See fitParaIND
, fitParaAR1
for details of the model explanations.
rNBME.R(
gdist = "G", n = 200, sn = 5, th = exp(1.3),
u1 = rep(1.5, 5), u2 = rep(1.5, 5),
a = exp(-0.5),d=NULL, othrp = list(u.n = 3, s.n = 0.5, p.mx = 0.05, sh.mx = NA)
)
The distribution of the random effect term $G[i]$.
If gdist="G"
, $G[i]$ is from the gamma distribution.
If gdist="N"
, $G[i]$ is from the log normal distribution.
If gdist="U"
, $G[i]$ (on the log scale) is from the uniform distribution.
If gdist="GN"
, $G[i]$ is from the mixture of the gamma distribution and the normal distribution.
If the generated values are negative, they are truncated to zero.
If gdist="NoN"
, $G[i]$ is sampled from the pre-specified vector othrp
with replacement.
gdist="G"
, th
is a scale
parameter of the gamma distribution.If gdist="N"
or gdist=="U"
, th
is $Var(G[i])$.
If gdist="GN"
, see details.
If gdist="NoN"
, this parameter is not used.
A vector of length sn, specifying the mean of the treatment group 1 $E(Y[ij])$ = u1[j]
.
u2[j]
.
The dispersion parameter $\alpha$ of the negative binomial mixed-effect independent model. See description in lmeNB.
d=NULL
, generate data from the independent model.
If d
is a scalar between 0 and 1, then d is $delta$ in the AR(1) model, and generate datasets from the AR(1) model.
gdist="GN"
, parameters for the GN
option. See details.
If gdist="NoN"
, othrp
is a vector, containing a sample of $G[i]$, which is treated as a population and $G[i]$ is resampled.
n*sn
containing patient IDs: rep(1:n,each=sn)
n*sn
containing the indicies of time points: rep(1:sn, n)
n*sn
containing the indicies of the treatment groupsn*sn
containing generated response countsn*sn
containing generated random effect termsThe generated datasets have equal number of scans per person.
The number of patients in the two groups are the same.
If gdist=="GN"
, datasets are generated from:
othrp$p.mx
*N(mean
=othrp$u.n
,s.d
=othrp$s.n
) + (1-othrp$p.mx
)*gamma(scale=th,shape)
,
where shape
of the gamma distribution is chosen to ensure $E(G[i])=1$.
lmeNB
,The functions to fit related models:
fitParaIND
,
fitParaAR1
,
fitSemiIND
,
fitSemiAR1
,
The subroutines of index.batch
to compute the conditional probability index:
jCP.ar1
,
CP1.ar1
,
MCCP.ar1
,
CP.ar1.se
,
CP.se
,
jCP
,
## Not run:
# ## See the examples in help files of fitParaIND, fitParaAR1, fitSemiIND, fitSemiAR1 and lmeNB
#
# ## End(Not run)
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