qgcomp.multinomial.noboot: Quantile g-computation for multinomial outcomes

In qgcomp: Quantile G-Computation

Quantile g-computation for multinomial outcomes

Description

Quantile g-computation for multinomial outcomes

Usage

qgcomp.multinomial.noboot(
  f,
  data,
  expnms = NULL,
  q = 4,
  breaks = NULL,
  id = NULL,
  weights,
  alpha = 0.05,
  bayes = FALSE,
  ...
)

Arguments

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 un-transformed version of exposures in the input datasets (useful if data are already transformed, or for performing standard g-computation)
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 cluster-appropriate 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 cluster-appropriate standard errors via bootstrapping.
weights	"case weights" - passed to the "weight" argument of multinom
alpha	alpha level for confidence limit calculation
bayes	Logical, Not yet implemented (gives and error if set to TRUE)
...	arguments to nnet::multinom

Value

a qgcompmultfit 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 and negative directions (weights).

See Also

qgcomp.glm.noboot, multinom

Other qgcomp_methods: qgcomp.cch.noboot(), qgcomp.cox.boot(), qgcomp.cox.noboot(), qgcomp.glm.boot(), qgcomp.glm.noboot(), qgcomp.hurdle.boot(), qgcomp.hurdle.noboot(), qgcomp.multinomial.boot(), qgcomp.partials(), qgcomp.zi.boot(), qgcomp.zi.noboot()

Examples

data("metals") # from qgcomp package
# create categorical outcome from the existing continuous outcome (usually, one will already exist)
metals$ycat = factor(quantize(metals, "y",q=4)$data$y, levels=c("0", "1", "2", "3"), 
                     labels=c("cct", "ccg", "aat", "aag")) 
# restrict to smaller dataset for simplicity
smallmetals = metals[,c("ycat", "arsenic", "lead", "cadmium", "mage35")]

### 1: Define mixture and underlying model ####
mixture = c("arsenic", "lead", "cadmium")
f0 = ycat ~ arsenic + lead + cadmium # the multinomial model 
# (be sure that factor variables are properly coded ahead of time in the dataset)

rr = qgcomp.multinomial.noboot(
 f0, 
 expnms = mixture,
 q=4, 
 data = smallmetals, 
 )
 
 ### 5: Create summary qgcomp object for nice printing ####
 
 summary(rr, tests=c("H")) # include homogeneity test
 
 # 95% confidence intervals
 confint(rr, level=0.95)
 rr$breaks # quantile cutpoints for exposures
 # homogeneity_test(rr)
 joint_test(rr)

qgcomp documentation built on Aug. 10, 2023, 5:07 p.m.