Description Usage Arguments Details Value References See Also Examples
slm
is used to fit linear models when the error process is assumed to be strictly stationary.
1 2 3 4 |
myformula |
an object of class " |
data |
an optional data frame, list or environment (or object coercible by |
model, x, y, qr |
logicals. If |
method_cov_st |
the method chosen by the user to estimate the autocovariances of the error process. The user has the choice between the methods "fitAR", "spectralproj", "efromovich", "kernel", "select" or "hac". By default, the "fitAR" method is used. |
cov_st |
numeric vector. The estimated autocovariances of the error process. The user can give his own vector. |
Cov_ST |
matrix. It is an argument given by the user if he wants to use his own covariance matrix estimator. |
model_selec |
integer or |
model_max |
integer. Maximal order of the method. |
kernel_fonc |
function. Use this argument if |
block_size |
integer. Size of the bootstrap blocks if |
block_n |
integer. Blocks number to use for the bootstrap if |
plot |
logical. By default, |
The slm
function is based on the architecture of the lm
function.
Models for slm
are specified symbolically.
A typical model has the form response ~ terms
where response
is the (numeric)
response vector and terms
is a series of terms which specifies a linear predictor for response
.
See the documentation of lm
for more details.
slm
returns an object of class
"slm
". The function
summary
is used to obtain and print a summary of the results. The generic accessor functions
coefficients
, effects
, fitted.values
and residuals
extract various
useful features of the value returned by slm
.
An object of class "slm
" is a list containing at least the following components:
method_cov_st |
print the method chosen. |
cov_st |
the estimated autocovariances of the error process. NA if "hac" is used. |
Cov_ST |
if given by the user, the estimated covariance matrix of the error process. NA if "hac" is used. |
model_selec |
the order of the method. |
norm_matrix |
the normalization matrix of the least squares estimator. |
design_qr |
the matrix (X^{t} X)^{-1}. |
coefficients |
a named vector of the estimated coefficients. |
residuals |
the residuals, that is response minus fitted values. |
fitted.values |
the fitted values. |
rank |
the numeric rank of the fitted linear model. |
df.residual |
the number of observations minus the number of variables. |
call |
the matched call. |
terms |
the |
xlevels |
(only where relevant) a record of the levels of the factors used in fitting. |
y |
if requested, the response used. |
x |
if requested, the model matrix used. |
model |
if requested (the default), the model frame used. |
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.
summary
for summaries.
The generic functions coef
, effects
,
residuals
, fitted
, vcov
.
predict
for prediction, including confidence intervals for x' beta, where x' is a new observation of the design.
confint
for confidence intervals of parameters.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data("shan")
slm(shan$PM_Xuhui ~ . , data = shan, method_cov_st = "fitAR", model_selec = -1)
data("co2")
y = as.vector(co2)
x = as.vector(time(co2)) - 1958
reg1 = 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, plot = TRUE)
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 = "kernel", model_selec = -1, model_max = 50, kernel_fonc = triangle,
block_size = 100, block_n = 100)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.