# nmm_fit: Fitting IPW or AIPW Estimators under Nonmonotone Missing at... In NMMIPW: Inverse Probability Weighting under Non-Monotone Missing

## Description

nmm_fit is the main function used to fit IPW or AIPW estimators under nonmonotone missing at random data

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```nmm_fit( data, O, AIPW = FALSE, formula = NULL, func = NULL, weights = NULL, ... ) ```

## Arguments

 `data` a data.frame to fit `O` missing indicator `AIPW` indicator if fitting augmented IPW `formula` optional formula specified to fit `func` optional fitting function, currently support 'lm' and 'glm' `weights` optional weights used in the estimation `...` further arguments passed to func, e.g. family = 'quasibinomial' for glm

## Value

NMMIPW returns an object of class "NMMIPW". An object of class "NMMIPW" is a list containing the following components:

 `coefficients` the fitted values, only reported when formula and func are given `coef_sd` the standard deviations of coefficients, only reported when formula and func are given `coef_IF` the influnece function of coefficients, only reported when formula and func are given `gamma_para` the first step fitted valus `AIPW` an indicator of whether AIPW is fitted `second_step` an indicator of whether the second step is fitted `second_fit` if second step fitted, we report the fit object `by_prod` a list of by products that might be useful for users, including first step IF, jacobian matrices

## Examples

 ```1 2 3 4 5 6 7 8``` ```n = 100 X = rnorm(n, 0, 1) Y = rnorm(n, 1 * X, 1) O1 = rbinom(n, 1, 1/(1 + exp(- 1 - 0.5 * X))) O2 = rbinom(n, 1, 1/(1 + exp(+ 0.5 + 1 * Y))) O = cbind(O1, O2) df <- data.frame(Y = Y, X = X) fit <- nmm_fit(data = df, O = O, formula = Y ~ X, func = lm) ```

NMMIPW documentation built on Dec. 20, 2021, 5:07 p.m.