HSI
From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press
(c) 2017, Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida
you are kindly asked to include the complete citation if you used this material in a publication
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Code 5.15 Synthetic data from logistic model in R
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set.seed(13979)
nobs <- 5000
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x1 <- rbinom(nobs, size = 1, 0.6)
x2 <- runif(nobs)
xb <- 2 + 0.75*x1 - 5*x2
exb <- 1/(1+exp(-xb))
by <- rbinom(nobs, size = 1, prob = exb)
logitmod <- data.frame(by, x1, x2)
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Code 5.16 - Logistic model using R
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library(MCMCpack)
myL <- MCMClogit(by ~ x1 + x2,
burnin = 5000,
mcmc = 10000,
data = logitmod)
summary(myL)
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Output on screen:
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Iterations = 5001:15000
Thinning interval = 1
Number of chains = 1
Sample size per chain = 10000
1. Empirical mean and standard deviation for each variable,
plus standard error of the mean:
Mean SD Naive SE Time-series SE
(Intercept) 1.9408 0.08314 0.0008314 0.002651
x1 0.8506 0.07183 0.0007183 0.002356
x2 -5.0118 0.14463 0.0014463 0.004709
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2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
(Intercept) 1.7772 1.884 1.943 1.9970 2.096
x1 0.7116 0.803 0.850 0.8963 1.000
x2 -5.2904 -5.109 -5.016 -4.9131 -4.719