RRmixed  R Documentation 
Uses the package lme4
to fit a generalized linear mixed model
(GLMM) with an adjusted link funciton.
RRmixed(formula, data, model, p, const = 1e04, adjust_control = FALSE, ...)
formula 
twosided formula including random and fixed effects (see
below or 
data 
an optional data frame with variables named in formula 
model 
type of RR design. Only 1group RR designs are supported at the
moment (i.e., 
p 
randomization probability 
const 
the RR link function is not defined for small and/or large
probabilities (the boundaries depend on 
adjust_control 
whether to adjust the control arguments for

... 
further arguments passed to 
Some examples for formula:
random intercept:
response ~ covariate + (1  group)
random slope:
response ~ covariate + (0 + covariate  group)
both random slope and intercept:
response ~ covariate +(covariate  group)
level2 predictor (must have constant values within groups!):
response ~ lev2 + (1group)
Note that parameter estimation will be unstable and might fail if the
observed responses are not in line with the model. For instance, a
ForcedResponse model (model="FR"
) with p=c(0,1/4)
requires
that expected probabilities for responses are in the interval [.25,1.00]. If
the observed proportion of responses is very low (<<.25), intercepts will be
estimated to be very small (<<0) and/or parameter estimation might fail. See
glmer
for setting better starting values and
lmerControl
for further options to increase stability.
an object of class glmerMod
van den Hout, A., van der Heijden, P. G., & Gilchrist, R. (2007). The Logistic Regression Model with Response Variables Subject to Randomized Response. Computational Statistics & Data Analysis, 51, 6060–6069.
# generate data with a level1 predictor set.seed(1234) d < data.frame( group = factor(rep(LETTERS[1:20], each = 50)), cov = rnorm(20 * 50) ) # generate dependent data based on logistic model (random intercept): d$true < simulate(~ cov + (1  group), newdata = d, family = binomial(link = "logit"), newparams = list( beta = c("(Intercept)" = .5, cov = 1), theta = c("group.(Intercept)" = .8) ) )[[1]] # scramble responses using RR: model < "FR" p < c(true0 = .1, true1 = .2) d$resp < RRgen(model = "FR", p = p, trueState = d$true)$response # fit model: mod < RRmixed(resp ~ cov + (1  group), data = d, model = "FR", p = p) summary(mod)
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