Description Usage Arguments Value References Examples

This function extends the main function `hsaft`

to create correlation among covariates.

1 2 3 4 | ```
hsaftallcorr(ct, X, method.tau = c("fixed", "truncatedCauchy",
"halfCauchy"), tau = 1, method.sigma = c("fixed", "Jeffreys"),
Sigma2 = 1, burn = 1000, nmc = 5000, thin = 1, alpha = 0.05, r,
n.seq, pk)
``` |

`ct` |
Response, a |

`X` |
Matrix of covariates, dimension |

`method.tau` |
Method for handling |

`tau` |
Use this argument to pass the (estimated) value of |

`method.sigma` |
Select "Jeffreys" for full Bayes with Jeffrey's prior on the error
variance |

`Sigma2` |
A fixed value for the error variance |

`burn` |
Number of burn-in MCMC samples. Default is 1000. |

`nmc` |
Number of posterior draws to be saved. Default is 5000. |

`thin` |
Thinning parameter of the chain. Default is 1 (no thinning). |

`alpha` |
Level for the credible intervals. For example, alpha = 0.05 results in 95% credible intervals. |

`r` |
number of groups. |

`n.seq` |
a vector of sample sizes for all groups. |

`pk` |
number of covariates in each group. |

`SurvivalHat` |
Predictive survival probability. |

`LogTimeHat` |
Predictive log time. |

`BetaHat` |
Posterior mean of Beta, a |

`LeftCI` |
The left bounds of the credible intervals. |

`RightCI` |
The right bounds of the credible intervals. |

`BetaMedian` |
Posterior median of Beta, a |

`Sigma2Hat` |
Posterior mean of error variance |

`TauHat` |
Posterior mean of global scale parameter tau, a positive scalar. If method.tau = "fixed" is used, this value will be equal to the user-selected value of tau passed to the function. |

`BetaSamples` |
Posterior samples of Beta. |

`TauSamples` |
Posterior samples of tau. |

`Sigma2Samples` |
Posterior samples of Sigma2. |

`BGHat` |
Posterior samples of b which is a part of the mean of |

`BPHat` |
Posterior samples of b which is the other part of the mean of |

`LikelihoodSamples` |
Posterior Samples of likelihood. |

Stephanie van der Pas, James Scott, Antik Chakraborty and Anirban Bhattacharya (2016). horseshoe: Implementation of the Horseshoe Prior. R package version 0.1.0. https://CRAN.R-project.org/package=horseshoe

Arnab Kumar Maity, Anirban Bhattacharya, Bani K. Mallick, and Veerabhadran Baladandayuthapani (2017). Joint Bayesian Estimation and Variable Selection for TCPA Protein Expression Data

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ```
## Not run: # Examples for hsaftallcorr function
burnin <- 500 # number of burnin
nmc <- 1000 # number of Markov Chain samples
y.sd <- 1 # standard deviation of the data
p <- 80 # number of covariates
r <- 5 # number of groups
p <- 80 # number of covariate in each group
n1 <- 40 # sample size of 1st group
n2 <- 50 # sample size of 2nd group
n3 <- 70 # sample size of 3rd group
n4 <- 100 # sample size of 4th group
n5 <- 120 # sample size of 5th group
n <- sum(c(n1, n2, n3, n4, n5)) # total sample size
n.seq <- c(n1, n2, n3, n4, n5)
Beta <- matrix(smoothmest::rdoublex(p * r), nrow = r, ncol = p, byrow = TRUE)
# from double exponential distribution
beta <- as.vector(t(Beta)) # vectorize Beta
x1 <- mvtnorm::rmvnorm(n1, mean = rep(0, p))
x2 <- mvtnorm::rmvnorm(n2, mean = rep(0, p))
x3 <- mvtnorm::rmvnorm(n3, mean = rep(0, p))
x4 <- mvtnorm::rmvnorm(n4, mean = rep(0, p))
x5 <- mvtnorm::rmvnorm(n5, mean = rep(0, p)) # from multivariate normal distribution
y.mu1 <- x1 %*% Beta[1, ]
y.mu2 <- x2 %*% Beta[2, ]
y.mu3 <- x3 %*% Beta[3, ]
y.mu4 <- x4 %*% Beta[4, ]
y.mu5 <- x5 %*% Beta[5, ]
y1 <- stats::rnorm(n1, mean = y.mu1, sd = y.sd)
y2 <- stats::rnorm(n2, mean = y.mu2, sd = y.sd)
y3 <- stats::rnorm(n3, mean = y.mu3, sd = y.sd)
y4 <- stats::rnorm(n4, mean = y.mu4, sd = y.sd)
y5 <- stats::rnorm(n5, mean = y.mu5, sd = y.sd)
y <- c(y1, y2, y3, y4, y5)
x <- Matrix::bdiag(x1, x2, x3, x4, x5)
X <- as.matrix(x)
y <- as.numeric(as.matrix(y)) # from normal distribution
T <- exp(y) # AFT model
C <- rgamma(n, shape = 1.75, scale = 3) # censoring time
time <- pmin(T, C) # observed time is min of censored and true
status = time == T # set to 1 if event is observed
ct <- as.matrix(cbind(time = time, status = status)) # censored time
posterior.fit <- hsaftallcorr(ct, X, method.tau = "truncatedCauchy", method.sigma = "Jeffreys",
burn = burnin, nmc = nmc,
r = r, n.seq = n.seq, pk = p)
summary(posterior.fit$BetaHat)
## End(Not run)
``` |

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