Base class for OOP statistical distribution
ddist[function] density function
pdist[function] distribution function
qdist[function] quantile function
rdist[function] random generator function
ks.test[ks.test] Goodness of fit with ks.test
fit_success[bool] TRUE only if the fit is a success and is occurred
name[string] name of the distribution
opt[stats::optim result] Result of the MLE to find parameters
cov[matrix] Covariance matrix of parameters, inverse of hessian
coef[vector] Vector of coefficients
new()Create a new AbstractDist object.
AbstractDist$new(ddist, pdist, qdist, rdist, name, has_gr_nlll)ddist[function] Density function, e.g. dnorm
pdist[function] Distribution function, e.g. pnorm
qdist[function] Quantile function, e.g. qnorm
rdist[function] Random generator function, e.g. rnorm
name[str] name of the distribution
has_gr_nlll[bool] If the derived class has defined the gradient of the negative log-likelihood
A new `AbstractDist` object.
rvs()Generation sample from the histogram
AbstractDist$rvs(n)n[integer] Number of samples drawn
[vector] A vector of samples
x[vector] Values to compute the density
[vector] density
logdensity()Log density function
AbstractDist$logdensity(x)x[vector] Values to compute the log-density
[vector] log of density
cdf()Cumulative Distribution Function
AbstractDist$cdf(q)q[vector] Quantiles to compute the CDF
[vector] cdf values
sf()Survival Function
AbstractDist$sf(q)q[vector] Quantiles to compute the SF
[vector] sf values
icdf()Inverse of Cumulative Distribution Function
AbstractDist$icdf(p)p[vector] Probabilities to compute the CDF
[vector] icdf values
isf()Inverse of Survival Function
AbstractDist$isf(p)p[vector] Probabilities to compute the SF
[vector] isf values
fit()Fit method
AbstractDist$fit(Y, n_max_try = 100)Y[vector] Dataset to infer the histogram
n_max_try[integer] Because the optim function can fails, the fit is retry n_try times.
`self`
qgradient()Gradient of the quantile function
AbstractDist$qgradient(p, lower.tail = TRUE)p[vector] Probabilities
lower.tail[bool] If CDF or SF.
[vector] gradient
qdeltaCI()Confidence interval of the quantile function
AbstractDist$qdeltaCI(p, Rt = FALSE, alpha = 0.05)p[vector] Probabilities
Rt[bool] if Probabilities or return times
alpha[double] level of confidence interval
[list] Quantiles, and confidence interval
pdeltaCI()Confidence interval of the CDF function
AbstractDist$pdeltaCI(x, Rt = FALSE, alpha = 0.05)x[vector] Quantiles
Rt[bool] if Probabilities or return times
alpha[double] level of confidence interval
[list] CDF, and confidence interval
diagnostic()Diagnostic of the fitted law
AbstractDist$diagnostic(Y, alpha = 0.05)Y[vector] data to check
alpha[double] level of confidence interval
[NULL]
clone()The objects of this class are cloneable with this method.
AbstractDist$clone(deep = FALSE)deepWhether to make a deep clone.
This class is only used to be herited