Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function fits hierarchical generalized linear models (HGLMs) using various approximation methods.
1 2 3 4 5 6 7 8 9 10 | HGLM(y = NULL, X = NULL, Z = NULL, X.disp = NULL,
family = gaussian(link = identity),
random.family = gaussian(link = identity), method = "EQL",
conv = 1e-04, maxit = 20, fixed = NULL, random = NULL,
disp = NULL, link.disp = "log", disp.random = NULL,
data = NULL, data.random = NULL, fix.disp = NULL,
Offset = NULL, Weights = NULL, disp.start = 0, binomial.N = NULL,
start.fixed = NULL, start.random = NULL, start.disp = NULL,
start.disp.random = NULL, info = TRUE, debug = FALSE,
contrasts = NULL)
|
y |
the dependent variable, only available when |
X |
a design matrix for the fixed effects, only available when |
Z |
an design matrix for the random effects, only available when |
X.disp |
a design matrix for the fixed effects in the dispersion part of the model, only available when |
family |
a description of the error distribution and link function to be used in the mean part of the model. (See |
random.family |
a description of the error distribution and link function to be used in the variance part of the model. |
method |
estimation method, which can be |
conv |
convergence criterion, the default is 1e-4, for models with many random effects could be set less strict. |
maxit |
maximum number of iterations in the IWLS algorithm, only available when |
fixed |
a |
random |
a one-sided |
disp |
a one-sided |
link.disp |
the link function for the dispersion part of the model, only available when |
disp.random |
a list of one-sided |
data |
the data frame to be used together with |
data.random |
a list of |
Weights |
prior weights to be specified in weighted regression, only available when |
fix.disp |
a numeric value if the dispersion parameter of the mean model is known for example 1 for binomial and Poisson models. |
Offset |
an offset for the linear predictor of the mean model. |
disp.start |
(starting) values for the overdispersion structure - vector of length equal to the number of parameters in the overdispersion structure, only available when |
binomial.N |
the number of trials for each observation for binomial models. |
start.fixed |
optional starting values for fixed effects in the mean structure (one vector of numeric values). |
start.random |
optional starting values for random effects in the mean structure (one vector of numeric values). |
start.disp |
optional starting values for parameters of dispersion components of the residuals (one vector of numeric values). |
start.disp.random |
optional starting values for parameters of dispersion components of random effects (one vector of numeric values). |
info |
a request to display of iteration information if |
debug |
a request to display of iteration mechanism progress in detail if |
contrasts |
see |
When method = 'EQL'
, all the model checking functions in the hglm-package
are available on the object returned; Otherwise, all the model checking functions in the HGLMMM-package
are available on the object returned.
When method = 'EQL'
, an object of class hglm
is returned, see hglm
; Otherwise, an object of class HGLM
is returned, see HGLMfit
.
The function provides a unified interface to the hglm-package
developed by Moudud Alam, Lars Ronnegard and Xia Shen, and the HGLMMM-package
developed by Marek Molas.
Xia Shen and Marek Molas
Lee, Y. and Nelder, J.A. (1996). Hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society. Series B (Methological) 58, 619-678.
Lee, Y. and Nelder, J.A. (2001). Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika 88, 987-1006.
Lee, Y., Nelder, J.A., and Pawitan, Y. (2006). Generalized Linear Models with Random Effects. Boca Raton: Chapman & Hall/CRC.
Noh, M. and Lee, Y. (2007). REML estimation for binary data in GLMMs. Journal of Multivariate Analysis 98, 896-915.
Ronnegard, L., Shen, X. and Alam, M. (2010). hglm: A Package for Fitting Hierarchical Generalized Linear Models. The R Journal. (to appear)
Molas, M. and Lesaffre, E. (2010). Hierarchical Generalized Linear Models: the R Package HGLMMM. Submitted.
hglm-package
, HGLMMM-package
, hglm
, HGLMfit
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | data(semiconductor)
# ----- use 'EQL'
h.gamma.normal <- HGLM(fixed = y ~ x1 + x3 + x5 + x6,
random = ~ 1|Device,
family = Gamma(link = log),
disp = ~ x2 + x3, data = semiconductor)
summary(h.gamma.normal)
plot(h.gamma.normal, cex = .6, pch = 1,
cex.axis = 1/.6, cex.lab = 1/.6,
cex.main = 1/.6, mar = c(3, 4.5, 0, 1.5))
# ----- use 'HL(0,1)'
RSC <- data.frame(int = rep(1, 16))
h.gamma.normal <- HGLM(fixed = y ~ x1 + x3 + x5 + x6,
random = ~ 1|Device,
family = Gamma(link = log),
disp = ~ x2 + x3, data = semiconductor,
method = 'HL01', disp.start = c(0, 0, 0),
disp.random = list(one = ~ 1), data.random = list(RSC))
# ----- use 'HL(1,1)'
RSC <- data.frame(int = rep(1, 16))
h.gamma.normal <- HGLM(fixed = y ~ x1 + x3 + x5 + x6,
random = ~ 1|Device,
family = Gamma(link = log),
disp = ~ x2 + x3, data = semiconductor,
method = 'HL11', disp.start = c(0, 0, 0),
disp.random = list(one = ~ 1), data.random = list(RSC))
|
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