Prints p-values and confidence intervals to the screen for both the random and best business-as-usual allocation procedures.

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`object` |
An object of class “PTE_bootstrap_results”. |

`...` |
Parameters that are ignored. |

Adam Kapelner and Justin Bleich

Kapelner, A, Bleich, J, Cohen, ZD, DeRubeis, RJ and Berk, R (2014) Inference for Treatment Regime Models in Personalized Medicine, arXiv

bootstrap_inference

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
beta0 = 1
beta1 = -1
gamma0 = 0
gamma1 = sqrt(2 * pi)
mu_x = 0
sigsq_x = 1
sigsq_e = 1
num_boot = 20 #for speed only
n = 50 #for speed only
x = sort(rnorm(n, mu_x, sigsq_x))
noise = rnorm(n, 0, sigsq_e)
treatment = sample(c(rep(1, n / 2), rep(0, n / 2)))
y = beta0 + beta1 * x + treatment * (gamma0 + gamma1 * x) + noise
X = data.frame(treatment, x)
res = bootstrap_inference(X, y,
"lm(y ~ . + treatment * ., data = Xyleft)",
num_cores = 1,
B = num_boot,
plot = FALSE)
summary(res)
``` |

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