()

Examples

## Create an object of `gaussmodel`
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate with a degree 6, a mean 0, and standard deviations 0.5
gmodel <- estimate(gmodel, 6, 0, 0.5)
## Input vector
x <- seq(-3, 3, 0.1)
## Output of PDF in above estimation
yv <- pdf_gaussmodel(gmodel$coeffs[[1]], gmodel$result[1, "mu1"],
 gmodel$result[1, "sig1"], x)

Plot a histogram and estimated densities/distributions of Exponential-based model object

Description

Plot the histogram and, if available, estimated densities or cumulative distributions of expmodel object.

Usage

## S3 method for class 'expmodel'
plot(
  x,
  cum = FALSE,
  nmax = 4,
  graphs = NULL,
  bins = 40,
  hist = TRUE,
  linesize = 1,
  ...
)

Arguments

Value

A ggplot2 object.

See Also

expmodel() summary.expmodel() func.expmodel() pdf_expmodel() cdf_expmodel()

Examples

## Create `expmodel` object from a data set mixexpgamma$n200
emodel <- expmodel(mixexpgamma$n200)
## Plot it (histogram only)
plot(emodel)

Plot a histogram and estimated densities/distributions of Gaussian-based model object

Description

Plot the histogram and, if available, estimated densities or cumulative distributions of gaussmodel object.

Usage

## S3 method for class 'gaussmodel'
plot(
  x,
  cum = FALSE,
  nmax = 4,
  graphs = NULL,
  bins = 40,
  hist = TRUE,
  scaling = FALSE,
  linesize = 1,
  ...
)

Arguments

Value

A ggplot2 object.

See Also

gaussmodel() summary.gaussmodel() func.gaussmodel() pdf_gaussmodel() cdf_gaussmodel()

Examples

## Create `gaussmodel` object from a data set mix2gauss$n200
gmodel <- gaussmodel(mix2gauss$n200)
## Plot it (histogram only)
plot(gmodel)

Substitute a coefficient of polynomial

Description

Substitute a coefficient of a polynomial with a*x + b. For a polynomial with a coefficient vector poly(x; coeff), compute the coefficient vector of

poly(a*x + b; coeff).

Usage

polyaxb(coeff, c, a, b)

Arguments

Value

A substituted coefficient.

See Also

eval_poly()

Examples

coeff <- c(2, -2, 1)
a <- 1.1
b <- 1.2
coeff1 <- c(b, a)
coeff2 <- polyaxb(coeff, 1, a, b)
xv <- c(1, 2, 3)
## a*x + b
yv1 <- eval_poly(coeff1, xv)
## polynomial(a*x + b, coeff)
yv2 <- eval_poly(coeff, yv1)
## polynomial(x, coeff2)
yv <- eval_poly(coeff2, xv)
## This value is nearly 0 in the presence of rounding errors
yv - yv2

printf

Description

printf

Usage

printf(...)

Arguments

Value

None.

Summary of Exponential-based expmodel object.

Description

Summary of expmodel object, including a mean and quantiles. If some estimation has done, also print out estimates, up to nmax number of them.

Usage

## S3 method for class 'expmodel'
summary(object, nmax = 10, estonly = FALSE, ...)

Arguments

Value

None.

See Also

expmodel() plot.expmodel() estimate.expmodel()

Examples

## Create expmodel object from a data set mixexpgamma$n200
emodel <- expmodel(mixexpgamma$n200)
## Print a summary of an object
summary(emodel)

Summary of Gaussian-based model gaussmodel object

Description

Summary of gaussmodel object, including a mean and a standard deviation and quantiles. If some estimation has done, also print out estimates, up to nmax number of them.

Usage

## S3 method for class 'gaussmodel'
summary(object, nmax = 10, estonly = FALSE, ...)

Arguments

Value

None.

See Also

gaussmodel() plot.gaussmodel() estimate.gaussmodel()

Examples

## Create gaussmodel object from a data set mix2gauss$n200
gmodel <- gaussmodel(mix2gauss$n200)
## Print a summary of an object
summary(gmodel)