R: Fitting Hierarchical Generalized Linear Models

hglm2 {hglm}

R Documentation

Fitting Hierarchical Generalized Linear Models

Description

hglm2 is used to fit hierarchical generalized linear models. hglm2 is used to fit hierarchical generalized linear models. It extends the hglm function by allowing for several random effects, where the model is specified in lme4 convension, and also by implementing sparse matrix techniques using the Matrix library.

Usage

hglm2(meanmodel, data = NULL, family = gaussian(link = identity),
      rand.family = gaussian(link = identity), method = "EQL", 
      conv = 1e-6, maxit = 50, startval = NULL,
      X.disp = NULL, disp = NULL, link.disp = "log", 
      weights = NULL, fix.disp = NULL, offset = NULL, 
      sparse = TRUE, vcovmat = FALSE, calc.like = FALSE, 
      RandC = NULL, bigRR = FALSE, verbose = FALSE, ...)

Arguments

`meanmodel`	`formula`. A two sided formula specifying the fixed and random terms in `lme4` convention, e.g. `y ~ x1 + (1\|id)` indicates `y` as response, `x1` as the fixed effect and `(1\|id)` represent a random intercept for each level of `id`.
`data`	`data.frame`. An optional data frame from where the variables in the `meanmodel` (and possibly `disp`) are to be obtained. It is expected that the data frame does not contain any missing value.
`family`	`family`. The description of the error distribution and link function to be used in the mean part of the model. (See `family` for details of family functions.)
`rand.family`	`family`. The description of the distribution and link function to be used for the random effect.
`method`	`character`. Estimation method where `EQL` is the method of interconnected GLMs presented in Lee et al (2006). Apart from the default option `EQL` there is also an `EQL1` option, which improves estimation for GLMMs (especially for Poisson models with a large number of levels in the random effects).
`conv`	`numeric`. The convergence criteria (change in linear predictor between iterations).
`maxit`	`numeric`. Maximum number of iterations in the `hglm` algorithm.
`startval`	`numeric`. A vector of starting values in the following order: fixed effects, random effect, variance of random effects, variance of residuals.
`X.disp`	`matrix`. The design matrix for the fixed effects in the dispersion part of the model.
`disp`	`formula`. A one-sided `formula` specifying the fixed effects in the dispersion part of the model.
`link.disp`	`character`. The link function for the dispersion part of the model.
`weights`	`numeric`. Prior weights to be specified in weighted regression.
`fix.disp`	`numeric`. A numeric value if the dispersion parameter of the mean model is known, e.g., 1 for binomial and Poisson model.
`offset`	An offset for the linear predictor of the mean model.
`sparse`	`logical`. If `TRUE`, the computation is to be carried out by using sparse matrix technique.
`vcovmat`	logical. If `TRUE`, the variance-covariance matrix is exported.
`calc.like`	logical. If `TRUE`, likelihoods will be computed at convergence and will be shown via the print or summary methods on the output object.
`RandC`	`numeric`. Necessary in old versions but can be neglected now. Integers (possibly a vector) specifying the number of column of Z to be used for each of the random-effect terms.
`bigRR`	logical. If `TRUE`, and only for the Gaussian model with one random effect term, a specific algorithm will be used for fast fitting high-dimensional (p >> n) problems. See Shen et al. (2013) for more details of the method.
`verbose`	logical. If `TRUE`, more information is printed during model fitting process.
`...`	not used.

Details

Models for hglm are either specified symbolically using formula or by specifying the design matrices ( X, Z and X.disp). Currently, only the extended quasi likelihood (EQL) method is available for the estimation of the model parameters. Only for the Gaussian-Gaussina linear mixed models, it is REML. It should be noted that the EQL estimator can be biased and inconsistent in some special cases e.g. binary pair matched response. A higher order correction might be useful to correct the bias of EQL (Lee et al. 2006). But, those currections are not implemented in the current version. By default, the dispersion parameter is always estimated via EQL. If the dispersion parameter of the mean model is to be held constant, for example if it is desired to be 1 for binomial and Poisson family, then fix.disp=value where, value=1 for the above example, should be used.

Value

It returns an object of class hglm consiting of the following values.

