clme_em_fixed {CLME} | R Documentation |
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 Expectation-maximization (EM) algorithms estimate model parameters and
compute a test statistic.
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 = 1e-04, 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 = 1e-04, 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 = 1e-04, 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
CLME-package
for further explanation the model and these values.
See w.stat
and lrt.stat
for more details on using custom
test statistics.
CLME-package
clme
create.constraints
lrt.stat
w.stat
data( rat.blood ) model_mats <- model_terms_clme( mcv ~ time + temp + sex + (1|id), 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)