glmmkin: Fit generalized linear mixed model with known relationship...

In GMMAT: Generalized Linear Mixed Model Association Tests

Fit generalized linear mixed model with known relationship matrices

Description

Fit a generalized linear mixed model with a random intercept, or a random intercept and an optional random slope of time effect for longitudinal data. The covariance matrix of the random intercept is proportional to a known relationship matrix (e.g. kinship matrix in genetic association studies). Alternatively, it can be a variance components model with multiple random effects, and each component has a known relationship matrix.

Usage

glmmkin(fixed, data = parent.frame(), kins = NULL, id, random.slope = NULL, 
	groups = NULL, family = binomial(link = "logit"), method = "REML", 
	method.optim = "AI", maxiter = 500, tol = 1e-5, taumin = 1e-5, 
	taumax = 1e5, tauregion = 10, verbose = FALSE, ...)

Arguments

fixed	an object of class formula (or one that can be coerced to that class): a symbolic description of the fixed effects model to be fitted.
data	a data frame or list (or object coercible by as.data.frame to a data frame) containing the variables in the model.
kins	a known positive semi-definite relationship matrix (e.g. kinship matrix in genetic association studies) or a list of known positive semi-definite relationship matrices. The rownames and colnames of these matrices must at least include all samples as specified in the id column of the data frame data. If not provided, glmmkin will switch to the generalized linear model with no random effects (default = NULL).
id	a column in the data frame data, indicating the id of samples. When there are duplicates in id, the data is assumed to be longitudinal with repeated measures.
random.slope	an optional column indicating the random slope for time effect used in a mixed effects model for cross-sectional data with related individuals, and longitudinal data. It must be included in the names of data. There must be duplicates in id and method.optim must be "AI" (default = NULL).
groups	an optional categorical variable indicating the groups used in a heteroscedastic linear mixed model (allowing residual variances in different groups to be different). This variable must be included in the names of data, and family must be "gaussian" and method.optim must be "AI" (default = NULL).
family	a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.)
method	method of fitting the generalized linear mixed model. Either "REML" or "ML" (default = "REML").
method.optim	optimization method of fitting the generalized linear mixed model. Either "AI", "Brent" or "Nelder-Mead" (default = "AI").
maxiter	a positive integer specifying the maximum number of iterations when fitting the generalized linear mixed model (default = 500).
tol	a positive number specifying tolerance, the difference threshold for parameter estimates below which iterations should be stopped (default = 1e-5).
taumin	the lower bound of search space for the variance component parameter \tau (default = 1e-5), used when method.optim = "Brent". See Details.
taumax	the upper bound of search space for the variance component parameter \tau (default = 1e5), used when method.optim = "Brent". See Details.
tauregion	the number of search intervals for the REML or ML estimate of the variance component parameter \tau (default = 10), used when method.optim = "Brent". See Details.
verbose	a logical switch for printing detailed information (parameter estimates in each iteration) for testing and debugging purpose (default = FALSE).
...	additional arguments that could be passed to glm.

Details

Generalized linear mixed models (GLMM) are fitted using the penalized quasi-likelihood (PQL) method proposed by Breslow and Clayton (1993). Generally, fitting a GLMM is computationally expensive, and by default we use the Average Information REML algorithm (Gilmour, Thompson and Cullis, 1995; Yang et al., 2011) to fit the model. If only one relationship matrix is specified (kins is a matrix), iterations may be accelerated using the algorithm proposed by Zhou and Stephens (2012) for linear mixed models. An eigendecomposition is performed in each outer iteration and the estimate of the variance component parameter \tau is obtained by maximizing the profiled log restricted likelihood (or likelihood) in a search space from taumin to taumax, equally divided into tauregion intervals on the log scale, using Brent's method (1973). If kins is a list of matrices and method = "Nelder-Mead", iterations are performed as a multi-dimensional maximization problem solved by Nelder and Mead's method (1965). It can be very slow, and we do not recommend using this method unless the likelihood function is badly behaved. Both Brent's method and Nelder and Mead's method are derivative-free. When the Average Information REML algorithm fails to converge, a warning message is given and the algorithm is default to derivative-free approaches: Brent's method if only one relationship matrix is specified, Nelder and Mead's method if more than one relationship matrix is specified.

