Description Usage Arguments Value See Also Examples
These are basic computing functions called by heavyLm
used to
fit linear models considering heavy-tailed errors. These should usually
not be used directly unless by experienced users.
1 2 3 | heavyLm.fit(x, y, family = Student(df = 4), control = heavy.control())
heavyMLm.fit(x, y, family = Student(df = 4), control = heavy.control())
|
x |
design matrix of dimension |
y |
vector of observations of length |
family |
a description of the error distribution to be used in the model. By default the Student-t distribution with 4 degrees of freedom is considered. |
control |
a list of control values for the estimation algorithm to replace
the default values returned by the function |
a list with components
family |
the |
coefficients |
|
sigma2 |
scale estimate of the random error (only available for univariate regression models). |
Sigma |
estimate of scatter matrix for each row of the response matrix
(only available for objects of class |
residuals |
|
fitted.values |
|
weights |
estimated weights corresponding to the assumed heavy-tailed distribution. |
distances |
squared of scaled residuals or Mahalanobis distances. |
acov |
asymptotic covariance matrix of the coefficients estimates. |
logLik |
the log-likelihood at convergence. |
heavyLm
which you should use for multivariate or univariate linear
regression under heavy-tailed distributions unless you know better.
1 2 3 4 5 6 7 8 9 10 11 12 13 | # univariate linear regression
data(ereturns)
x <- cbind(1, ereturns$CRSP)
colnames(x) <- c("Intercept", "CRSP")
y <- ereturns$m.marietta
z <- heavyLm.fit(x = x, y = y)
# multivariate linear regression
data(dialyzer)
y <- as.matrix(dialyzer[,1:4])
n <- nrow(y)
x <- matrix(1, nrow = n, ncol = 1) # a vector of ones
z <- heavyMLm.fit(x = x, y = y)
|
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