drawDiagPlots: Draw diagnostic plots for FDSLRM

In gajdosandrej/fdslrm: R package for modeling and prediction of time series using linear mixed models

Description

drawDiagPlots(plots_names, fit_diag_fdslrm_output) draws diagnostics plots of fitted FDSLRM. Plot with original time series, fitted values and fitted mean values is included.

Usage

drawDiagPlots(plots_names = "all", fit_diag_fdslrm_output)

Arguments

plots_names	vector of type character containing names of available diagnostic plots. Default is plots_names = "all". See details section of documentation and example for more information.
fit_diag_fdslrm_output	list - an output of function fitDiagFDSLRM.

Details

You can find names of all available diagnostic plots in the list, which is an ouput of function fitDiagFDSLRM. These names are under the item "diagnostic_plots_names".

Value

Diagnostic plots of particular fitted FDSLRM.

Note

Ver.: 12-Jan-2019 17:18:53.

Examples

## EXAMPLE 1 (LMM)
# load all necessary R packages and functions to fit and make diagnostic of FDSLRM
initialFDSLRM()
# the following data set was adapted from Hyndman's package fpp2 (Hyndman 2018)
# data represent total quarterly visitor nights (in millions) from 1998-2016 in one of the regions
# of Australia - inner zone of Victoria state
# the number of time series observations is  n = 76
# data visualization
autoplot(window(visnights)[,15], ylab = "Visnights")
# time series realization, observation times
dt <- as.numeric((window(visnights)[,15]))
t <- 1:length(dt)
# periodogram to identify the most significant frequencies to build the model (design matrices)
periodo <- spec.pgram(dt, log="no")
# according to periodogram we choose the frequencies 1/80, 2/80 for trend
# and the frequencies 20/80, 40/80 for random component
# fit the model with lme function
output_lme <- fitDiagFDSLRM(dt, t, c(1/80, 2/80), include_fixed_eff = c(1,0,0,1),
                          freq_random = c(20/80, 40/80), include_random_eff = c(1,1,1,0),
                          poly_trend_degree = 0, season_period = 4)
# draw diagnostic plots
drawDiagPlots("all", output_lme)
drawDiagPlots(output_lme$diagnostic_plots_names$FittedTimeSeries, output_lme)
# show summary of model fit
str(output_lme$fit_summary)
# fit the model with mixed function
output_mixed <- fitDiagFDSLRM(dt, t, c(1/80, 2/80), include_fixed_eff = c(1,0,0,1),
                            freq_random = c(20/80, 40/80), include_random_eff = c(1,1,1,0),
                            var_estim_method = "MINQEI",
                            fit_function = "mixed", season_period = 4)
# draw diagnostic plots
drawDiagPlots("all", output_mixed)
drawDiagPlots(output_mixed$diagnostic_plots_names$CumulatPeriodogCondResid, output_lme)
# show summary of model fit
str(output_mixed$mixed_fit)

# fit the model with mmer function
output_mmer <- fitDiagFDSLRM(dt, t, c(1/80, 2/80), include_fixed_eff = c(1,0,0,1),
                            freq_random = c(20/80, 40/80), include_random_eff = c(1,1,1,0),
                            fit_function = "mmer", season_period = 4)
# draw diagnostic plots
drawDiagPlots("all", output_mmer)
drawDiagPlots(output_mmer$diagnostic_plots_names$CumulatPeriodogCondResid, output_mmer)
# show summary of model fit
str(output_mmer$mmer_fit)

## EXAMPLE 2 (CLRM)
# load all necessary R packages and functions to fit and make diagnostic of FDSLRM
initialFDSLRM()
# the following data set was adapted from Hyndman's package fpp2 (Hyndman 2018)
# data represent total quarterly visitor nights (in millions) from 1998-2016 in one of the regions
# of Australia - inner zone of Victoria state
# the number of time series observations is  n = 76
# data visualization
autoplot(window(visnights)[,15], ylab = "Visnights")
# time series realization, observation times
dt <- as.numeric((window(visnights)[,15]))
t <- 1:length(dt)
# periodogram to identify the most significant frequencies to build the model (design matrices)
periodo <- spec.pgram(dt, log="no")
# according to periodogram we choose the frequencies 1/80, 2/80 for trend
# and the frequencies 20/80, 40/80 for random component
# fit the model with lme function
output_lm <- fitDiagFDSLRM(dt, t, c(1/80, 2/80, 20/80, 40/80), include_fixed_eff = c(1,0,0,1,0,1,1,0), season_period = 4)
# draw diagnostic plots
drawDiagPlots("all", output_lm)
drawDiagPlots(output_lm$diagnostic_plots_names$FittedTimeSeries, output_lme)
# show summary of model fit
str(output_lm$fit_summary)

gajdosandrej/fdslrm documentation built on April 28, 2020, 11:35 a.m.