gauss_fit: Fit a time serie as independent gaussians.
In thaos/FARg: FAR estimation

Description Usage Arguments Details Value Examples

gauss_fit fits a time serie as independant gaussians where the mean and the variance depend linearly on a set of covariates.

1 2	gauss_fit(y, data, mu_mod = ~1, sig_mod = ~1, sig_link = "log", time_var, init = NULL)

`y`	the time serie to be fitted.
`data`	a data.frame object with where the function looks first for the variables y, time_var and the covariates specified in the mu_mod and sig_mod arguments.
`mu_mod`	a formula defining the covariates the mean parameter of the gaussian depends linearly on.
`sig_mod`	a formula defining the covariates of the standard deviation parameter of the gaussian depends linearly on.
`sig_link`	a link function name for the parameter sigma: sig_link(sigma) is a linear function of the covariates.
`time_var`	a variable used to define the time in the time serie. It can also be a string giving the variable name.
`init`	vector of initialization parameter for the minimization of the negative log-likelihood. if NULL, the initialisation is done using one iteration of feasible GLS.

MLE fit of a time serie y as independant gaussians where the mean and the variance depend linearly on a set of covariates. The optimization of the negative log-likelihood is done with the nlminb function in R.

returns an object of class gauss_fit. It contains the nlminb output which provides the estimated parameters as well the minimum of the negative log-likelihood. The arguments use to call gauss_fit are also returned in the list.

data(tas)
#Example with the same covariate for the mean and variance parameter
ga_fit <- gauss_fit(eur_tas, data=tas, mu_mod=~gbl_tas, sig_mod=~gbl_tas, time_var="year")
# get the values of the mean and variance parameters of the gaussian at each time
compute_par(ga_fit, tas)
# plot diagnostic plot of the fit : standardized residuals plots, qqplot, density of fitted vs theorical density, times series ans return levels
plot(ga_fit)