Description Usage Arguments Details Value References Examples
This function implements model-assisted inference for population means with missing data, using regularized calibrated estimation along a regularization path for propensity score (PS) estimation while based on cross validation for outcome regression (OR).
1 2 | mn.regu.path(fold, nrho = NULL, rho.seq = NULL, y, tr, x, ploss = "cal",
yloss = "gaus", off = 0, ...)
|
fold |
A vector of length 2, with the second component giving the fold number for cross validation in outcome regression. The first component is not used. |
nrho |
A vector of length 2 giving the number of tuning parameters in a regularization path for PS estimation and that in cross validation for OR. |
rho.seq |
A list of two vectors giving the tuning parameters for propensity score estimation (first vector) and outcome regression (second vector). |
y |
An n x 1 vector of outcomes with missing data. |
tr |
An n x 1 vector of non-missing indicators (=1 if |
x |
An n x p matix of covariates, used in both propensity score and outcome regression models. |
ploss |
A loss function used in propensity score estimation (either "ml" or "cal"). |
yloss |
A loss function used in outcome regression (either "gaus" for continuous outcomes or "ml" for binary outcomes). |
off |
An offset value (e.g., the true value in simulations) used to calculate the z-statistic from augmented IPW estimation. |
... |
Additional arguments to |
See Details for mn.regu.cv
.
ps |
A list containing the results from fitting the propensity score model by |
fp |
The matrix of fitted propensity scores, column by column, along the PS regularization path. |
or |
A list of objects, each giving the results from fitting the outcome regression model by |
fo |
The matrix of fitted values from outcome regression based on cross validation, column by column, along the PS regularization path. |
est |
A list containing the results from augmented IPW estimation by |
rho |
A vector of tuning parameters leading to converged results in propensity score estimation. |
Tan, Z. (2020a) Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data, Biometrika, 107, 137<e2><80><93>158.
Tan, Z. (2020b) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data, Annals of Statistics, 48, 811<e2><80><93>837.
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