Constrained inference for linear fixed or mixed effects models using distribution-free bootstrap methodology
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a formula expression. The constrained effect must come before any unconstrained covariates on the right-hand side of the expression. The constrained effect should be an ordered factor.
data frame containing the variables in the model.
optional vector of group levels for residual variances. Data should be sorted by this value.
optional list containing the constraints. See Details for further information.
function to calculate the test statistic.
function to calculate the test statistic for individual constrats. See Details for further information.
solver to use in isotonization (passed to
logical, whether all pairwise comparisons should be considered (constraints will be ignored).
optional. Vector of 3 logicals. The first causes printing of iteration step, the second two are passed as the
space for additional arguments.
If any random effects are included, the function computes MINQUE estimates of variance components. After,
clme_em is run to obtain the observed values. If
nsim>0, a bootstrap test is performed
For the argument
levels the first list element should be the column index (in
data) of the
constrained effect. The second element should be the true order of the levels.
The output of
clme is an object of the class
clme, which is list with elements:
theta estimates of theta coefficients
theta estimates of theta_0 coefficients under the null hypothesis
ssq estimate of residual variance(s), sigma.i^2.
tsq estimate of random effects variance component(s), tau.i^2.
cov.theta the unconstrained covariance matrix of theta
ts.glb test statistic for the global hypothesis.
ts.ind test statistics for each of the constraints.
mySolver the solver used for isotonization.
constraints list containing the constraints (
A) and the contrast for the global test (
dframe data frame containing the variables in the model.
residuals matrix containing residuals. For mixed models three types of residuals are given.
random.effects estimates of random effects.
gfix group sample sizes for residual variances.
gran group sizes for random effect variance components.
gfix_group group names for residual variances.
formula the formula used in the model.
call the function call.
order list describing the specified or estimated constraints.
P1 the number of constrained parameters.
nsim the number of bootstrap simulations used for inference.
constraints is a list containing the order restrictions. The elements are
B, though not all are necessary.
The function can calculate the last two for default orders (simple, umbrella, or simple tree). For
constraints should be a list containing any subset of
descending. See the figure below for a depiction of these values; the
node of the simple tree orders (middle column) is 1, and the
node for the
umbrella orders (right column) is 3. These may be vectors (e.g. order=('simple','umbrella') ).
If any of these three are missing, the function will test for all possible values of the missing
element(s), excluding simple tree.
For non-default orders, the elements
B should be provided.
A is an
r x 2 matrix (where r is the number of linear constraints, 0 < r.
Each row should contain two indices, the first element is the index of the lesser coefficient, the
second element is the index of the greater coefficient. So a row of (1,2) corresponds
to the constraint theta_1 <= theta_2, and a row (4,3)
corresponds to the constraint theta_4 <= theta_3, etc. Element
should hold similar contrasts, specifically those needed for calculating the Williams' type test
B is only needed if
tsf is a function to calculate the desired test statistic. The default function
calculates likelihood ratio type test statistic. A Williams type test statistic, which is the maximum
of the test statistic over the constraints in
constraints\$B, is also available, and custom
functions may be defined. See
w.stat for details.
By default, homogeneity of variances is assumed for residuals (e.g.,
gfix does not define groups)
and for each random effect.
Some values can be passed to
clme that are not used in this function. For instance,
nsim can each be passed as an argument here, and
use these values.
Jelsema, C. M. and Peddada, S. D. (2016). CLME: An R Package for Linear Mixed Effects Models under Inequality Constraints. Journal of Statistical Software, 75(1), 1-32. doi:10.18637/jss.v075.i01
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