# genGamma: Estimating expectations of terms in the MTR (gamma objects) In ivmte: Instrumental Variables: Extrapolation by Marginal Treatment Effects

## 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

 ```1 2 3 4 5 6 7 8 9``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```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) ```

ivmte documentation built on Sept. 17, 2021, 5:06 p.m.