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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../_images/testfig.png

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