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.