nmm_fit {NMMIPW}

Fitting IPW or AIPW Estimators under Nonmonotone Missing at Random Data

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)