sn_mle(X, y, cp, plotit, traceout, iter_max, abs_tol)
y
| a vector contaning the observed variable. This is the response variable in case of linear regression. Missing values (NaN) are not allowed. |
X
| a matrix of explanatory variables. If X is missing, then a one-column matrix of all 1's is created. If X is supplied, and an intercept term is required, then it must include a column of 1's. Missing values (NaN) are not allowed. |
cp
| a vector of initial values for the centred parameters, with length(cp)=size(X,2)+2 |
plotit
| logical value, If plotit=1 (default) a plot of the nonparametric estimate of variable y (or the residuals, in the case of regression), and the parametric fit is superimposed. See below for details. |
traceout
| logical value which controls printing of the algorithm convergence. If traceout=1, details are printed. Default value is 0. |
iter_max
| this parameter is passed to the optimizer routine and represent the maximum number of iterations (default is 100); see the documentation of 'foptions' for its usage. |
abs_tol
| this parameter is passed to the optimizer routine and represent the absolute tolerance (default is 1e-5); see the documentation of 'foptions' for its usage. |
If plotting operates, an histogram is plotted.
cp
| a vector of length size(X,2)+2 with the centred parameters. |
logL
| the log-likelihood at convergence |
se
| a vector of standard errors for the cp component. |
info
| the observed information matrix for the cp component. |
options
|
messages from the optimisation routine; see the documentation
of foptions for explanation of its components.
|
Azzalini, A. and Capitanio, A. (1999). Statistical applications of the multivariate skew-normal distribution. J.Roy.Statist.Soc. B 61, part 3.
dsn
, msn_mle
a = sn_mle(NaN,bmi) # a = sn_mle([ones(length(lbm),1),lbm,lbm^2],bmi)