Description Usage Arguments Details Value Note See Also Examples
clme_em_fixed
performs a constrained EM algorithm for linear fixed effects models.
clme_em_mixed
performs a constrained EM algorithm for linear mixed effects models.
clme_em
is the general function, it will call the others.
These Expectationmaximization (EM) algorithms estimate model parameters and
compute a test statistic.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  clme_em_fixed(Y, X1, X2 = NULL, U = NULL, Nks = dim(X1)[1],
Qs = dim(U)[2], constraints, mq.phi = NULL, tsf = lrt.stat,
tsf.ind = w.stat.ind, mySolver = "LS", em.iter = 500, em.eps = 1e04,
all_pair = FALSE, dvar = NULL, verbose = FALSE, ...)
clme_em_mixed(Y, X1, X2 = NULL, U = NULL, Nks = dim(X1)[1],
Qs = dim(U)[2], constraints, mq.phi = NULL, tsf = lrt.stat,
tsf.ind = w.stat.ind, mySolver = "LS", em.iter = 500, em.eps = 1e04,
all_pair = FALSE, dvar = NULL, verbose = FALSE, ...)
clme_em(Y, X1, X2 = NULL, U = NULL, Nks = nrow(X1), Qs = ncol(U),
constraints, mq.phi = NULL, tsf = lrt.stat, tsf.ind = w.stat.ind,
mySolver = "LS", em.iter = 500, em.eps = 1e04, all_pair = FALSE,
dvar = NULL, verbose = FALSE, ...)

Y 
Nx1 vector of response data. 
X1 
Nxp1 design matrix. 
X2 
optional Nxp2 matrix of covariates. 
U 
optional Nxc matrix of random effects. 
Nks 
optional Kx1 vector of group sizes. 
Qs 
optional Qx1 vector of group sizes for random effects. 
constraints 
list containing the constraints. See Details. 
mq.phi 
optional MINQUE estimates of variance parameters. 
tsf 
function to calculate the test statistic. 
tsf.ind 
function to calculate the test statistic for individual constrats. See Details for further information. 
mySolver 
solver to use in isotonization (passed to 
em.iter 
maximum number of iterations permitted for the EM algorithm. 
em.eps 
criterion for convergence for the EM algorithm. 
all_pair 
logical, whether all pairwise comparisons should be considered (constraints will be ignored). 
dvar 
fixed values to replace bootstrap variance of 0. 
verbose 
if 
... 
space for additional arguments. 
Argument constraints
is a list including at least the elements A
, B
, and Anull
. This argument can be generated by function create.constraints
.
The function returns a list with the elements:
theta
coefficient estimates.
theta.null
vector of coefficient estimates under the null hypothesis.
ssq
estimate of residual variance term(s).
tsq
estimate of variance components for any random effects.
cov.theta
covariance matrix of the unconstrained coefficients.
ts.glb
test statistic for the global hypothesis.
ts.ind
test statistics for each of the constraints.
mySolver
the solver used for isotonization.
There are few error catches in these functions. If only the EM estimates are desired,
users are recommended to run clme
setting nsim=0
.
By default, homogeneous variances are assumed for the residuals and (if included)
random effects. Heterogeneity can be induced using the arguments Nks
and Qs
,
which refer to the vectors (n1, n2 ,... , nk) and
(c1, c2 ,... , cq), respectively. See
CLMEpackage
for further explanation the model and these values.
See w.stat
and lrt.stat
for more details on using custom
test statistics.
CLMEpackage
clme
create.constraints
lrt.stat
w.stat
1 2 3 4 5 6 7 8 9 10 11 12  data( rat.blood )
model_mats < model_terms_clme( mcv ~ time + temp + sex + (1id), data = rat.blood )
Y < model_mats$Y
X1 < model_mats$X1
X2 < model_mats$X2
U < model_mats$U
cons < list(order = "simple", decreasing = FALSE, node = 1 )
clme.out < clme_em(Y = Y, X1 = X1, X2 = X2, U = U, constraints = cons)

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