Description slm function, in "slm-main.R" Methods for slm, in "slm-method.R" Others functions, in "auxiliary-fun.R" Generative functions, in "generative.R" Data References
The slm
package enables to fit linear models on datasets considering the dependence between the observations.
Most of the functions are based on the functions and methods of lm
, with the same arguments and the same format for the outputs.
slm
function, in "slm-main.R"The slm
function is the main function of this package. Its architecture is the same as the lm
function
but it takes into account the possible correlation between the observations. To estimate the asymptotic covariance matrix of
the least squares estimator, several approaches are available: "fitAR" calls the
cov_AR
function, "spectralproj" the cov_spectralproj
function, "kernel" the cov_kernel
function,
"efromovich" the cov_efromovich
function and "select" the cov_select
function. The "hac" method uses the sandwich
package,
and more precisely, the method described by Andrews (1991) and Zeileis (2004).
slm
, in "slm-method.R"The slm
function has several associated methods, which are the same as for the lm
function.
The available methods are: summary
, confint
, predict
, plot
and vcov
.
The package has some auxiliary functions, in particular some predefined kernels for the kernel method of slm
function: the
trapeze kernel, the triangle kernel and the rectangular kernel. The user can also define his own kernel and put it in the argument
kernel_fonc
in the slm
function.
The generative_process
function generates some stationary processes.
The generative_model
function generates some designs.
The package contains a dataset "shan". This dataset comes from a study about fine particle pollution in the city of Shanghai. The data are available on the following website https://archive.ics.uci.edu/ml/datasets/PM2.5+Data+of+Five+Chinese+Cities#.
D. Andrews (1991). Heteroskedasticity and autocorrelation consistent covariant matrix estimation. Econometrica, 59(3), 817-858.
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.
A. Zeileis (2004). Econometric computing with HC and HAC covariance matrix estimators.
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