Correct the bias of the ML estimate of the variance and compute the first derivative of the information matrix.
sCorrect(object, ...)# S3 method for lm
sCorrect(object, adjust.Omega = TRUE, adjust.n = TRUE,
score = TRUE, df = TRUE, numeric.derivative = FALSE, param = NULL,
data = NULL, tol = 1e-05, n.iter = 20, trace = 0, ...)
# S3 method for lm2
sCorrect(object, ...)
# S3 method for gls
sCorrect(object, cluster, adjust.Omega = TRUE,
adjust.n = TRUE, score = TRUE, df = TRUE, numeric.derivative = FALSE,
param = NULL, data = NULL, tol = 1e-05, n.iter = 20, trace = 0, ...)
# S3 method for gls2
sCorrect(object, ...)
# S3 method for lme
sCorrect(object, cluster, adjust.Omega = TRUE,
adjust.n = TRUE, score = TRUE, df = TRUE, numeric.derivative = FALSE,
param = NULL, data = NULL, tol = 1e-05, n.iter = 20, trace = 0, ...)
# S3 method for lme2
sCorrect(object, ...)
# S3 method for lvmfit
sCorrect(object, adjust.Omega = TRUE, adjust.n = TRUE,
score = TRUE, df = TRUE, numeric.derivative = FALSE, param = NULL,
data = NULL, tol = 1e-05, n.iter = 20, trace = 0, ...)
# S3 method for lvmfit2
sCorrect(object, ...)
sCorrect(x, ...) <- value
# S3 method for lm
sCorrect(x, ...) <- value
# S3 method for lm2
sCorrect(x, ...) <- value
# S3 method for gls
sCorrect(x, ...) <- value
# S3 method for gls2
sCorrect(x, ...) <- value
# S3 method for lme
sCorrect(x, ...) <- value
# S3 method for lme2
sCorrect(x, ...) <- value
# S3 method for lvmfit
sCorrect(x, ...) <- value
# S3 method for lvmfit2
sCorrect(x, ...) <- value
a gls, lme, or lvm object.
[internal] only used by the generic method or by the <- methods.
[logical] should the standard errors of the coefficients be corrected for small sample bias?
[logical] should the correction for the degree of freedom be performed?
[internal] export the score.
[logical] should the degree of freedoms of the Wald statistic be computed using the Satterthwaite correction?
Otherwise the degree of freedoms are set to Inf, i.e. a normal distribution is used instead of a Student's t distribution when computing the p-values.
[logical] should a numerical derivative be used to compute the first derivative of the information matrix? Otherwise an analytic formula is used.
[numeric vector, optional] the values of the parameters at which to perform the correction.
[data.frame, optional] the dataset relative to which the correction should be performed.
[numeric >0] the minimum absolute difference between two estimation of the small sample bias. Below this value, the algorithm used to estimate the bias stop.
[integer >0] the maximum number of iterations used to estimate the small sample bias of the residual variance-covariance matrix.
[logical] should the execution of the function be traced.
[integer vector] the grouping variable relative to which the observations are iid.
Only required for gls models with no correlation argument.
[logical] value for the arguments adjust.Omega and adjust.n.
The argument value is equivalent to the argument bias.correct of the function summary2.