summary.HLfit {spaMM} | R Documentation |
Summary and print methods for fit and test results.
Description
Summary and print methods for results from HLfit or related functions. summary
may also be used as an extractor (see e.g. beta_table
).
Usage
## S3 method for class 'HLfit'
summary(object, details=FALSE, max.print=100L, verbose=TRUE, ...)
## S3 method for class 'HLfitlist'
summary(object, ...)
## S3 method for class 'fixedLRT'
summary(object, verbose=TRUE, ...)
## S3 method for class 'HLfit'
print(x,...)
## S3 method for class 'HLfitlist'
print(x,...)
## S3 method for class 'fixedLRT'
print(x,...)
Arguments
object |
An object of class |
x |
The return object of HLfit or related functions. |
verbose |
For |
max.print |
Controls |
details |
A vector with elements controlling whether to print some obscure details. Element |
... |
further arguments passed to or from other methods. |
Details
The random effect terms of the linear predictor are of the form ZLv. In particular, for random-coefficients models (i.e., including random-effect terms such as (z|group)
specifying a random-slope component), correlated random effects are represented as b = Lv for some matrix L, and where the elements of v are uncorrelated. In the output of the fit, the Var.
column gives the
variances of the correlated effects, b=Lv. The Corr.
column(s) give their correlation(s). If details
is TRUE, estimates and SEs of the (log) variances of the elements of v are reported as for other random effects in the Estimate
and cond.SE.
columns of the table of lambda coefficients. However, this non-default output is potentially misleading as the elements of v cannot generally be assigned to specific terms (such as intercept and slope) of the random-effect formula, and the representation of b as Lv is not unique.
Value
The return value is a list whose elements may be subject to changes, but two of them can be considered stable, and are thus part of the API: the beta_table
and lambda_table
which are the displayed tables for the coefficients of fixed effects and random-effect variances.
Examples
## see examples of fitme() or corrHLfit() usage