For longitudinal data (with duplicated id), two types of models can be applied: random intercept only models, and random intercept and random slope models. The random intercept only model is appropriate for analyzing repeated measures with no time trends, and observations for the same individual are assumed to be exchangeable. The random intercept and random slope model is appropriate for analyzing longitudinal data with individual-specific time trends (therefore, a random slope for time effect). Typically, the time effect should be included in the model as a fixed effect covariate as well. Covariances of the random intercept and the random slope are estimated.

For multiple phenotype analysis, formula recognized by lm, such as cbind(y1, y2, y3) ~ x1 + x2, can be used in fixed as fixed effects. For each matrix in kins, variance components corresponding to each phenotype, as well as their covariance components, will be estimated. Currently, family must be "gaussian" and method.optim must be "AI".

Value

theta	a vector or a list of variance component parameter estimates. See below. For cross-sectional data, if kins is not provided (unrelated individuals), theta is the dispersion parameter estimate from the generalized linear model; if kins is a matrix and groups is not provided, theta is a length 2 vector, with theta[1] being the dispersion parameter estimate and theta[2] being the variance component parameter estimate for kins; if kins is a list and groups is not provided, theta is a length 1 + length(kins) vector, with theta[1] being the dispersion parameter estimate and theta[2:(1 + length(kins))] being the variance component parameter estimates, corresponding to the order of matrices in the list kins; if kins is a matrix and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 1 + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group and theta[1 + n.groups] being the variance component parameter estimate for kins; if kins is a list and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length length(kins) + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group and theta[(1 + n.groups):(length(kins) + n.groups)] being the variance component parameter estimates, corresponding to the order of matrices in the list kins. For longitudinal data (with duplicated id) in a random intercept only model, if kins is not provided (unrelated individuals) and groups is not provided, theta is a length 2 vector, with theta[1] being the dispersion parameter estimate and theta[2] being the variance component parameter estimate for the random individual effects; if kins is a matrix and groups is not provided, theta is a length 3 vector, with theta[1] being the dispersion parameter estimate, theta[2] being the variance component parameter estimate for the random individual effects attributable to relatedness from kins, and theta[3] being the variance component parameter estimate for the random individual effects not attributable to relatedness from kins; if kins is a list and groups is not provided, theta is a length 2 + length(kins) vector, with theta[1] being the dispersion parameter estimate, theta[2:(1 + length(kins))] being the variance component parameter estimates for the random individual effects attributable to relatedness from kins, corresponding to the order of matrices in the list kins, and theta[2 + length(kins)] being the variance component parameter estimate for the random individual effects not attributable to relatedness from kins; if kins is not provided (unrelated individuals) and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 1 + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group and theta[1 + n.groups] being the variance component parameter estimate for the random individual effects; if kins is a matrix and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 2 + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group, theta[1 + n.groups] being the variance component parameter estimate for the random individual effects attributable to relatedness from kins, and theta[2 + n.groups] being the variance component parameter estimate for the random individual effects not attributable to relatedness from kins; if kins is a list and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 1 + length(kins) + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group, theta[(1 + n.groups):(length(kins) + n.groups)] being the variance component parameter estimates for the random individual effects attributable to relatedness from kins, corresponding to the order of matrices in the list kins, and theta[1 + length(kins) + n.groups] being the variance component parameter estimate for the random individual effects not attributable to relatedness from kins. For longitudinal data (with duplicated id) in a random intercept and random slope (for time effect) model, if kins is not provided (unrelated individuals) and groups is not provided, theta is a length 4 vector, with theta[1] being the dispersion parameter estimate, theta[2] being the variance component parameter estimate for the random individual effects of the intercept, theta[3] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope, and theta[4] being the variance component parameter estimate for the random individual effects of the time slope; if kins is a matrix and groups is not provided, theta is a length 7 vector, with theta[1] being the dispersion parameter estimate, theta[2] being the variance component parameter estimate for the random individual effects of the intercept attributable to relatedness from kins, theta[3] being the variance component parameter estimate for the random individual effects of the intercept not attributable to relatedness from kins, theta[4] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope attributable to relatedness from kins, theta[5] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope not attributable to relatedness from kins, theta[6] being the variance component parameter estimate for the random individual effects of the time slope attributable to relatedness from kins, and theta[7] being the variance component parameter estimate for the random individual effects of the time slope not attributable to relatedness from kins; if kins is a list and groups is not provided, theta is a length 4 + 3 * length(kins) vector, with theta[1] being the dispersion parameter estimate, theta[2:(1 + length(kins))] being the variance component parameter estimates for the random individual effects of the intercept attributable to relatedness from kins, corresponding to the order of matrices in the list kins, theta[2 + length(kins)] being the variance component parameter estimate for the random individual effects of the intercept not attributable to relatedness from kins, theta[(3 + length(kins)):(2 + 2 * length(kins))] being the covariance estimates for the random individual effects of the intercept and the random individual effects of the