Description Usage Arguments Details Value See Also Examples
This function estimates a linear doseresponse parameter representing a one quantile increase in a set of exposures of interest for hurdle count outcomes. This function is limited to linear and additive effects of individual components of the exposure. This model estimates the parameters of a marginal structural hurdle model (MSM) based on gcomputation with quantized exposures. Note: this function is valid only under linear and additive effects of individual components of the exposure, but when these hold the model can be fit with very little computational burden.
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f 
R style formula using syntax from 'pscl' package: depvar ~ indvars_count  indvars_zero 
data 
data frame 
expnms 
character vector of exposures of interest 
q 
NULL or number of quantiles used to create quantile indicator variables representing the exposure variables. If NULL, then gcomp proceeds with untransformed version of exposures in the input datasets (useful if data are already transformed, or for performing standard gcomputation) 
breaks 
(optional) NULL, or a list of (equal length) numeric vectors that characterize the minimum value of each category for which to break up the variables named in expnms. This is an alternative to using 'q' to define cutpoints. 
id 
(optional) NULL, or variable name indexing individual units of observation (only needed if analyzing data with multiple observations per id/cluster) 
weights 
"case weights"  passed to the "weight" argument of

alpha 
alpha level for confidence limit calculation 
bayes 
not yet implemented 
... 
arguments to hurdle (e.g. dist) 
A hurdle version of quantile gcomputation based on the implementation in the 'pscl' package. A hurdle distribution is a mixture distribution in which one of the distributions is a point mass at zero (with probability given by a logistic model), and the other distribution is a discrete or continuous distribution. This estimates the effect of a joint increase in all exposures on 1) the odds of belonging to the "zero" vs. "count" portions of the distribution and/or 2) the rate parameter for the "count" portion of the distribution.
a qgcompfit object, which contains information about the effect measure of interest (psi) and associated variance (var.psi), as well as information on the model fit (fit) and information on the weights/standardized coefficients in the positive (pos.weights) and negative (neg.weights) directions.
qgcomp.hurdle.boot
,qgcomp.noboot
,
qgcomp.cox.noboot
, and hurdle
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  set.seed(50)
n=100
dat < data.frame(y=rbinom(n, 1, 0.5)*rpois(n, 1.2), x1=runif(n), x2=runif(n), z=runif(n))
# poisson count model, mixture in both portions
qgcomp.hurdle.noboot(f=y ~ z + x1 + x2  x1 + x2, expnms = c('x1', 'x2'),
data=dat, q=2, dist="poisson")
# negative binomial count model, mixture and covariate in both portions
qgcomp.hurdle.noboot(f=y ~ z + x1 + x2  z + x1 + x2, expnms = c('x1', 'x2'),
data=dat, q=2, dist="negbin")
qgcomp.hurdle.noboot(f=y ~ z + x1 + x2, expnms = c('x1', 'x2'),
data=dat, q=2, dist="negbin") # equivalent
# negative binomial count model, mixture only in the 'count' portion of the model
qgcomp.hurdle.noboot(f=y ~ z + x1 + x2  z, expnms = c('x1', 'x2'), data=dat, q=2, dist="negbin")
# weighted analysis
dat$w = runif(n)*5
qgcomp.hurdle.noboot(f=y ~ z + x1 + x2  x1 + x2, expnms = c('x1', 'x2'),
data=dat, q=2, dist="poisson", weights=w)
# Expect this:
# Warning message:
# In eval(family$initialize) : noninteger #successes in a binomial glm!

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