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Methods for msarima Objects

Source: R/methods.R
msarima-methods.Rd

Methods for extracting information from and displaying fitted multi-seasonal ARIMA models.

Usage

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

# S3 method for class 'msarima'
summary(object, ...)

# S3 method for class 'summary_msarima'
print(x, digits = 4, signif.stars = TRUE, ...)

# S3 method for class 'msarima'
coef(object, ...)

# S3 method for class 'msarima'
fitted(object, ...)

# S3 method for class 'msarima'
residuals(object, ...)

# S3 method for class 'msarima'
logLik(object, ...)

# S3 method for class 'msarima'
AIC(object, ...)

# S3 method for class 'msarima'
BIC(object, ...)

Arguments

x, object

An object of class "msarima".

...

Additional arguments (currently not used).

digits

Number of digits to display. Default is 4.

signif.stars

Logical; if TRUE, significance stars are printed. Default is TRUE.

Value

print

Returns the input object invisibly.

summary

Returns an object of class "summary_msarima" containing:

  • call: The matched call.

  • series: Name of the time series.

  • order: Non-seasonal ARIMA order.

  • seasonal: List of seasonal orders (P, D, Q), one vector per seasonal component.

  • seasonal.period: Vector of seasonal periods.

  • coefficients: Matrix with estimates, standard errors, z-values, and p-values.

  • sigma2: Estimated innovation variance.

  • loglik: Log-likelihood of the fitted model.

  • aic: Akaike Information Criterion.

  • bic: Bayesian Information Criterion.

  • aicc: Corrected Akaike Information Criterion.

  • nobs: Number of observations in the original series.

  • n.used: Number of observations used in estimation.

  • method: Character string describing the fitted model, e.g. "mSARIMA(1,0,0)(1,0,0)[4]".

  • convergence: Convergence code from optimization.

coef

A named numeric vector of coefficients.

fitted

A time series object of fitted values.

residuals

A time series object of residuals.

logLik

An object of class "logLik".

AIC

The AIC value.

BIC

The BIC value.

Details

print

Prints a concise summary of the fitted model including coefficients and information criteria.

summary

Produces a detailed summary including coefficient estimates, standard errors, z-values, and p-values.

coef

Extracts the estimated model coefficients.

fitted

Extracts the fitted values.

residuals

Extracts the model residuals.

logLik

Extracts the log-likelihood value.

AIC

Calculates the Akaike Information Criterion.

BIC

Calculates the Bayesian Information Criterion.

See also

msarima

Examples

# Fit a model
set.seed(123)
y <- msarima.sim(nsim = 200,
                 order = c(1, 0, 0),
                 seasonal = list(c(1, 0, 0), c(0, 0, 1)),
                 seasonal.period = c(4, 11),
                 ar = c(0.5, 0.5), ma = 0.3, sigma2 = 1)

fit <- msarima(y, order = c(1, 0, 0),
               seasonal = list(c(1, 0, 0), c(0, 0, 1)),
               seasonal.period = c(4, 11))

# Use methods
print(fit)
#> Series: y 
#> mSARIMA(1,0,0)(1,0,0)[4](0,0,1)[11]
#> 
#> Coefficients:
#>    AR(1) SAR(1,1) SMA(2,1) 
#>   0.4101   0.4678   0.3805 
#> 
#> sigma^2 = 0.9272  log likelihood = -92.44
#> AIC = 192.88   AICc = 193.08   BIC = 206.07
summary(fit)
#> Series: y 
#> mSARIMA(1,0,0)(1,0,0)[4](0,0,1)[11]
#> 
#> Coefficients:
#>          Estimate Std. Error z value Pr(>|z|)    
#> AR(1)     0.41006    0.06570   6.241 4.35e-10 ***
#> SAR(1,1)  0.46782    0.06428   7.278 3.40e-13 ***
#> SMA(2,1)  0.38051    0.06943   5.481 4.24e-08 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> sigma^2 = 0.9272  log likelihood = -92.4387
#> AIC = 192.8775   AICc = 193.0826   BIC = 206.0707
coef(fit)
#>     AR(1)  SAR(1,1)  SMA(2,1) 
#> 0.4100567 0.4678163 0.3805071 
head(fitted(fit))
#> Multi-Seasonal Time Series:
#> Start: 1 1
#> Seasonal Periods: 4 11
#> Data:
#> [1] -0.8118898 -0.6042914 -0.8312793 -1.5763490 -0.7240990 -0.5768790
#> 
head(residuals(fit))
#> Multi-Seasonal Time Series:
#> Start: 1 1
#> Seasonal Periods: 4 11
#> Data:
#> [1]  0.13471646 -0.62643432 -2.21643836  0.02942345  0.40456177 -1.85157381
#> 
logLik(fit)
#> 'log Lik.' -92.43873 (df=4)
AIC(fit)
#> [1] 192.8775
BIC(fit)
#> [1] 206.0707