beta_var: Estimate fixed-effect variance for Joint Rank Method (JR) in...
In rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

Description Usage Arguments Details Value Author(s) References

Fixed effect variance estimation for Joint Rank Method (JR). It assumes Compound Symmetric (CS) structure of error terms. For k-level design, there are k-1 intra/inter-class parameters to place in a correlation matrix of errors.

1	beta_var(x, school, tauhat, v1, v2, v3, section, mat)

`x`	Data frame of covariates.
`school`	A vector of cluster.
`tauhat`	This is obtained from Rank-based fitting. `tauhat` here~~
`v1`	This is 1, main diagonal element for correlation matrix of observations. Correlation of an observation with itself is 1.
`v2`	Intra-cluster correlation coefficient.
`v3`	Intra-subcluster correlation coefficient.
`section`	A vector of subclusters, nx1.
`mat`	A matrix of numbers of observations in subclusters. Dimension is Ixmax(number ofsubclusters). Each row indicates one cluster.

Correlation coefficients are obtained using Moment Estimates. See Klole et. al (2009), Bilgic (2012) and HM (2012)

var

The variance of fixed estimated.

Yusuf Bilgic

Y. K. Bilgic. Rank-based estimation and prediction for mixed effects models in nested designs. 2012. URL http://scholarworks.wmich.edu/dissertations/40. Dissertation.

J. Kloke, J. W. McKean and M. Rashid. Rank-based estimation and associated inferences for linear models with cluster correlated errors. Journal of the American Statistical Association, 104(485):384-390, 2009.

T. P. Hettmansperger and J. W. McKean. Robust Nonparametric Statistical Methods. Chapman Hall, 2012.

rlme documentation built on May 2, 2019, 3:47 p.m.