genSSet: Generating moments/data for IV-like estimands
In ivmte: Instrumental Variables: Extrapolation by Marginal Treatment Effects

Description Usage Arguments Value Examples

This function takes in the IV estimate and its IV-like specification, and generates a list containing the corresponding IV-like point estimate, and the corresponding moments (gammas) that will enter into the constraint matrix of the LP problem. If the option means = FALSE, then the data are not averaged to generate the gamma moments and may be used for GMM. The function requires the user to provide a list (i.e. the list the point estimates and moments corresponding to other IV-like specifications; or an empty list) to append these point estimates and moments to.

genSSet(
  data,
  sset,
  sest,
  splinesobj,
  pmodobj,
  pm0,
  pm1,
  ncomponents,
  scount,
  subset_index,
  means = TRUE,
  yvar,
  dvar,
  noisy = TRUE,
  ivn = NULL,
  redundant = NULL
)

`data`	`data.frame` used to estimate the treatment effects.
`sset`	list, which is modified and returned as the output. This object will contain all the information from the IV-like specifications that can be used for estimating the treatment effect.
`sest`	list containing the point estimates and S-weights corresponding to a particular IV-like estimand.
`splinesobj`	list of spline components in the MTRs for treated and control groups. Spline terms are extracted using `removeSplines`.
`pmodobj`	vector of propensity scores.
`pm0`	list of the monomials in the MTR for the control group.
`pm1`	list of the monomials in the MTR for the treated group.
`ncomponents`	The number of components from the IV regression to include in the S-set.
`scount`	integer, an index for the elements in the S-set.
`subset_index`	vector of integers, a row index for the subset of the data the IV regression is restricted to.
`means`	boolean, set to `TRUE` by default. If set to `TRUE`, then the gamma moments are returned, i.e. sample averages are taken. If set to `FALSE`, then no sample averages are taken, and a matrix is returned. The sample average of each column of the matrix corresponds to a particular gamma moment.
`yvar`	name of outcome variable. This is only used if `means = FALSE`, which occurs when the user believes the treatment effect is point identified.
`dvar`	name of treatment indicator. This is only used if `means = FALSE`, which occurs when the user believes the treatment effect is point identified.
`noisy`	boolean, default set to `TRUE`. If `TRUE`, then messages are provided throughout the estimation procedure. Set to `FALSE` to suppress all messages, e.g. when performing the bootstrap.
`ivn`	integer, the number indicating which IV specification the component corresponds to.
`redundant`	vector of integers indicating which components in the S-set are redundant.

A list containing the point estimate for the IV regression, and the expectation of each monomial term in the MTR.

dtm <- ivmte:::gendistMosquito()

## Declare empty list to be updated (in the event multiple IV like
## specifications are provided)
sSet <- list()

## Declare MTR formulas
formula1 = ~ 1 + u
formula0 = ~ 1 + u

## Construct object that separates out non-spline components of MTR
## formulas from the spline components. The MTR functions are
## obtained from this object by the function 'genSSet'.
splinesList = list(removeSplines(formula0), removeSplines(formula1))

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

polynomials1 <- polyparse(formula = formula0,
                 data = dtm,
                 uname = u,
                 as.function = FALSE)

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

## Generate IV estimates
ivEstimates <- ivEstimate(formula = ey ~ d | z,
                          data = dtm,
                          components = l(d),
                          treat = d,
                          list = FALSE)

## Construct S-set, which contains the coefficients and weights
## corresponding to various IV-like estimands
genSSet(data = dtm,
        sset = sSet,
        sest = ivEstimates,
        splinesobj = splinesList,
        pmodobj = propensityObj$phat,
        pm0 = polynomials0,
        pm1 = polynomials1,
        ncomponents = 1,
        scount = 1)