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Model Information

Ivan Svetunkov edited this page Jan 30, 2026 · 7 revisions

Model Information Methods

This page documents methods for extracting dimensional and scale information from fitted smooth models.

Note: None of this is yet implemented in Python.

Summary Table

Function Returns Type
nobs() Number of observations integer
nparam() Parameter counts matrix
sigma() Residual standard deviation numeric
extractScale() Scale parameter(s) numeric/vector

For model type methods (modelType(), modelName(), errorType()), see Model-Specification.

For structural methods (orders(), lags()), see Orders-and-Lags.

For formula method, see Explanatory-Variables.

Basic Model Information

nobs()

Returns the number of observations used in model estimation.

R Usage

model <- adam(AirPassengers, "MMM", lags=12, h=12, holdout=TRUE)

# Number of observations
nobs(model)  # 132 (144 - 12 holdout)

Note

When holdout=TRUE, nobs() returns the number of in-sample observations (excluding holdout).

nparam()

Returns the number of estimated parameters in the model, broken down by category.

R Usage

model <- adam(AirPassengers, "MMM", lags=12)

# Number of estimated parameters
nparam(model)

Example

model <- adam(AirPassengers, "MAM", lags=12)
nparam(model)

The more detailed information about parameters can be accessed via:

model$nParam

Scale of the model

sigma()

Returns the residual standard deviation (scale parameter) of the model.

R Usage

model <- adam(AirPassengers, "MMM", lags=12)

# Residual standard deviation
sigma(model)

Note

The scale of the sigma depends on the type of the error in the model. If it was the additive error, the scale will be in the original units. If it was the multiplicative one, sigma will have the interpretation closer to variability percents.

extractScale()

Extracts the scale parameter from the model, including time-varying scale if a Scale-Model was fitted.

R Usage

# Basic model
model <- adam(AirPassengers, "MMM", lags=12)
extractScale(model)  # Single value

# With scale model
scaleModel <- sm(model, model="MNM", lags=12)
fullModel <- implant(model, scaleModel)
extractScale(fullModel)  # Time series of scale values

See Also

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