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Regression-based model fitting

fit_lmshift.Rd

Regression-based model fitting

Usage

fit_lmshift(x, tau, deg_poly = 0, ...)

fit_lmshift_ar1(x, tau, ...)

fit_trendshift(x, tau, ...)

fit_trendshift_ar1(x, tau, ...)

Arguments

x

A time series

tau

a set of indices representing a changepoint set

deg_poly

integer indicating the degree of the polynomial spline to be fit. Passed to stats::poly().

...

arguments passed to stats::lm()

Value

A mod_cpt object

Details

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

See also

Other model-fitting: fit_arima(), fit_meanshift(), fit_meanvar(), fit_nhpp(), model_args(), model_name(), new_fun_cpt(), whomademe()

Examples

# Manually specify a changepoint set
tau <- c(365, 826)

# Fit the model
mod <- fit_lmshift(DataCPSim, tau)

# Retrieve model parameters
logLik(mod)
#> 'log Lik.' -5250.548 (df=6)
deg_free(mod)
#> [1] 6

# 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)
#> # A tibble: 1 × 11
#>   pkg     version algorithm params num_cpts  rmse logLik   AIC   BIC  MBIC   MDL
#>   <chr>   <pckg_> <chr>     <list>    <int> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 tidych… 1.0.0.… meanshift <dbl>         3 0.576  -317.  651.  682.  681.  692.
glance(fit_lmshift(CET, tau = ids, deg_poly = 1))
#> # A tibble: 1 × 11
#>   pkg     version algorithm params num_cpts  rmse logLik   AIC   BIC  MBIC   MDL
#>   <chr>   <pckg_> <chr>     <list>    <int> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 tidych… 1.0.0.… trendshi… <dbl>         3 0.538  -292.  608.  655.  630.  658.
glance(fit_lmshift_ar1(CET, tau = ids))
#> # A tibble: 1 × 11
#>   pkg     version algorithm params num_cpts  rmse logLik   AIC   BIC  MBIC   MDL
#>   <chr>   <pckg_> <chr>     <list>    <int> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 tidych… 1.0.0.… meanshif… <dbl>         3 0.567  -311.  640.  675.  668.  684.
glance(fit_lmshift_ar1(CET, tau = ids, deg_poly = 1))
#> # A tibble: 1 × 11
#>   pkg     version algorithm params num_cpts  rmse logLik   AIC   BIC  MBIC   MDL
#>   <chr>   <pckg_> <chr>     <list>    <int> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 tidych… 1.0.0.… trendshi… <dbl>         3 0.537  -291.  608.  658.  628.  661.
glance(fit_lmshift_ar1(CET, tau = ids, deg_poly = 2))
#> # A tibble: 1 × 11
#>   pkg     version algorithm params num_cpts  rmse logLik   AIC   BIC  MBIC   MDL
#>   <chr>   <pckg_> <chr>     <list>    <int> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 tidych… 1.0.0.… splinesh… <dbl>         3 0.535  -290.  613.  680.  625.  675.

# Empty changepoint sets are allowed
fit_lmshift(CET, tau = NULL)
#> ℹ Model: A null  model with 1 region(s).
#> → Each region has 1 parameter(s).
#> → The model has 1 global parameter(s).

# Duplicate changepoints are removed
fit_lmshift(CET, tau = c(42, 42))
#> ℹ Model: A meanshift  model with 2 region(s).
#> → Each region has 1 parameter(s).
#> → The model has 1 global parameter(s).