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Summarise mcpfit objects

Source: R/mcpfit_methods.R
summary.mcpfit.Rd

Summarise parameter estimates and model diagnostics.

Usage

# S3 method for class 'mcpfit'
summary(object, width = 0.95, digits = 2, prior = FALSE, verbose = FALSE, ...)

# S3 method for class 'mcpfit'
fixef(object, width = 0.95, prior = FALSE, verbose = FALSE, ...)

# S3 method for class 'mcpfit'
ranef(object, width = 0.95, prior = FALSE, verbose = FALSE, ...)

# S3 method for class 'mcpfit'
print(x, ...)

Arguments

object

An mcpfit object.

width

Float. The width of the central posterior interval (between 0 and 1).

digits

a non-null value for digits specifies the minimum number of significant digits to be printed in values. The default, NULL, uses getOption("digits"). (For the interpretation for complex numbers see signif.) Non-integer values will be rounded down, and only values greater than or equal to 1 and no greater than 22 are accepted.

prior

TRUE/FALSE. Summarise prior instead of posterior?

verbose

Logical. Include the segment and dpar columns. Defaults to FALSE for a compact, v0.3.4-compatible summary.

...

Currently ignored

x

An mcpfit object.

Value

A data frame with parameter estimates and MCMC diagnostics. Rows are ordered by change point first, then mu, then the other distributional parameters, then ar/ma components - each ascending by segment. OBS: The change point distributions are often not unimodal and symmetric so the intervals can be deceiving. Plot them using plot_pars(fit).

With verbose = TRUE:

  • segment is the segment the parameter belongs to.

  • dpar is the distributional parameter ("cp", "mu", "sigma", "ar1", "ma1", etc.) the parameter belongs to.

  • mean is the posterior mean

  • lower and upper are the bounds of the central posterior interval given in width.

  • Rhat is the rank-normalized split-Rhat convergence diagnostic.

  • ess_bulk and ess_tail are the bulk and tail effective sample sizes. Low effective sample sizes are also obvious as poor mixing in trace plots (see plot_pars(fit)). Read how to deal with such problems here

For simulated data, the summary contains two additional columns so that it is easy to inspect whether the model can recover the parameters. Run simulation and summary multiple times to get a sense of the robustness.

  • sim is the value used to generate the data.

  • match is "OK" if sim is contained in the central posterior interval (lower to upper).

Functions

  • fixef(mcpfit): Fixed (population-level) effects of mcpfit.

  • ranef(mcpfit): Random (varying) effects of mcpfit.

  • print(mcpfit): Print the posterior summary of an mcpfit object.

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

# Typical usage
summary(demo_fit)
#> Family: gaussian(link = 'identity')
#> Iterations: 3000 from 3 chains.
#> Segments:
#>   1: response ~ 1
#>   2: response ~ 1 ~ 0 + time
#>   3: response ~ 1 ~ 1 + time
#> 
#> Population-level parameters:
#>         name match  sim  mean lower upper Rhat ess_bulk ess_tail
#>         cp_1       30.0 10.96   8.8  13.4  2.7        4       19
#>         cp_2       70.0 15.63  14.4  16.3  3.1        3       12
#>  Intercept_1    OK 10.0  8.35   5.5  12.8  2.5        4       22
#>       time_2    OK  0.5  0.15  -2.2   1.5  2.8        3       11
#>  Intercept_3        0.0 11.71  10.3  14.1  3.4        3       11
#>       time_3    OK -0.2 -0.31  -2.8   1.3  3.0        3       14
#>      sigma_1        4.0  9.17   5.7  11.7  3.1        3       12
#> 
#> Warning: 7 parameters show poor convergence (Rhat > 1.01 or ESS < 400).
summary(demo_fit, width = 0.8, digits = 4)  # Set interval width
#> Family: gaussian(link = 'identity')
#> Iterations: 3000 from 3 chains.
#> Segments:
#>   1: response ~ 1
#>   2: response ~ 1 ~ 0 + time
#>   3: response ~ 1 ~ 1 + time
#> 
#> Population-level parameters:
#>         name match  sim    mean  lower  upper  Rhat ess_bulk ess_tail
#>         cp_1       30.0 10.9614  8.784 13.381 2.655        4       19
#>         cp_2       70.0 15.6302 14.440 16.250 3.054        3       12
#>  Intercept_1    OK 10.0  8.3516  5.539 12.843 2.540        4       22
#>       time_2    OK  0.5  0.1523 -2.142  1.501 2.826        3       11
#>  Intercept_3        0.0 11.7093 10.282 14.095 3.404        3       11
#>       time_3    OK -0.2 -0.3131 -2.749  1.299 2.951        3       14
#>      sigma_1        4.0  9.1750  5.749 11.717 3.106        3       12
#> 
#> Warning: 7 parameters show poor convergence (Rhat > 1.01 or ESS < 400).

# Get the results as a data frame
results = summary(demo_fit)
#> Family: gaussian(link = 'identity')
#> Iterations: 3000 from 3 chains.
#> Segments:
#>   1: response ~ 1
#>   2: response ~ 1 ~ 0 + time
#>   3: response ~ 1 ~ 1 + time
#> 
#> Population-level parameters:
#>         name match  sim  mean lower upper Rhat ess_bulk ess_tail
#>         cp_1       30.0 10.96   8.8  13.4  2.7        4       19
#>         cp_2       70.0 15.63  14.4  16.3  3.1        3       12
#>  Intercept_1    OK 10.0  8.35   5.5  12.8  2.5        4       22
#>       time_2    OK  0.5  0.15  -2.2   1.5  2.8        3       11
#>  Intercept_3        0.0 11.71  10.3  14.1  3.4        3       11
#>       time_3    OK -0.2 -0.31  -2.8   1.3  3.0        3       14
#>      sigma_1        4.0  9.17   5.7  11.7  3.1        3       12
#> 
#> Warning: 7 parameters show poor convergence (Rhat > 1.01 or ESS < 400).

# Varying (random) effects
# ranef(my_fit)

# Summarise prior
summary(demo_fit, prior = TRUE)
#> Family: gaussian(link = 'identity')
#> Iterations: 3000 from 3 chains.
#> Segments:
#>   1: response ~ 1
#>   2: response ~ 1 ~ 0 + time
#>   3: response ~ 1 ~ 1 + time
#> 
#> Population-level parameters:
#>         name match  sim   mean  lower upper Rhat ess_bulk ess_tail
#>         cp_1    OK 30.0 37.799   4.23  91.5    1     2673     2481
#>         cp_2    OK 70.0 60.947  14.47  97.0    1     2459     2898
#>  Intercept_1    OK 10.0 10.601 -28.21  49.9    1     2811     2902
#>       time_2    OK  0.5 -0.014  -1.18   1.2    1     2638     2750
#>  Intercept_3    OK  0.0 10.405 -30.56  50.6    1     2872     2863
#>       time_3    OK -0.2  0.002  -1.30   1.3    1     2716     2883
#>      sigma_1    OK  4.0 14.225   0.42  51.8    1     2935     2990