Description Usage Arguments Details Value Note Author(s) References See Also Examples
Produces an object of class "qif
" which is a Quadratic Inference Function fit
of the balanced longitudinal data.
1 2 3 |
formula |
a formula expression as for other regression models, of the form
|
id |
a vector which identifies the clusters. The length of |
data |
an optional data frame in which to interpret the variables occurring
in the |
b |
an initial estimate for the parameters. |
tol |
the tolerance used in the fitting algorithm. |
maxiter |
the maximum number of iterations. |
family |
a |
corstr |
a character string specifying the correlation structure. The
following are permitted: |
invfun |
a character string specifying the matrix inverse function. The
following are permitted: |
qif
provides two options of computing matrix inverses. The default
is from Fortran math library, and the other one is generalized inverse "ginv
"
given in R package MASS
. You can call option "ginv
" through argument "invfun
"
in "qif()
".
A list containing:
title
: name of qif
version
: the current version of qif
model
: analysis model for link function, variance function and correlation struture
terms
: analysis model for link function, variance function and correlation struture
iteration
: the number of iterations
coefficients
: beta esitmates value
linear.perdictors
: linear predictor value
fitted.value
: fitted value of y
x
: the perdicted matrix
y
: the response
residuals
: y-mu
pearson.resi
: pearson residuals
scale
: the scale of fitted model
family
: the type of distribution
id
: model fitted value
max.id
: max number of each steps
xnames
: the values are X name of qif
statistics
: The qif statistics
Xnames
: the name X matrix in qif
parameter
: parameter estimates
covariance
: Covariance of coefficients
This R package is created by transplanting a SAS macro QIF developed originally by Peter Song and Zhichang Jiang (2006). This is version 1.5 of this user documentation file, revised 2019-07-02.
Zhichang Jiang, Alberta Health Services, and Peter X.K. Song, University of Michigan.
Qu A, Lindsay BG, Li B. Improving generalized estimating equations using quadratic inference functions. Biometrika 2000, 87 823-836.
Qu A, Song P X-K. Assessing robustness of generalised estimating equations and quadratic inference functions. Biometrika 2004, 91 447-459.
Qu A, Lindsay BG. Building adaptive estimating equations when inverse of covariance estimation is difficult. J. Roy. Statist. Soc. B 2003, 65, 127-142.
glm, lm, formula.
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 | ## Marginal log-linear model for the epileptic seizures count data
## (Diggle et al., 2002, Analysis of Longitudinal Data, 2nd Ed., Oxford Press).
# Read in the epilepsy data set:
data(epil)
# Fit the QIF model:
fit <- qif(y ~ base + trt + lage + V4, id=subject, data=epil,
family=poisson, corstr="AR-1")
# Alternately, use ginv() from package MASS
fit <- qif(y ~ base + trt + lage + V4, id=subject, data=epil,
family=poisson, corstr="AR-1", invfun = "ginv")
# Print summary of QIF fit:
summary(fit)
## Second example: MS study
data(exacerb)
qif_BIN_IND<-qif(exacerbation ~ treatment + time + duration + time2, id=id,
data=exacerb, family=binomial, corstr="independence")
qif_BIN_AR1<-qif(exacerbation ~ treatment + time + duration + time2, id=id,
data=exacerb, family=binomial, corstr="AR-1")
qif_BIN_CS<-qif(exacerbation ~ treatment + time + duration + time2, id=id,
data=exacerb, family=binomial, corstr="exchangeable")
qif_BIN_UN<-qif(exacerbation ~ treatment + time + duration + time2, id=id,
data=exacerb, family=binomial, corstr="unstructured")
summary(qif_BIN_CS)
qif_BIN_CS$statistics
qif_BIN_CS$covariance
|
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