Description Usage Arguments Value References See Also Examples
This function ensures that the units overlap according to the estimated gps
values. The overlapping dataset depends on the number of classes
n_class
to subclassify on.
1 2 3 4 5 6 7 8  overlap_fun(Y,
treat,
treat_formula,
data_set,
n_class,
treat_mod,
link_function,
...)

Y 
is the the name of the outcome variable contained in 
treat 
is the name of the treatment variable contained in

treat_formula 
an object of class "formula" (or one that can be
coerced to that class) that regresses 
data_set 
is a dataframe containing 
n_class 
is the number of classes to split 
treat_mod 
a description of the error distribution to be used in the
model for treatment. Options include: 
link_function 
is either "log", "inverse", or "identity" for the
"Gamma" 
... 
additional arguments to be passed to the treatment regression function 
overlap_fun
returns a list containing the following
elements:
overlap_dataset 
dataframe containing overlapping data. 
median_vec 
a vector containing median values. 
overlap_treat_result 
the resulting treatment fit. 
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric doseresponse models. Manuscript in preparation.
Bia, Michela, et al. "A Stata package for the application of semiparametric estimators of dose response functions." Stata Journal 14.3 (2014): 580604.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Example from Schafer (2015).
example_data < sim_data
overlap_list < overlap_fun(Y = Y,
treat = T,
treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
data_set = example_data,
n_class = 3,
treat_mod = "Normal")
overlapped_data < overlap_list$overlap_dataset
summary(overlapped_data)
rm(example_data, overlap_list, overlapped_data)

A.1 A.2 A.3 A.4
Min. :2.16090 Min. :3.62468 Min. :2.37188 Min. :3.49492
1st Qu.:0.63657 1st Qu.:0.65190 1st Qu.:0.65857 1st Qu.:0.73409
Median :0.04458 Median : 0.01898 Median :0.02908 Median : 0.03106
Mean :0.04038 Mean :0.01385 Mean :0.02268 Mean :0.01967
3rd Qu.: 0.53748 3rd Qu.: 0.66500 3rd Qu.: 0.59032 3rd Qu.: 0.61554
Max. : 2.37493 Max. : 2.83913 Max. : 2.79272 Max. : 3.65007
A.5 A.6 A.7 A.8
Min. :3.25750 Min. :3.128066 Min. :3.041520 Min. :3.218881
1st Qu.:0.68608 1st Qu.:0.636384 1st Qu.:0.688681 1st Qu.:0.652851
Median :0.03000 Median : 0.068285 Median :0.013420 Median :0.008034
Mean :0.03602 Mean : 0.006962 Mean : 0.005488 Mean :0.019213
3rd Qu.: 0.63969 3rd Qu.: 0.634998 3rd Qu.: 0.629184 3rd Qu.: 0.631477
Max. : 2.85290 Max. : 2.812570 Max. : 3.051767 Max. : 3.081647
B.1 B.2 B.3 B.4
Min. :2.77129 Min. :1.568 Min. :18.09 Min. :0.0178
1st Qu.:0.46805 1st Qu.:2.066 1st Qu.:28.60 1st Qu.:0.3031
Median : 0.12624 Median :2.255 Median :33.33 Median :0.4296
Mean : 0.07015 Mean :2.273 Mean :33.68 Mean :0.4260
3rd Qu.: 0.67434 3rd Qu.:2.452 3rd Qu.:38.04 3rd Qu.:0.5527
Max. : 1.86019 Max. :3.703 Max. :58.17 Max. :0.8705
B.5 B.6 B.7 B.8
Min. :0.0003746 Min. : 0.1082 Min. :0.0000 Min. :0.0000
1st Qu.:0.2490920 1st Qu.: 3.2006 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.4845486 Median : 4.7494 Median :0.0000 Median :0.0000
Mean :0.4862348 Mean : 5.2450 Mean :0.2344 Mean :0.2309
3rd Qu.:0.7206392 3rd Qu.: 6.6311 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :0.9998761 Max. :19.2801 Max. :1.0000 Max. :1.0000
T Y Theta.1 Theta.2
Min. : 8.441 Min. :17.08 Min. :32.26 Min. :1.70667
1st Qu.:11.027 1st Qu.:41.08 1st Qu.:45.56 1st Qu.:0.43309
Median :11.898 Median :49.46 Median :49.57 Median :0.03767
Mean :11.927 Mean :49.88 Mean :49.68 Mean :0.01435
3rd Qu.:12.752 3rd Qu.:58.05 3rd Qu.:53.77 3rd Qu.: 0.39385
Max. :15.685 Max. :86.54 Max. :66.87 Max. : 1.99159
support_indices
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.949
3rd Qu.:3.000
Max. :3.000
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