slm (version 1.2.0)

summary.slm: Summarizing Stationary Linear Model Fits

Description

Summary method for class "slm".

Usage

# S3 method for slm
summary(object, correlation = FALSE,
  symbolic.cor = FALSE, ...)

Arguments

object

an object of class "slm", usually, a result of a call to slm.

correlation

logical. If TRUE, the correlation matrix of the estimated parameters is returned and printed.

symbolic.cor

logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers.

...

further arguments passed to or from other methods.

Value

The function summary.slm computes and returns a list of summary statistics of the fitted linear model given in object, using the components (list elements) "call" and "terms" from its argument, plus:

residuals

the residuals, that is response minus fitted values.

coefficients

a \(p*4\) matrix with columns for the estimated coefficient, its standard error, z-statistic and corresponding (two-sided) p-value. Aliased coefficients are omitted.

aliased

named logical vector showing if the original coefficients are aliased.

sigma

the square root of the estimated variance of the error process.

df

degrees of freedom, a 3-vector \((p, n-p, p*)\), the first being the number of non-aliased coefficients, the last being the total number of coefficients.

chi2statistic

a 2-vector with the value of the chi2-statistic with its degree of freedom.

r.squared

\(R^2\), the 'fraction of variance explained by the model'.

cov.unscaled

the matrix \((X^{t} X)^{-1}\).

correlation

the correlation matrix corresponding to the above cov.unscaled, if correlation = TRUE is specified.

symbolic.cor

(only if correlation is true.) The value of the argument symbolic.cor.

References

E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583. https://arxiv.org/abs/1906.06583.

See Also

The model fitting function slm, summary.

The function coef extracts the matrix of coefficients with standard errors, z-statistics and p-values.

Examples

# NOT RUN {
data("shan")
reg1 = slm(shan$PM_Xuhui ~ . , data = shan, method_cov_st = "fitAR", model_selec = -1)
summary(reg1)

data("co2")
y = as.vector(co2)
x = as.vector(time(co2)) - 1958
reg2 = slm(y ~ x + I(x^2) + I(x^3) + sin(2*pi*x) + cos(2*pi*x) + sin(4*pi*x) +
 cos(4*pi*x) + sin(6*pi*x) + cos(6*pi*x) + sin(8*pi*x) + cos(8*pi*x),
 method_cov_st = "fitAR", model_selec = -1)
summary(reg2)
# }