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

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

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.