Regression-based model fitting
fit_lmshift(x, tau, deg_poly = 0, ...) fit_lmshift_ar1(x, tau, ...) fit_trendshift(x, tau, ...) fit_trendshift_ar1(x, tau, ...)
x: A time seriestau: a set of indices representing a changepoint setdeg_poly: integer indicating the degree of the polynomial spline to be fit. Passed to stats::poly()....: arguments passed to stats::lm()A mod_cpt object
These model-fitting functions use stats::lm() to fit the corresponding regression model to a time series, using the changepoints specified by the tau argument. Each changepoint is treated as a categorical fixed-effect, while the deg_poly
argument controls the degree of the polynomial that interacts with those fixed-effects. For example, setting deg_poly equal to 0 will return the same model as calling fit_meanshift_norm(), but the latter is faster for larger changepoint sets because it doesn't have to fit all of the regression models.
Setting deg_poly equal to 1 fits the trendshift model.
fit_lmshift_ar1(): will apply auto-regressive lag 1 errors
fit_trendshift(): will fit a line in each region
fit_trendshift_ar1(): will fit a line in each region and autoregress lag 1 errors
# Manually specify a changepoint set tau <- c(365, 826) # Fit the model mod <- fit_lmshift(DataCPSim, tau) # Retrieve model parameters logLik(mod) deg_free(mod) # Manually specify a changepoint set cpts <- c(1700, 1739, 1988) ids <- time2tau(cpts, as_year(time(CET))) # Fit the model mod <- fit_lmshift(CET, tau = ids) # View model parameters glance(mod) glance(fit_lmshift(CET, tau = ids, deg_poly = 1)) glance(fit_lmshift_ar1(CET, tau = ids)) glance(fit_lmshift_ar1(CET, tau = ids, deg_poly = 1)) glance(fit_lmshift_ar1(CET, tau = ids, deg_poly = 2)) # Empty changepoint sets are allowed fit_lmshift(CET, tau = NULL) # Duplicate changepoints are removed fit_lmshift(CET, tau = c(42, 42))
Other model-fitting: fit_meanshift(), fit_meanvar(), fit_nhpp(), model_args(), model_name(), new_fun_cpt(), whomademe()