Description Usage Arguments Details Value References Examples
Estimating the variance components under the multivariate mixed effects model using REML methods
1 2 |
fml |
a two-sided linear formula object describing both the fixed-effects and random-effects parts of the model, with the response on the left of a ~ operator. For univariate response, put variable name directly; for multivariate responses combine variables using concatenate operator, for example, for bivariate responses, c(var1, var2). The predictor terms are separated by + operators, on the right. Random-effects terms are distinguished by vertical bars '|' separating expressions for design matrices from grouping factors. |
data |
data frame containing the variables named in formula. |
factor_X |
(logical) indicating whether predictor is a factor or continuous. By default is TRUE |
T.start |
the starting matrix for the variance covariance matrix of the block random effects, it has to be positive definite q by q symmetric matrix. |
E.start |
the starting matrix for the variance covariance matrix of the block random effects, it has to be positive definite q by q symmetric matrix. |
maxit |
the maximum number of iterations |
tol |
the convergence tolerance |
Suppose n observational units, q variates, p fixed effects coefficients and s random effects units. The model supports multivariate mixed effects model for one-way randomized block design with equal design matrices:
Y = XB + ZU + E
where Y is n by q response variates matrix; X is n by p design matrix for the fixed effects; B is p by q coefficients matrix for the fixed effects; Z is n by s design matrix for the random effects; U is s by q matrix for the random effects; E is n by q random errors matrix.
The model also supports simple OLS multivariate regression:
y = Xb + Zu + e
where y is n by 1 response vector; b is p by 1 coefficients vector for the fixed effects; u is s by 1 matrix for the random effects.
The function returns a list with the following objects:
T.estimates
is the estimated variance covariance components of the variance covariance matrix of the block random effects
E.estimates
is the estimated variance covariance components of the variance covariance matrix of the residuals
VCOV
is the asymptotic
dispersion matrix of the estimated variance covariance components for the block random effects and the residuals.
Meyer, K. "Maximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices." Biometrics 1985: 153,165.
1 2 3 4 5 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.