ivmte (version 1.1.0)

genGamma: Estimating expectations of terms in the MTR (gamma objects)

Description

This function generates the gamma objects defined in the paper, i.e. each additive term in E[md], where md is a MTR.

Usage

genGamma(monomials, lb, ub, multiplier = 1, subset = NULL,
  means = TRUE, late.rows = NULL)

Arguments

monomials

[UPDATE DESCRIPTION] object containing list of list of monomials. Each element of the outer list represents an observation in the data set, each element in the inner list is a monomial from the MTR. The variable is the unobservable u, and the coefficient is the evaluation of any interactions with u.

lb

vector of lower bounds for the interval of integration. Each element corresponds to an observation.

ub

vector of upper bounds for the interval of integration. Each element corresponds to an observation.

multiplier

a vector of the weights that enter into the integral. Each element corresponds to an observation.

subset

The row names/numbers of the subset of observations to use.

means

logical, if TRUE then function returns the terms of E[md]. If FALSE, then function instead returns each term of E[md | D, X, Z]. This is useful for testing the code, i.e. obtaining population estimates.

late.rows

Boolean vector indicating which observations to include when conditioning on covariates X.

Value

If means = TRUE, then the function returns a vector of the additive terms in Gamma (i.e. the expectation is over D, X, Z, and u). If means = FALSE, then the function returns a matrix, where each row corresponds to an observation, and each column corresponds to an additive term in E[md | D, X, Z] (i.e. only the integral with respect to u is performed).

Examples

# NOT RUN {
dtm <- ivmte:::gendistMosquito()

## Declare MTR formula
formula0 = ~ 1 + u

## Construct MTR polynomials
polynomials0 <- polyparse(formula = formula0,
                data = dtm,
                uname = u,
                as.function = FALSE)

## Construct propensity score model
propensityObj <- propensity(formula = d ~ z,
                            data = dtm,
                            link = "linear")

## Generate gamma moments, with S-weight equal to its default value
## of 1
genGamma(monomials = polynomials0,
         lb = 0,
         ub = propensityObj$phat)

# }