time slope attributable to relatedness from kins, corresponding to the order of matrices in the list kins, theta[3 + 2 * length(kins)] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope not attributable to relatedness from kins, theta[(4 + 2 * length(kins)):(3 + 3 * length(kins))] being the variance component parameter estimates for the random individual effects of the time slope attributable to relatedness from kins, corresponding to the order of matrices in the list kins, theta[4 + 3 * length(kins)] being the variance component parameter estimate for the random individual effects of the time slope not attributable to relatedness from kins; if kins is not provided (unrelated individuals) and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 3 + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group, theta[1 + n.groups] being the variance component parameter estimate for the random individual effects of the intercept, theta[2 + n.groups] being the covariance estimate for the random individual effect of the intercept and the random individual effects of the time slope, and theta[3 + n.groups] being the variance component parameter estimate for the random individual effects of the time slope; if kins is a matrix and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 6 + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group, theta[1 + n.groups] being the variance component parameter estimate for the random individual effects of the intercept attributable to relatedness from kins, theta[2 + n.groups] being the variance component parameter estimate for the random individual effects of the intercept not attributable to relatedness from kins, theta[3 + n.groups] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope attributable to relatedness from kins, theta[4 + n.groups] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope not attributable to relatedness from kins, theta[5 + n.groups] being the variance component parameter estimate for the random individual effects of the time slope attributable to relatedness from kins, and theta[6 + n.groups] being the variance component parameter estimate for the random individual effects of the time slope not attributable to relatedness from kins; if kins is a list and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 3 + 3 * length(kins) + n.groups vector, with theta[1:n.groups] being the residual variance estimates for each group, theta[(1 + n.groups):(length(kins) + n.groups)] being the variance component parameter estimates for the random individual effects of the intercept attributable to relatedness from kins, corresponding to the order of matrices in the list kins, theta[1 + length(kins) + n.groups] being the variance component parameter estimate for the random individual effects of the intercept not attributable to relatedness from kins, theta[(2 + length(kins) + n.groups):(1 + 2 * length(kins) + n.groups)] being the covariance estimates for the random individual effects of the intercept and the random individual effects of the time slope attributable to relatedness from kins, corresponding to the order of matrices in the list kins, theta[2 + 2 * length(kins) + n.groups] being the covariance estimate for the random individual effects of the intercept and the random individual effects of the time slope not attributable to relatedness from kins, theta[(3 + 2 * length(kins) + n.groups):(2 + 3 * length(kins) + n.groups)] being the variance component parameter estimates for the random individual effects of the time slope attributable to relatedness from kins, corresponding to the order of matrices in the list kins, and theta[3 + 3 * length(kins) + n.groups] being the variance component parameter estimate for the random individual effects of the time slope not attributable to relatedness from kins. For multiple phenotype analysis, theta is a list of variance-covariance matrices. If kins is not provided (unrelated individuals), theta is an n.pheno by n.pheno variance-covariance matrix for the residuals of the multiple phenotypes from the linear model; if kins is a matrix and groups is not provided, theta is a length 2 list, with theta[[1]] being the variance-covariance matrix for the residuals and theta[[2]] being the variance-covariance matrix for kins; if kins is a list and groups is not provided, theta is a length 1 + length(kins) list, with theta[[1]] being the variance-covariance matrix for the residuals and theta[[2]] to theta[[1 + length(kins)]] being the variance-covariance matrices, corresponding to the order of matrices in the list kins; if kins is a matrix and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length 1 + n.groups list, with theta[[1]] to theta[[n.groups]] being the variance-covariance matrices for the residuals in each group and theta[[1 + n.groups]] being the variance-covariance matrix for kins; if kins is a list and groups is provided (a heteroscedastic linear mixed model with n.groups residual variance groups), theta is a length length(kins) + n.groups list, with theta[[1]] to theta[[n.groups]] being the variance-covariance matrices for the residuals in each group and theta[[1 + n.groups]] to theta[[length(kins) + n.groups]] being the variance-covariance matrices, corresponding to the order of matrices in the list kins.
n.pheno	an integer indicating the number of phenotypes in multiple phenotype analysis (for single phenotype analysis, n.pheno = 1).
n.groups	an integer indicating the number of distinct residual variance groups in heteroscedastic linear mixed models (for other models, n.groups = 1).
coefficients	a vector or a matrix for the fixed effects parameter estimates (including the intercept).
linear.predictors	a vector or a matrix for the linear predictors.
fitted.values	a vector or a matrix for the fitted mean values on the original scale.
Y	a vector or a matrix for the final working vector.
X	model matrix for the fixed effects.
P	the projection matrix with dimensions equal to the sample size multiplied by n.pheno. Used in glmm.score and SMMAT for dense matrices.
residuals	a vector or a matrix for the residuals on the original scale. NOT rescaled by the dispersion parameter.
scaled.residuals	a vector or a matrix for the scaled residuals, calculated as the original residuals divided by the dispersion parameter (in heteroscedastic linear mixed models, corresponding residual variance estimates by each group).
cov	covariance matrix for the fixed effects (including the intercept).
Sigma_i	the inverse of the estimated covariance matrix for samples, with dimensions equal to the sample size multiplied by n.pheno. Used in glmm.score and SMMAT for sparse matrices.
Sigma_iX	Sigma_i multiplied by X. Used in glmm.score and SMMAT for sparse matrices.
converged	a logical indicator for convergence.
call	the matched call.
id_include	a vector indicating the id of rows in data with nonmissing outcome and covariates, thus are included in the model fit.

