Fitting multivariate regression models with order restrictions
Description
It determines the orderrestricted maximum likelihood estimates and the corresponding log likelihood for the hypothesis of interest. Additionally it gives the (unconstrained) maximum likelihood estimates and the active contraints.
Usage
1 2 
Arguments
formula 
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. 
data 
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called. 
set 
either a character string (see 
direction 
direction of the order constraints 
n 
a (possibly named) vector of sample sizes for each group 
base 
column of the constraint matrix representing a control group 
control 
a list of control arguments; see

Details
This function is just a wrapper for repeated calls of
orlm
with different constraint definitions.
Predefined lists with constraintsets can be constructed with function constrSet
.
Value
an object of class list
Author(s)
Daniel Gerhard and Rebecca M. Kuiper
References
Kuiper R.M., Hoijtink H., Silvapulle M.J. (2011). An Akaiketype Information Criterion for Model Selection Under Inequality Constraints. Biometrika, 98, 495–501.
Kuiper R.M., Hoijtink H., Silvapulle M.J. (2012). Generalization of the OrderRestricted Information Criterion for Multivariate Normal Linear Models. Journal of Statistical Planning and Inference, 142, 24542463. doi:10.1016/j.jspi.2012.03.007.
Kuiper R.M. and Hoijtink H. (submitted). A Fortran 90 Program for the Generalization of the OrderRestricted Information Criterion. Journal of Statictical Software.
See Also
orlm
, constrSet
, goric
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ########################
## Artificial example ##
########################
n < 10
m < c(1,2,4,5,2,1)
nm < length(m)
dat < data.frame(grp=as.factor(rep(1:nm, each=n)),
y=rnorm(n*nm, rep(m, each=n), 1))
(cs < constrSet(table(dat$grp), set="sequence"))
oss < orlmSet(y ~ grp1, data=dat, set=cs)
oss
# the same as:
oss < orlmSet(y ~ grp1, data=dat, set="sequence")