`fixef`	fixed effect estimates.
`ranef`	random effect estimates.
`RandC`	integers (possibly a vector) specified the number of column of Z to be used for each of the random-effect terms.
`varFix`	dispersion parameter of the mean model (residual variance for LMM).
`varRanef`	dispersion parameter of the random effects (variance of random effects for GLMM).
`iter`	number of iterations used.
`Converge`	specifies if the algorithm converged.
`SeFe`	standard errors of fixed effects.
`SeRe`	standard errors of random effects.
`dfReFe`	deviance degrees of freedom for the mean part of the model.
`SummVC1`	estimates and standard errors of the linear predictor in the dispersion model.
`SummVC2`	estimates and standard errors of the linear predictor for the dispersion parameter of the random effects.
`dev`	individual deviances for the mean part of the model.
`hv`	hatvalues for the mean part of the model.
`resid`	studentized residuals for the mean part of the model.
`fv`	fitted values for the mean part of the model.
`disp.fv`	fitted values for the dispersion part of the model.
`disp.resid`	standardized deviance residuals for the dispersion part of the model.
`link.disp`	link function for the dispersion part of the model.
`vcov`	the variance-covariance matrix.
`likelihood`	a list of log-likelihood values for model selection purposes, where `$hlik` is -2 times the log-h-likelihood, `$pvh` -2 times the adjusted profile log-likelihood profiled over random effects, `$pbvh` -2 times the adjusted profile log-likelihood profiled over fixed and random effects, and `$cAIC` the conditional AIC.
`bad`	the index of the influential observation.

Author(s)

Moudud Alam, Xia Shen, Lars Ronnegard

References

Lars Ronnegard, Xia Shen and Moudud Alam (2010). hglm: A Package for Fitting Hierarchical Generalized Linear Models. The R Journal, 2(2), 20-28.

Youngjo Lee, John A Nelder and Yudi Pawitan (2006) Generalized Linear Models with Random Effect: a unified analysis via h-likelihood. Chapman and Hall/CRC.

Xia Shen, Moudud Alam, Freddy Fikse and Lars Ronnegard (2013). A novel generalized ridge regression method for quantitative genetics. Genetics.

Moudud Alam, Lars Ronnegard, Xia Shen (2014). Fitting conditional and simultaneous autoregressive spatial models in hglm. Submitted.

Examples

# Find more examples and instructions in the package vignette:
# vignette('hglm')

require(hglm)

# --------------------- #
# semiconductor example #
# --------------------- #

data(semiconductor)

m11 <- hglm(fixed = y ~ x1 + x3 + x5 + x6,
            random = ~ 1|Device,
            family = Gamma(link = log),
            disp = ~ x2 + x3, data = semiconductor)
summary(m11)
plot(m11, cex = .6, pch = 1,
     cex.axis = 1/.6, cex.lab = 1/.6,
     cex.main = 1/.6, mar = c(3, 4.5, 0, 1.5))

# ------------------- #
# redo it using hglm2 #
# ------------------- #

m12 <- hglm2(y ~ x1 + x3 + x5 + x6 + (1|Device),
             family = Gamma(link = log),
             disp = ~ x2 + x3, data = semiconductor)
summary(m12)
     
# -------------------------- #
# redo it using matrix input #
# -------------------------- #

attach(semiconductor)
m13 <- hglm(y = y, X = model.matrix(~ x1 + x3 + x5 + x6),
            Z = kronecker(diag(16), rep(1, 4)),
            X.disp = model.matrix(~ x2 + x3), 
            family = Gamma(link = log))
summary(m13)
     
# --------------------- #
# verbose & likelihoods #
# --------------------- #
     
m14 <- hglm(fixed = y ~ x1 + x3 + x5 + x6,
            random = ~ 1|Device,
            family = Gamma(link = log),
            disp = ~ x2 + x3, data = semiconductor,
            verbose = TRUE, calc.like = TRUE)
summary(m14)

# --------------------------------------------- #  
# simulated example with 2 random effects terms #
# --------------------------------------------- #  
## Not run: 
set.seed(911)
x1 <- rnorm(100)
x2 <- rnorm(100)
x3 <- rnorm(100)
z1 <- factor(rep(LETTERS[1:10], rep(10, 10)))
z2 <- factor(rep(letters[1:5], rep(20, 5)))
Z1 <- model.matrix(~ 0 + z1)
Z2 <- model.matrix(~ 0 + z2)
u1 <- rnorm(10, 0, sqrt(2))
u2 <- rnorm(5, 0, sqrt(3))
y <- 1 + 2*x1 + 3*x2 + Z1%*%u1 + Z2%*%u2 + rnorm(100, 0, sqrt(exp(x3)))
dd <- data.frame(x1 = x1, x2 = x2, x3 = x3, z1 = z1, z2 = z2, y = y)

(m21 <- hglm(X = cbind(rep(1, 100), x1, x2), y = y, Z = cbind(Z1, Z2), 
              RandC = c(10, 5)))
summary(m21)
plot(m21)

(m22 <- hglm2(y ~ x1 + x2 + (1|z1) + (1|z2), data = dd, vcovmat = TRUE))
image(m22$vcov, main = 'Variance-covariance Matrix')
summary(m22)
plot(m22)

m31 <- hglm2(y ~ x1 + x2 + (1|z1) + (1|z2), disp = ~ x3, data = dd)
print (m31)
summary(m31)
plot(m31)

## End(Not run)

[Package hglm version 2.2-1 Index]