Author(s)

Han Chen, Matthew P. Conomos

References

Brent, R.P. (1973) "Chapter 4: An Algorithm with Guaranteed Convergence for Finding a Zero of a Function", Algorithms for Minimization without Derivatives, Englewood Cliffs, NJ: Prentice-Hall, ISBN 0-13-022335-2.

Breslow, N.E. and Clayton, D.G. (1993) Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association 88, 9-25.

Chen, H., Wang, C., Conomos, M.P., Stilp, A.M., Li, Z., Sofer, T., Szpiro, A.A., Chen, W., Brehm, J.M., Celedón, J.C., Redline, S., Papanicolaou, G.J., Thornton, T.A., Laurie, C.C., Rice, K. and Lin, X. (2016) Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. The American Journal of Human Genetics 98, 653-666.

Gilmour, A.R., Thompson, R. and Cullis, B.R. (1995) Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models. Biometrics 51, 1440-1450.

Nelder, J.A. and Mead, R. (1965) A simplex algorithm for function minimization. Computer Journal 7, 308-313.

Yang, J., Lee, S.H., Goddard, M.E. and Visscher, P.M. (2011) GCTA: A Tool for Genome-wide Complex Trait Analysis. The American Journal of Human Genetics 88, 76-82.

Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis for association studies. Nature Genetics 44, 821-824.

Examples

data(example)
attach(example)
model0 <- glmmkin(disease ~ age + sex, data = pheno, kins = GRM, id = "id",
       family = binomial(link = "logit"))
model0$theta
model0$coefficients
model0$cov

model1 <- glmmkin(y.repeated ~ sex, data = pheno2, kins = GRM, id = "id", 
       family = gaussian(link = "identity"))
model1$theta
model1$coefficients
model1$cov
model2 <- glmmkin(y.trend ~ sex + time, data = pheno2, kins = GRM, id = "id", 
       random.slope = "time", family = gaussian(link = "identity"))
model2$theta
model2$coefficients
model2$cov

GMMAT documentation built on Nov. 17, 2023, 5:07 p.m.