cdfFMD | R Documentation |
It computes the cumulative distribution function on x
for a folded p
-variate Normal, Skew-normal (SN), Extended Skew-normal (ESN) and Student's t-distribution.
cdfFMD(x,mu,Sigma,lambda = NULL,tau = NULL,dist,nu = NULL)
x |
vector of length p where the cdf is evaluated. |
mu |
a numeric vector of length p representing the location parameter. |
Sigma |
a numeric positive definite matrix with dimension pxp representing the scale parameter. |
lambda |
a numeric vector of length p representing the skewness parameter for SN and ESN cases. If |
tau |
It represents the extension parameter for the ESN distribution. If |
dist |
represents the folded distribution to be computed. The values are |
nu |
It represents the degrees of freedom for the Student's t-distribution. |
Normal case by default, i.e., when dist
is not provided. Univariate case is also considered, where Sigma
will be the variance σ^2.
It returns the distribution value for a single point x
.
Degrees of freedom must be a positive integer. If nu >= 200
, Normal case is considered."
Christian E. Galarza <cgalarza88@gmail.com> and Victor H. Lachos <hlachos@uconn.edu>
Maintainer: Christian E. Galarza <cgalarza88@gmail.com>
Galarza, C. E., Lin, T. I., Wang, W. L., & Lachos, V. H. (2021). On moments of folded and truncated multivariate Student-t distributions based on recurrence relations. Metrika, 84(6), 825-850 <doi:10.1007/s00184-020-00802-1>.
Galarza, C. E., Matos, L. A., Dey, D. K., & Lachos, V. H. (2022a). "On moments of folded and doubly truncated multivariate extended skew-normal distributions." Journal of Computational and Graphical Statistics, 1-11 <doi:10.1080/10618600.2021.2000869>.
Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022b). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 <doi:10.1016/j.jmva.2021.104944>.
momentsFMD
, meanvarFMD
mu = c(0.1,0.2,0.3,0.4) Sigma = matrix(data = c(1,0.2,0.3,0.1,0.2,1,0.4,-0.1,0.3,0.4,1,0.2,0.1,-0.1,0.2,1), nrow = length(mu),ncol = length(mu),byrow = TRUE) cdfFMD(x = c(0.5,0.2,1.0,1.3),mu,Sigma,dist="normal") cdfFMD(x = c(0.5,0.2,1.0,1.3),mu,Sigma,dist = "t",nu = 4) cdfFMD(x = c(0.5,0.2,1.0,1.3),mu,Sigma,lambda = c(-2,0,2,1),dist = "SN") cdfFMD(x = c(0.5,0.2,1.0,1.3),mu,Sigma,lambda = c(-2,0,2,1),tau = 1,dist = "ESN")
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