| elt | R Documentation |
Tests a linear hypothesis with various calibration options.
## S4 method for signature 'EL'
elt(
object,
rhs = NULL,
lhs = NULL,
alpha = 0.05,
calibrate = "chisq",
control = NULL
)
object |
An object that inherits from EL. |
rhs |
A numeric vector or a column matrix for the right-hand side of
hypothesis, with as many entries as the rows in |
lhs |
A numeric matrix or a vector (treated as a row matrix) for the
left-hand side of a hypothesis. Each row gives a linear combination of the
parameters in |
alpha |
A single numeric for the significance level. Defaults to |
calibrate |
A single character representing the calibration method. It
is case-insensitive and must be one of |
control |
An object of class ControlEL constructed by
|
elt() performs the constrained minimization of l(\theta)
described in CEL. rhs and lhs cannot be both NULL. For
non-NULL lhs, it is required that lhs have full row rank
q \leq p and p be equal to the number of parameters in the
object.
Depending on the specification of rhs and lhs, we have the following
three cases:
If both rhs and lhs are non-NULL, the constrained minimization
is performed with the right-hand side r and the left-hand side
L as
\inf_{\theta: L\theta = r} l(\theta).
If rhs is NULL, r is set to the zero vector as
\inf_{\theta: L\theta = 0} l(\theta).
If lhs is NULL, L is set to the identity matrix and the
problem reduces to evaluating at r as l(r).
calibrate specifies the calibration method used. Four methods are
available: "ael" (adjusted empirical likelihood calibration), "boot"
(bootstrap calibration), "chisq" (chi-square calibration), and "f"
(F calibration). When lhs is not NULL, only "chisq" is
available. "f" is applicable only to the mean parameter. The an slot in
control applies specifically to "ael", while the nthreads, seed,
and B slots apply to "boot".
An object of class of ELT. If lhs is non-NULL, the
optim slot corresponds to that of CEL. Otherwise, it
corresponds to that of EL.
Adimari G, Guolo A (2010). “A Note on the Asymptotic Behaviour of Empirical Likelihood Statistics.” Statistical Methods & Applications, 19(4), 463–476. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10260-010-0137-9")}.
Chen J, Variyath AM, Abraham B (2008). “Adjusted Empirical Likelihood and Its Properties.” Journal of Computational and Graphical Statistics, 17(2), 426–443.
Kim E, MacEachern SN, Peruggia M (2024). “melt: Multiple Empirical Likelihood Tests in R.” Journal of Statistical Software, 108(5), 1–33. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v108.i05")}.
Qin J, Lawless J (1995). “Estimating Equations, Empirical Likelihood and Constraints on Parameters.” Canadian Journal of Statistics, 23(2), 145–159. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/3315441")}.
EL, ELT, elmt(), el_control()
## Adjusted empirical likelihood calibration
data("precip")
fit <- el_mean(precip, 32)
elt(fit, rhs = 100, calibrate = "ael")
## Bootstrap calibration
elt(fit, rhs = 32, calibrate = "boot")
## F calibration
elt(fit, rhs = 32, calibrate = "f")
## Test of no treatment effect
data("clothianidin")
contrast <- matrix(c(
1, -1, 0, 0,
0, 1, -1, 0,
0, 0, 1, -1
), byrow = TRUE, nrow = 3)
fit2 <- el_lm(clo ~ -1 + trt, clothianidin)
elt(fit2, lhs = contrast)
## A symbolic description of the same hypothesis
elt(fit2, lhs = c(
"trtNaked - trtFungicide",
"trtFungicide - trtLow",
"trtLow - trtHigh"
))
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