mn.regu.path {RCAL} | R Documentation |
Model-assisted inference for population means along a regularization path
Description
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).
Usage
mn.regu.path(fold, nrho = NULL, rho.seq = NULL, y, tr, x, ploss = "cal",
yloss = "gaus", off = 0, ...)
Arguments
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 |
tr |
An |
x |
An |
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 |
Details
See Details for mn.regu.cv
.
Value
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. |
References
Tan, Z. (2020a) Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data, Biometrika, 107, 137–158.
Tan, Z. (2020b) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data, Annals of Statistics, 48, 811–837.
Examples
data(simu.data)
n <- dim(simu.data)[1]
p <- dim(simu.data)[2]-2
y <- simu.data[,1]
tr <- simu.data[,2]
x <- simu.data[,2+1:p]
x <- scale(x)
# missing data
y[tr==0] <- NA
mn.path.rcal <- mn.regu.path(fold=5*c(0,1), nrho=(1+10)*c(1,1), y=y, tr=tr, x=x,
ploss="cal", yloss="gaus")
mn.path.rcal$est