wtrg_est {causaldrf} R Documentation

## The weighted regression estimator

### Description

This method uses weight matrices to estimate parameters for an ADRF with quadratic or linear fits.

### Usage

```wtrg_est(Y,
treat,
covar_formula,
data,
e_treat_1,
e_treat_2,
e_treat_3,
e_treat_4,
degree)
```

### Arguments

 `Y` is the output `treat` is the treatment variable `covar_formula` is the formula for the covariates model of the form: ~ X.1 + .... `data` will contain all the data: X, treat, and Y `e_treat_1` is estimated treatment `e_treat_2` is estimated treatment squared `e_treat_3` is estimated treatment cubed `e_treat_4` is estimated treatment to the fourth `degree` is 1 for linear fit and 2 for quadratic fit

### Details

This function estimates the ADRF by the method described in Schafer and Galagate (2015) which uses weight matrices to adjust for possible bias.

### Value

`wtrg_est` returns an object of class "causaldrf", a list that contains the following components:

 `param` the estimated parameters. `call` the matched call.

### References

Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response models. Manuscript in preparation.

### See Also

`iptw_est`, `ismw_est`, `reg_est`, `aipwee_est`, `wtrg_est`, etc. for other estimates.

`t_mod`, `overlap_fun` to prepare the `data` for use in the different estimates.

### Examples

```## Example from Schafer (2015).

example_data <- sim_data

t_mod_list <- t_mod(treat = T,
treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
data = example_data,
treat_mod = "Normal")

cond_exp_data <- t_mod_list\$T_data
full_data <- cbind(example_data, cond_exp_data)

wtrg_list <- wtrg_est(Y = Y,
treat = T,
covar_formula = ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
data = example_data,
e_treat_1 = full_data\$est_treat,
e_treat_2 = full_data\$est_treat_sq,
e_treat_3 = full_data\$est_treat_cube,
e_treat_4 = full_data\$est_treat_quartic,
degree = 1)

sample_index <- sample(1:1000, 100)

plot(example_data\$T[sample_index],
example_data\$Y[sample_index],
xlab = "T",
ylab = "Y",
main = "weighted regression estimate")

abline(wtrg_list\$param,
wtrg_list\$param,
lty = 2,
lwd = 2,
col = "blue")

legend('bottomright',
"weighted regression estimate",
lty = 2,
lwd = 2,
col = "blue",
bty='Y',
cex=1)

rm(example_data, t_mod_list, cond_exp_data, full_data, wtrg_list, sample_index)
```

[Package causaldrf version 0.3 Index]