Sim.DiffProc (version 2.5)

Ajdexp: Adjustment By Exponential Distribution

Description

Adjusted your sample by the exponential law, estimated these parameters using the method of maximum likelihood, and calculating the Akaike information criterion for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood + k*npar, where npar represents the number of parameters in the fitted model, and k = 2 for the usual AIC, and computes confidence intervals for one or more parameters in a fitted model (Law).

Usage

Ajdexp(X, starts = list(lambda = 1), leve = 0.95)

Arguments

X

a numeric vector of the observed values.

starts

named list. Initial values for optimizer.

leve

the confidence level required.

Value

• coef
  Coefficients extracted from the model.
• AIC
  A numeric value with the corresponding AIC.
• vcov
  A matrix of the estimated covariances between the parameter estimates in the linear or non-linear predictor of the model.
• confint
  A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2.

Details

The optim optimizer is used to find the minimum of the negative log-likelihood. An approximate covariance matrix for the parameters is obtained by inverting the Hessian matrix at the optimum. For more detail consulted mle,confint,AIC. R has the [dqpr]exp functions to evaluate the density, the quantiles, and the cumulative distribution or generate pseudo random numbers from the exponential distribution.

See Also

Ajdchisq Adjustment By Chi-Squared Distribution,Ajdf Adjustment By F Distribution, Ajdgamma Adjustment By Gamma Distribution,Ajdlognorm Adjustment By Log Normal Distribution, Ajdnorm Adjustment By Normal Distribution,Ajdt Adjustment By Student t Distribution, Ajdweibull Adjustment By Weibull Distribution,Ajdbeta Adjustment By Beta Distribution.

Examples

X <- rexp(100,15)
Ajdexp(X, starts = list(lambda = 1), leve = 0.95)