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leg
mcglm
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0bef1ae7
Commit
0bef1ae7
authored
9 years ago
by
wbonat
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Add the mcglm function in a new file.
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R/mc_main_function.R
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0bef1ae7
#' Fitting Multivariate Covariance Generalized Linear Models (McGLM)
#'
#' @description \code{mcglm} is used to fit multivariate covariance generalized linear models.
#' The models are specified by a set of lists giving a symbolic description of the linear predictor.
#' The user can choose between a list of link, variance and covariance functions. The models are
#' fitted using an estimating function approach, combining quasi-score functions for regression
#' parameters and Pearson estimating function for covariance parameters. For details see Bonat and
#' Jorgensen (2015).
#'
#' @param linear_pred A list of formula see \code{\link[stats]{formula}} for details.
#' @param matrix_pred A list of known matrices to be used on the matrix linear predictor. Details
#' can be obtained on \code{\link[mcglm]{mc_matrix_linear_predictor}}.
#' @param link A list of link functions names, see \code{\link[mcglm]{mc_link_function}} for details.
#' @param variance A list of variance functions names, see \code{\link[mcglm]{mc_variance_function}}
#' for details.
#' @param covariance A list of covariance link functions names, current options are: identity, inverse
#' and exponential-matrix (expm).
#' @param offset A list with values of offset values if any.
#' @param Ntrial A list with values of the number of trials on Bernoulli experiments. It is useful only
#' for binomialP and binomialPQ variance functions.
#' @param power_fixed A list of logicals indicating if the values of the power parameter should be
#' estimated or not.
#' @param control_initial A list of initial values for the fitting algorithm. See details below.
#' @param control_algorithm A list of arguments to be passed for the fitting algorithm. See
#' \code{\link[mcglm]{fit_mcglm}} for details.
#' @param contrasts Extra arguments to passed to \code{\link[stats]{model.matrix}}.
#' @param data A dta frame.
#' @return mcglm returns an object of class 'mcglm'.
#' @export
#' @import Matrix
mcglm
<-
function
(
linear_pred
,
matrix_pred
,
link
,
variance
,
covariance
,
offset
,
Ntrial
,
power_fixed
,
data
,
control_initial
=
"automatic"
,
contrasts
=
NULL
,
control_algorithm
=
list
())
{
n_resp
<-
length
(
linear_pred
)
linear_pred
<-
as.list
(
linear_pred
)
matrix_pred
<-
as.list
(
matrix_pred
)
if
(
missing
(
link
))
{
link
=
rep
(
"identity"
,
n_resp
)
}
if
(
missing
(
variance
))
{
variance
=
rep
(
"constant"
,
n_resp
)
}
if
(
missing
(
covariance
))
{
covariance
=
rep
(
"identity"
,
n_resp
)
}
if
(
missing
(
offset
))
{
offset
=
rep
(
list
(
NULL
),
n_resp
)
}
if
(
missing
(
Ntrial
))
{
Ntrial
=
rep
(
list
(
rep
(
1
,
dim
(
data
)[
1
])),
n_resp
)
}
if
(
missing
(
power_fixed
))
{
power_fixed
<-
rep
(
TRUE
,
n_resp
)
}
if
(
missing
(
contrasts
))
{
constrasts
=
NULL
}
link
<-
as.list
(
link
)
variance
<-
as.list
(
variance
)
covariance
<-
as.list
(
covariance
)
offset
<-
as.list
(
offset
)
Ntrial
<-
as.list
(
Ntrial
)
power_fixed
=
as.list
(
power_fixed
)
if
(
class
(
control_initial
)
!=
"list"
)
{
control_initial
<-
mc_initial_values
(
linear_pred
=
linear_pred
,
matrix_pred
=
matrix_pred
,
link
=
link
,
variance
=
variance
,
covariance
=
covariance
,
offset
=
offset
,
Ntrial
=
Ntrial
,
contrasts
=
contrasts
,
data
=
data
)
cat
(
"Automatic initial values selected."
)
}
con
<-
list
(
correct
=
TRUE
,
max_iter
=
20
,
tol
=
1e-04
,
method
=
"chaser"
,
tunning
=
1
,
verbose
=
FALSE
)
con
[(
namc
<-
names
(
control_algorithm
))]
<-
control_algorithm
if
(
!
is.null
(
contrasts
))
{
list_X
<-
list
()
for
(
i
in
1
:
n_resp
)
{
list_X
[[
i
]]
<-
model.matrix
(
linear_pred
[[
i
]],
contrasts
=
contrasts
[[
i
]],
data
=
data
)
}
}
else
{
list_X
<-
lapply
(
linear_pred
,
model.matrix
,
data
=
data
)
}
list_model_frame
<-
lapply
(
linear_pred
,
model.frame
,
data
=
data
)
list_Y
<-
lapply
(
list_model_frame
,
model.response
)
y_vec
<-
as.numeric
(
do.call
(
c
,
list_Y
))
sparse
<-
lapply
(
matrix_pred
,
function
(
x
)
{
if
(
class
(
x
)
==
"dgeMatrix"
)
{
FALSE
}
else
TRUE
})
model_fit
<-
try
(
fit_mcglm
(
list_initial
=
control_initial
,
list_link
=
link
,
list_variance
=
variance
,
list_covariance
=
covariance
,
list_X
=
list_X
,
list_Z
=
matrix_pred
,
list_offset
=
offset
,
list_Ntrial
=
Ntrial
,
list_power_fixed
=
power_fixed
,
list_sparse
=
sparse
,
y_vec
=
y_vec
,
correct
=
con
$
correct
,
max_iter
=
con
$
max_iter
,
tol
=
con
$
tol
,
method
=
con
$
method
,
tunning
=
con
$
tunning
,
verbose
=
con
$
verbose
))
if
(
class
(
model_fit
)
!=
"try-error"
)
{
model_fit
$
beta_names
<-
lapply
(
list_X
,
colnames
)
model_fit
$
power_fixed
<-
power_fixed
model_fit
$
list_initial
<-
control_initial
model_fit
$
n_obs
<-
dim
(
data
)[
1
]
model_fit
$
link
<-
link
model_fit
$
variance
<-
variance
model_fit
$
covariance
<-
covariance
model_fit
$
linear_pred
<-
linear_pred
model_fit
$
con
<-
con
model_fit
$
observed
<-
Matrix
(
y_vec
,
ncol
=
length
(
list_Y
),
nrow
=
dim
(
data
)[
1
])
model_fit
$
list_X
<-
list_X
class
(
model_fit
)
<-
"mcglm"
}
return
(
model_fit
)
}
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