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, form = c("Weibull", "exponential", "loglog"), prop.var, id_WV = colnames(A) ) # S3 method for spatialSPsurv summary(object, parameter = character(), ...) # S3 method for spatialSPsurv print(x, ...) # S3 method for 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. |
| form | type of parametric model (Weibull, Exponential, or Log-Logistic). |
| prop.var | proposal variance for Metropolis-Hastings. |
| id_WV | vector of type character that modifies the colnames of W and V in the model’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:
matrix, numeric values of the posterior for each variable in the duration equation .
matrix, numeric values of the posterior for each variable in the immune equation.
vector, numeric values of rho.
vector, numeric values of lambda.
vector, numeric values of delta.
matrix, numeric values of the posterior for Ws.
matrix, numeric values of the posterior for Vs.
matrix of X's variables.
matrix of Z's variables.
vector of `Y'.
vector of `Y0'.
vector of `C'.
vector of `S'.
character, type of distribution.
description for the model to be estimated.
# \donttest{ walter <- spduration::add_duration(Walter_2015_JCR,"renewed_war", unitID = "ccode", tID = "year", freq = "year", ongoing = FALSE)#> Warning: Converting to 'Date' class with yyyy-06-30walter <- 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.var = 1e-05, A = walter[[2]] ) print(model)#> Call: #> spatialSPsurv(duration = duration ~ fhcompor1 + lgdpl + comprehensive + #> victory + instabl + intensityln + ethfrac + unpko, immune = cured ~ #> fhcompor1 + lgdpl + victory, Y0 = "t.0", LY = "lastyear", #> S = "sp_id", A = walter[[2]], data = walter[[1]], N = 100, #> burn = 10, thin = 10, w = c(1, 1, 1), m = 10, form = "Weibull", #> prop.var = 1e-05) #> #> #> Iterations = 1:9 #> Thinning interval = 1 #> Number of chains = 1 #> Sample size per chain = 9 #> #> Empirical mean and standard deviation for each variable, #> plus standard error of the mean: #> #> #> Duration equation: #> Mean SD Naive SE Time-series SE #> (Intercept) -0.007201969 0.66130836 0.22043612 0.26789018 #> fhcompor1 -1.061114953 0.41744747 0.13914916 0.13914916 #> lgdpl 0.165102666 0.05750428 0.01916809 0.01804033 #> comprehensive -0.900192682 0.24683578 0.08227859 0.08227859 #> victory 0.459614177 0.36403873 0.12134624 0.12134624 #> instabl 0.993314336 0.61545544 0.20515181 0.20515181 #> intensityln 0.077556779 0.11611075 0.03870358 0.08824995 #> ethfrac 0.287245928 0.40197028 0.13399009 0.07084927 #> unpko 0.930642210 0.72474516 0.24158172 0.24158172 #> #> Inmune equation: #> Mean SD Naive SE Time-series SE #> (Intercept) -1.8859005 4.217679 1.4058929 1.4058929 #> fhcompor1 -0.6911851 3.279678 1.0932261 1.0932261 #> lgdpl -3.1980281 3.281575 1.0938583 1.0938583 #> victory 1.6578210 2.834405 0.9448017 0.9448017 #>#> #> Iterations = 1:9 #> Thinning interval = 1 #> Number of chains = 1 #> Sample size per chain = 9 #> #> 1. Empirical mean and standard deviation for each variable, #> plus standard error of the mean: #> #> Mean SD Naive SE Time-series SE #> (Intercept) -0.007202 0.6613 0.22044 0.26789 #> fhcompor1 -1.061115 0.4174 0.13915 0.13915 #> lgdpl 0.165103 0.0575 0.01917 0.01804 #> comprehensive -0.900193 0.2468 0.08228 0.08228 #> victory 0.459614 0.3640 0.12135 0.12135 #> instabl 0.993314 0.6155 0.20515 0.20515 #> intensityln 0.077557 0.1161 0.03870 0.08825 #> ethfrac 0.287246 0.4020 0.13399 0.07085 #> unpko 0.930642 0.7247 0.24158 0.24158 #> #> 2. Quantiles for each variable: #> #> 2.5% 25% 50% 75% 97.5% #> (Intercept) -0.72636 -0.474098 -0.14557 0.3283 1.0874 #> fhcompor1 -1.79121 -1.006467 -0.88788 -0.7696 -0.7236 #> lgdpl 0.09600 0.125091 0.16211 0.2041 0.2596 #> comprehensive -1.23909 -1.099777 -0.82024 -0.7503 -0.5811 #> victory 0.05352 0.255383 0.39102 0.7578 1.0533 #> instabl 0.13204 0.795864 0.83648 1.1202 2.0308 #> intensityln -0.05380 -0.016493 0.04289 0.1900 0.2475 #> ethfrac -0.24589 -0.009066 0.31607 0.6467 0.7384 #> unpko -0.33668 0.531894 1.05486 1.1717 1.9152 #># plot(model) # }