Description Usage Arguments Value Author(s) References Examples
Simulates example data used in Bondell and Reich (2012), Wang et. al (2012), and Wilson and Reich (2014).
1 | SimExample(n = 100, p, model, rho)
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n |
Number of observations. |
p |
The total number of covariates (including the exposure of interest for WPD2). |
model |
What model to simulate data from. WPD2 is design 2 from Wang et. al (2012). BR1 and BR2 are designs 1 and 2 from Bondell and Reich (2012). |
rho |
This specifies the correlation between covariates in WPD2 and BR2. |
y |
n vector of responses. |
X |
n vector of exposures for WPD and the n x p design matrix for BR1 and BR2. |
U |
n x p matrix of potential confounders for WPD2.This is missing for BR1 and BR2. |
p |
Total number of potential confounders |
beta |
The regression coeficients. For WPD2 the first beta corresponds to X. |
rho |
Returns rho. |
model |
Returns model. |
Ander Wilson, Howard D. Bondell, and Brian J. Reich
Bondell, H. D. and Reich, B. J. (2012). Consistent high-dimensional Bayesian variable selection via penalized credible regions. J. Am. Statist. Assoc. 107, 1610-1624.
Wang, C., Parmigiani, G., and Dominici, F. (2012). Bayesian effect estimation accounting for adjustment uncertainty. Biometrics 68, 661-671.
Wilson A., Reich B. J. (2014). Confounder selection via penalized credible regions. Biometrics 70: 852-861.
1 2 3 | set.seed(1234)
dat <- SimExample(500,model="BR1")
lm.fit <- lm(dat$y~dat$X)
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