Return EM algorithm output for NL-SMSN regression for both "Homoscedastic" and "Heteroscedastic" (univaritate case, p=1).
smsn.nl(y, x = NULL, z = NULL, betas = NULL, sigma2 = NULL,
shape = NULL, rho = NULL,
nu = NULL, nlf = NULL, rho.func = 1,
reg.type = "Homoscedastic", criteria = FALSE,
family = "Skew.t", error = 1e-05, iter.max = 100)
the response vector
the independent covariates
the independent covariates for sigma2. "Heteroscedastic" model ONLY!
regression coefficient(s) vector
initial value for the scale parameter
initial value for the skewness parameter
initial value for "Heteroscedastic" coefficient rho. "Heteroscedastic" model ONLY!
the parameter of the scale variable (vector or scalar) of the SMSN family (kurtosis parameter). For the "Skew.cn" must be a vector of length 2 and values in (0,1)
non linear function for the regression
Choose the type of heteroscedasticity for sigma2. If rho.func == 1 ( f(z,rho) = exp(z*rho) ) and rho.func == 2 ( f(z,rho) = z^rho ).
the type of possible regression: "Homoscedastic" or "Ho"; "Heteroscedastic" or "He".
if TRUE, loglik, AIC, BIC will be calculated
distribution famility to be used in fitting ("t", "Skew.t", "Skew.cn", "Skew.slash", "Skew.normal", "Normal")
the covergence maximum error
maximum iterations of the EM algorithm
Estimated values of the location, scale, skewness, regression coefficients and "Heteroscedastic" coefficient (when reg.type = "He").
Aldo M. Garay, Victor H. Lachos, Carlos A. Abanto-Valle (2011). "Nonlinear regression models based on scale mixture of skew-normal distributions". Journal of the Korean Stastical Society, 40, 115-124.\
Victor H. Lachos, Dipankar Bandyopadhyay and Aldo M. Garay (2011). "Heteroscedastic nonlinear regression models based on scale mixture of skew-normal distributions". Statistics -and Probability Letters, 81, 1208-1217.
# NOT RUN {
##see examples in \code{\link{Oil}} and \code{\link{Ultrasonic}}
# }
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