Description Usage Arguments Details Author(s) References See Also Examples
Standard methods for computing on olmm
objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  ## S3 method for class 'olmm'
anova(object, ...)
## S3 method for class 'olmm'
coef(object, which = c("all", "fe"), ...)
## S3 method for class 'olmm'
fixef(object, which = c("all", "ce", "ge"), ...)
## S3 method for class 'olmm'
model.matrix(object, which = c("fe", "fece", "fege",
"re", "rece", "rege"), ...)
## S3 method for class 'olmm'
neglogLik2(object, ...)
## S3 method for class 'olmm'
ranef(object, norm = FALSE, ...)
## S3 method for class 'olmm'
ranefCov(object, ...)
## S3 method for class 'olmm'
simulate(object, nsim = 1, seed = NULL,
newdata = NULL, ranef = TRUE, ...)
## S3 method for class 'olmm'
terms(x, which = c("fece", "fege", "rece", "rege"), ...)
## S3 method for class 'olmm'
VarCorr(x, sigma = 1., ...)
## S3 method for class 'olmm'
weights(object, level = c("observation", "subject"), ...)

object, x 
an 
which 
optional character string. For 
level 
character string. Whether the results should be on the
observation level ( 
norm 
logical. Whether residuals should be divided by their standard deviation. 
nsim 
number of response vectors to simulate. Defaults to 1. 
seed 
an object specifying if and how the random number
generator should be initialized. See 
newdata 
a data frame with predictor variables. 
ranef 
either a logical or a matrix (see

sigma 
ignored but obligatory argument from original generic. 
... 
potential further arguments passed to methods. 
anova
implements loglikelihood ratio tests for model
comparisons, based on the marginal likelihood. At the time being,
at least two models must be assigned.
neglogLik2
is the marginal maximum likelihood of the
fitted model times minus 2.
ranefCov
extracts the variancecovariance matrix of
the random effects. Similarly, VarCorr
extracts the
estimated variances, standard deviations and correlations of the
random effects.
resid
extracts the residuals of Li and Sheperd
(2012). By default, the marginal outcome distribution is used to
compute these residuals. The conditional residuals can be computed by
assigning ranef = TRUE
as a supplementary argument.
Further, undocumented methods are deviance
,
extractAIC
, fitted
,
formula
, getCall
,
logLik
, model.frame
,
nobs
, update
, vcov
.
The anova
implementation is based on codes of the
lme4 package. The authors are grateful for these codes.
Reto Buergin
Agresti, A. (2010). Analysis of Ordinal Categorical Data (2 ed.). New Jersey, USA: John Wiley & Sons.
Tutz, G. (2012). Regression for Categorical Data. New York, USA: Cambridge Series in Statistical and Probabilistic Mathematics.
Li, C. and B. E. Sheperd (2012). A New Residual for Ordinal Outcomes, Biometrika, 99(2), 437–480.
Bates, D., M. Maechler, B. M. Bolker and S. Walker (2015). Fitting Linear MixedEffects Models Using lme4, Journal of Statistical Software, 67(1), 1–48.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  ##  #
## Example: Schizophrenia (see also example of 'olmm')
##  #
data(schizo)
schizo < schizo[1:181,]
schizo$id < droplevels(schizo$id)
## anova comparison
## 
## fit two alternative models for the 'schizo' data
model.0 < olmm(imps79o ~ tx + sqrt(week) + re(1id), schizo)
model.1 < olmm(imps79o ~ tx + sqrt(week)+tx*sqrt(week)+re(1id),schizo)
anova(model.0, model.1)
## simulate responses
## 
## simulate responses based on estimated random effects
simulate(model.0, newdata = schizo[1, ], ranef = TRUE, seed = 1)
simulate(model.0, newdata = schizo[1, ], seed = 1,
ranef = ranef(model.0)[schizo[1, "id"],,drop=FALSE])
## simulate responses based on simulated random effects
newdata < schizo[1, ]
newdata$id < factor("123456789")
simulate(model.0, newdata = newdata, ranef = TRUE)
## other methods
## 
coef(model.1)
fixef(model.1)
head(model.matrix(model.1, "fege"))
head(weights(model.1))
ranefCov(model.1)
head(resid(model.1))
terms(model.1, "fege")
VarCorr(model.1)
head(weights(model.1, "subject"))

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