minque {CLME} R Documentation

## MINQUE Algorithm

### Description

Algorithm to obtain MINQUE estimates of variance components of a linear mixed effects model.

### Usage

```minque(
Y,
X1,
X2 = NULL,
U = NULL,
Nks = dim(X1),
Qs = dim(U),
mq.eps = 1e-04,
mq.iter = 500,
verbose = FALSE,
...
)
```

### Arguments

 `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. `mq.eps` criterion for convergence for the MINQUE algorithm. `mq.iter` maximum number of iterations permitted for the MINQUE algorithm. `verbose` if `TRUE`, function prints messages on progress of the MINQUE algorithm. `...` space for additional arguments.

### Details

By default, the model assumes homogeneity of variances for both the residuals and the random effects (if included). See the Details in `clme_em` for more information on how to use the arguments `Nks` and `Qs` to permit heterogeneous variances.

### Value

The function returns a vector of the form (tau1^2, tau2^2, …, tauq^2, sigma1^2,sigma2^2,…, sigmak^2)'. If there are no random effects, then the output is just (sigma1^2,sigma2^2,…, sigmak^2)'.

### Note

This function is called by several other function in CLME to obtain estimates of the random effect variances. If there are no random effects, they will not call `minque`.

### Examples

```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

# No covariates or random effects
minque(Y = Y, X1 = X1 )

# Include covariates and random effects
minque(Y = Y, X1 = X1, X2 = X2, U = U )

```

[Package CLME version 2.0-12 Index]