int_lap {errint} | R Documentation |
Building Error Intervals
Description
int_lap
computes the error interval of a set of residuals
assuming a Laplace distribution with zero location for the noise.
int_gau
computes the error interval of a set of residuals
assuming a Gaussian distribution with zero mean for the noise.
int_lap_mu
computes the error interval of a set of residuals
assuming a Laplace distribution.
int_gau_mu
computes the error interval of a set of residuals
assuming a Gaussian distribution.
int_beta
computes the error interval of a set of residuals
assuming a Beta distribution.
int_weibull
computes the error interval of a set of residuals
assuming a Weibull distribution.
See also 'Details'.
int_moge
computes the error interval of a set of residuals
assuming a MOGE distribution.
Usage
int_lap(phi, s)
int_gau(phi, s, ps = 0, threshold = 10^-2, upper = 10^6)
int_lap_mu(phi, s, ps = stats::median(phi, na.rm = T), threshold = 10^-2,
upper = 10^6)
int_gau_mu(phi, s, ps = mean(phi, na.rm = T), threshold = 10^-2,
upper = 10^6)
int_beta(phi, s, original_phi = phi, ps = 10^-4, threshold = 10^-4,
upper = 1, m1 = mean(phi, na.rm = T), m2 = mean(phi^2, na.rm = T),
alpha_0 = (m1 * (m1 - m2))/(m2 - m1^2), beta_0 = (alpha_0 * (1 - m1)/m1))
int_weibull(phi, s, ps = 10^-4, threshold = 10^-2, upper = 10^6,
k_0 = 1)
int_moge(phi, s, ps = 10^-4, threshold = 10^-4, upper = 10^6,
lambda_0 = 1, alpha_0 = 1, theta_0 = 1)
Arguments
phi |
residual values used to compute the error interval. |
s |
confidence level, e,g. s=0.05 for the standard 95 percent confidence interval. |
ps |
minimum value to search for solution of the integral equation to solve. See also 'Details'. |
threshold |
step size to increase ps after each iterarion. See also 'Details'. |
upper |
maximum value to search for solution of the integral equation to solve. See also 'Details'. |
original_phi |
original |
m1 |
first moment of the residuals. Used to compute |
m2 |
second moment of the residuals. Used to compute |
alpha_0 |
initial value for Newton-Raphson method for the parameter |
beta_0 |
initial value for Newton-Raphson method for the parameter |
k_0 |
initial value for Newton-Raphson method for the parameter |
lambda_0 |
initial value for Newton-Raphson method for the parameter |
theta_0 |
initial value for Newton-Raphson method for the parameter |
Details
For the Zero-\mu
Laplace distribution the value of the corresponding integral
equation has a closed solution of the form ps=-\sigma \log{2s}
.
For the other distributions, starting with the initial value of ps
passed as argument, the value, integral
, of the corresponding integral expression is
computed (see also 'References' for an in-depth explanation of this integral expression).
If integral
is smaller than 1-s
then ps
is increased
by a step size of threshold
value and integral
is recomputed.
If integral
is greater or equal than 0 or if ps
gets bigger than
upper
, the loop stops and the last value of ps
will be its final value.
In addition, for the Beta distribution values of parameters \alpha
and
\beta
are estimated using Newton-Raphson method.
For the Weibull distribution
value of parameter \kappa
is estimated using Newton-Raphson method and then estimated
value of \lambda
is computed using a closed form that depends on \kappa
.
For the MOGE distribution values of parameters \lambda
, \alpha
and
\theta
are estimated using Newton-Raphson method.
See also 'References'.
Value
Returns an object of class c("error_interval","list")
with information of the corresponding error interval.
Author(s)
Jesus Prada, jesus.prada@estudiante.uam.es
References
Link to the scientific paper
Prada, Jesus, and Jose Ramon Dorronsoro. "SVRs and Uncertainty Estimates in Wind Energy Prediction." Advances in Computational Intelligence. Springer International Publishing, 2015. 564-577,
with theoretical background for this package is provided below.
http://link.springer.com/chapter/10.1007/978-3-319-19222-2_47
See Also
Examples
int_lap(rnorm(10),0.1)
int_gau(rnorm(10),0.1,0.1,10^-3,10^2)
int_lap_mu(rnorm(10),0.1,0.1,10^-3,10^2)
int_gau_mu(rnorm(10),0.1,0.1,10^-3,10^2)
int_beta(runif(10,0,0.99),0.1,alpha_0=1,beta_0=1)
int_weibull(abs(rnorm(10)),0.1,k_0=2)
int_moge(runif(10,0.01,0.99),0.1,lambda_0=2,alpha_0=3,theta_0=4)