summary.lts {rrcov} | R Documentation |
summary
method for class "lts"
.
## S3 method for class 'lts': summary(object, correlation = FALSE, ...) ## S3 method for class 'summary.lts': print(x, digits = max(3, getOption("digits") - 3), ...)
object |
an object of class "lts" , usually, a result of a call to ltsReg . |
x |
an object of class "summary.lts" , usually, a result of a call to summary.lts . |
correlation |
logical; if TRUE , the correlation matrix of the estimated parameters is returned and printed. |
digits |
the number of significant digits to use when printing. |
... |
further arguments passed to or from other methods. |
This function prints summary statistics for weighted least square estimates with weights based on LTS estimates. Therefore the statistics are similar to these for LS but all terms are multiplied by the corresponding weight.
Correlations are printed to two decimal places: to see the actual correlations
print summary(object)$correlation
directly.
The function summary.lts
computes and returns a list of summary
statistics of the fitted linear model given in object
, using
the components of this object (list elements).
residuals |
the residuals - a vector like the response y containing the residuals from the weighted
least squares regression. |
coefficients |
a p x 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. |
sigma |
the estimated scale of the reweighted residuals
sigma^2 = 1/(n-p) Sum(R[i]^2),
where R[i] is the i-th residual, |
df |
degrees of freedom, a 3-vector (p, n-p, p*), the last being the number of non-aliased coefficients. |
fstatistic |
(for models including non-intercept terms) a 3-vector with the value of the F-statistic with its numerator and denominator degrees of freedom. |
r.squared |
R^2, the “fraction of variance explained by
the model”,
R^2 = 1 - Sum(R[i]^2) / Sum((y[i]- y*)^2), where y* is the mean of y[i] if there is an intercept and zero otherwise. |
adj.r.squared |
the above R^2 statistic “adjusted”, penalizing for higher p. |
cov.unscaled |
a p x p matrix of (unscaled) covariances of the coef[j], j=1, ..., p. |
correlation |
the correlation matrix corresponding to the above
cov.unscaled , if correlation = TRUE is specified. |
data(brain) summary(ltsReg(brain~body, data = log(brain)))