Contents
Quickstart¶
m <- lvm(y ~ a+x, a~x)
distribution(m,~ a+y) <- binomial.lvm()
d <- sim(m,1e3,seed=1)
head(d)
y a x
1 0 1 -0.6264538
2 1 0 0.1836433
3 1 1 -0.8356286
4 1 1 1.5952808
5 0 1 0.3295078
6 1 0 -0.8204684
library(targeted)
a <- ace(y ~ a, nuisance=~x, data=d)
summary(a)
Augmented Inverse Probability Weighting estimator
Response y (Outcome model: logistic regression):
y ~ x
Exposure a (Propensity model: logistic regression):
a ~ x
Estimate Std.Err 2.5% 97.5% P-value
a=0 0.48506 0.02626 0.4336 0.5365 3.458e-76
a=1 0.67794 0.02225 0.6343 0.7215 6.005e-204
Outcome model:
(Intercept) 0.44427 0.07306 0.3011 0.5875 1.196e-09
x 1.06929 0.08537 0.9020 1.2366 5.408e-36
Propensity model:
(Intercept) 0.06214 0.09258 -0.1193 0.2436 5.021e-01
x -0.92905 0.15311 -1.2291 -0.6289 1.297e-09
Average Causal Effect (constrast: 'a=0' vs. 'a=1'):
Estimate Std.Err 2.5% 97.5% P-value
RR 0.7155 0.04356 0.6301 0.8009 1.259e-60
OR 0.4475 0.06268 0.3246 0.5703 9.383e-13
RD -0.1929 0.03295 -0.2575 -0.1283 4.791e-09
Note
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Important
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Tip
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Warning
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