qgcomp.glm.noboot  R Documentation 
This function estimates a linear doseresponse parameter representing a one quantile increase in a set of exposures of interest. This function is limited to linear and additive effects of individual components of the exposure. This model estimates the parameters of a marginal structural 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. qgcomp.noboot is an equivalent function (slated for deprecation)
qgcomp.glm.noboot(
f,
data,
expnms = NULL,
q = 4,
breaks = NULL,
id = NULL,
weights,
alpha = 0.05,
bayes = FALSE,
...
)
qgcomp.noboot(
f,
data,
expnms = NULL,
q = 4,
breaks = NULL,
id = NULL,
weights,
alpha = 0.05,
bayes = FALSE,
...
)
f 
R style formula 
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). Note that qgcomp.glm.noboot will not produce clusterappropriate standard errors (this parameter is essentially ignored in qgcomp.glm.noboot). qgcomp.glm.boot can be used for this, which will use bootstrap sampling of clusters/individuals to estimate clusterappropriate standard errors via bootstrapping. 
weights 
"case weights"  passed to the "weight" argument of

alpha 
alpha level for confidence limit calculation 
bayes 
use underlying Bayesian model ( 
... 
arguments to glm (e.g. family) 
For continuous outcomes, under a linear model with no interaction terms, this is equivalent to gcomputation of the effect of increasing every exposure by 1 quantile. For binary/count outcomes outcomes, this yields a conditional log odds/rate ratio(s) representing the change in the expected conditional odds/rate (conditional on covariates) from increasing every exposure by 1 quantile. In general, the latter quantity is not equivalent to gcomputation estimates. Hypothesis test statistics and confidence intervals are based on using the delta estimate variance of a linear combination of random variables.
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.
Other qgcomp_methods:
qgcomp.cch.noboot()
,
qgcomp.cox.boot()
,
qgcomp.cox.noboot()
,
qgcomp.glm.boot()
,
qgcomp.hurdle.boot()
,
qgcomp.hurdle.noboot()
,
qgcomp.multinomial.boot()
,
qgcomp.multinomial.noboot()
,
qgcomp.partials()
,
qgcomp.zi.boot()
,
qgcomp.zi.noboot()
set.seed(50)
# linear model
dat < data.frame(y=runif(50,1,1), x1=runif(50), x2=runif(50), z=runif(50))
qgcomp.glm.noboot(f=y ~ z + x1 + x2, expnms = c('x1', 'x2'), data=dat, q=2, family=gaussian())
# not intercept model
qgcomp.glm.noboot(f=y ~1+ z + x1 + x2, expnms = c('x1', 'x2'), data=dat, q=2, family=gaussian())
# logistic model
dat2 < data.frame(y=rbinom(50, 1,0.5), x1=runif(50), x2=runif(50), z=runif(50))
qgcomp.glm.noboot(f=y ~ z + x1 + x2, expnms = c('x1', 'x2'), data=dat2, q=2, family=binomial())
# poisson model
dat3 < data.frame(y=rpois(50, .5), x1=runif(50), x2=runif(50), z=runif(50))
qgcomp.glm.noboot(f=y ~ z + x1 + x2, expnms = c('x1', 'x2'), data=dat3, q=2, family=poisson())
# weighted model
N=5000
dat4 < data.frame(y=runif(N), x1=runif(N), x2=runif(N), z=runif(N))
dat4$w=runif(N)*2
qdata = quantize(dat4, expnms = c("x1", "x2"))$data
(qgcfit < qgcomp.glm.noboot(f=y ~ z + x1 + x2, expnms = c('x1', 'x2'), data=dat4, q=4,
family=gaussian(), weights=w))
qgcfit$fit
glm(y ~ z + x1 + x2, data = qdata, weights=w)
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