spatialSPsurv {BayesSPsurv} | R Documentation |

Markov Chain Monte Carlo (MCMC) to run time-varying Bayesian split population survival model with spatial frailties.

Returns a summary of a exchangeSPsurv object via `summary.mcmc`

.

Print method for a `spatialSPsurv`

x.

Returns a plot of a spatialSPsurv object via `plot.mcmc`

.

spatialSPsurv( duration, immune, Y0, LY, S, A, data, N, burn, thin, w = c(1, 1, 1), m = 10, ini.beta = 0, ini.gamma = 0, ini.W = 0, ini.V = 0, form = c("Weibull", "exponential", "loglog"), prop.varV, prop.varW, id_WV = colnames(A) ) ## S3 method for class 'spatialSPsurv' summary(object, parameter = character(), ...) ## S3 method for class 'spatialSPsurv' print(x, ...) ## S3 method for class 'spatialSPsurv' plot(x, parameter = character(), ...)

`duration` |
survival stage equation written in a formula of the form Y ~ X1 + X2 + ... where Y is duration until failure or censoring. |

`immune` |
split stage equation written in a formula of the form C ~ Z1 + Z2 + ... where C is a binary indicator of immunity. |

`Y0` |
the elapsed time since inception until the beginning of time period (t-1). |

`LY` |
last observation year (coded as 1; 0 otherwise) due to censoring or failure. |

`S` |
spatial information (e.g. district ID) for each observation that matches the spatial matrix row/column information. |

`A` |
an a times a spatial weights matrix where a is the number of unique spatial units (S) load as a separate file. |

`data` |
data.frame. |

`N` |
number of MCMC iterations. |

`burn` |
burn-in to be discarded. |

`thin` |
thinning to prevent from autocorrelation. |

`w` |
size of the slice in the slice sampling for (betas, gammas, rho). Write it as a vector. E.g. c(1,1,1). |

`m` |
limit on steps in the slice sampling. A vector of values for beta, gamma, rho. |

`ini.beta` |
initial value for the parameter vector beta. By default is 0. |

`ini.gamma` |
initial value for the parameter vector gamma. By default is 0. |

`ini.W` |
initial value for the parameter vector W. By default is 0. |

`ini.V` |
initial value for the parameter vector V. By default is 0. |

`form` |
type of parametric model (Weibull, Exponential, or Log-Logistic). |

`prop.varV` |
proposal for variance of V in Metropolis-Hastings. |

`prop.varW` |
proposal for variance of W in Metropolis-Hastings. |

`id_WV` |
vector of type character that modifies the colnames of W and V in the modelâ€™s result. By default is |

`object` |
an object of class |

`parameter` |
one of five parameters of the |

`...` |
additional parameter. |

`x` |
an object of class |

spatialSPsurv returns an object of class `"spatialSPsurv"`

.

A `"spatialSPsurv"`

object has the following elements:

`betas` |
matrix, numeric values of the posterior for each variable in the duration equation . |

`gammas` |
matrix, numeric values of the posterior for each variable in the immune equation. |

`rho` |
vector, numeric values of rho. |

`lambda` |
vector, numeric values of lambda. |

`delta` |
vector, numeric values of delta. |

`W` |
matrix, numeric values of the posterior for Ws. |

`V` |
matrix, numeric values of the posterior for Vs. |

`X` |
matrix of X's variables. |

`Z` |
matrix of Z's variables. |

`Y` |
vector of ‘Y’. |

`Y0` |
vector of ‘Y0’. |

`C` |
vector of ‘C’. |

`S` |
vector of ‘S’. |

`ini.beta` |
numeric initial values of beta. |

`ini.gamma` |
numeric initial values of gamma. |

`ini.W` |
numeric initial values of W. |

`ini.V` |
numeric initial values of V. |

`form` |
character, type of distribution. |

`call` |
description for the model to be estimated. |

list. Empirical mean, standard deviation and quantiles for each variable.

list. Empirical mean, standard deviation and quantiles for each variable.

walter <- spduration::add_duration(Walter_2015_JCR,"renewed_war", unitID = "ccode", tID = "year", freq = "year", ongoing = FALSE) walter <- spatial_SA(data = walter, var_ccode = "ccode", threshold = 800L) set.seed(123456) model <- spatialSPsurv( duration = duration ~ fhcompor1 + lgdpl + comprehensive + victory + instabl + intensityln + ethfrac + unpko, immune = cured ~ fhcompor1 + lgdpl + victory, Y0 = 't.0', LY = 'lastyear', S = 'sp_id' , data = walter[[1]], N = 100, burn = 10, thin = 10, w = c(1,1,1), m = 10, form = "Weibull", prop.varV = 1e-05, prop.varW = 1e-05, A = walter[[2]] ) print(model) summary(model, parameter = "betas") # plot(model)

[Package *BayesSPsurv* version 0.1.4 Index]