nestsr: Nest Survival Rate Esitmate

In BEANSP: Bayesian Estimate of Age-specific Nest Survival Probabilities

Description

This function can handle nests with unknown nest age, nest-specific covariates(both discrete and continues covariates), and any hazard rate function(or equivelently survival rate function) as long as it is a smooth function.

Usage

nestsr(jj, nx, nn, ul, ur, zl, zr, x, y, a, b, sigma, day, enc, covar, n0, ntotal)

Arguments

jj	The number of time units(days) a nest is required to survive to be considered successful.
nx	The number of covariates.
nn	The number of observed nests (sample size).
ul	The youngest possible age that the nest could have been when first encounterd.
ur	The oldest possible age that the nest could have been when first encounterd.
zl	The smallest possible number of time units from the first encounter date to the outcome date.
zr	The largest possible number of time units from the first encounter date to the outcome date.
x	The nest-specific covariate-matrix.
y	The nest fate.
a	The specified value for hyperparameter a. The prior gamma(a,b) is for age effect variances.
b	The specified value for hyperparameter b. The prior gamma(a,b) is for age effect variances.
sigma	The specified values for hyperparameters age-effect variances.
day	The iniital values for age effect of outcome rates.
enc	The initial values for age effect of encounter rates.
covar	The initial values for the coefficients for covariates.
n0	The number of burn-in cycles.
ntotal	The number of total Gibbs cycles.

Details

The Bayesian estimate of parameter is computed from its posterior distribution which is simulated by Gibbs sampler. Users need to specify a set of initial values ,the number of burn-in cycles and the total number of Gibbs sampling cycles.

Value

The BEANSP returns the esitmate and corresponding standard deviation for all key parameters, the average age-specific survival rate and average cumulative survival rate (average over all nests), and selected age-specific survival rate and cumulative survival rate for individual nest. It also outpus a model selection criterion DIC (Spiegelhalter et al. 2002).

jj	nest period time.
enc	numerical values of estimate of encounter age effect for all age.
day	numerical values of estimate of outcome age effect for all age .
sigma	numerical values of estimate of age effect variances.
covar	numerical values of estimate of regression coefficients.
q	numerical values of estimate of age-specific outcome rates.
del	numerical values of estimate of age-specific encounter rates.
sr	numerical values of estimate of individual age-specific survival rates.
asr	numerical values of estimate of average age-specific survival rates.
casr	numerical values of estimate of average cumulative age-specific survival rates.
DIC	model selection criterion DIC value
Dbar	a expectation measure of how well the model fits the data.
pd	a measure for the effective number of parameters of the model.
trace1	numerical trace values for encounter age effect for all age.
trace2	numerical trace values for outcome age effect for all age.
trace3	numerical trace values for regression coefficients.
trace4	numerical trace values for age effect variances.
venc	standard deviation of encounter age effect for all age.
vday	standard deviation of outcome age effect for all age.
vsigma	standard deviation of age effect variances.
vcovar	standard deviation of regression coefficients.
vq	standard deviation of age-specific outcome rates.
vdel	standard deviation of age-specific outcome rates.
vsr	standard deviation of individual age-specific survival rates.
vasr	standard deviation of average age-specific survival rates.
vcasr	standard deviation of average cumulative age-specific survival rates.

Author(s)

Chong He, Yiqun Yang, Jing Cao

References

Cao, J., He, C., Suedkamp Wells, K.M., Millspaugh, J.J., and Ryan, M.R. (2009). Modeling age and nest-specific survival using a hierarchical Bayesian approach. Biometrics, 65, 1052-1062.

BEANSP documentation built on April 14, 2017, 10:11 p.m.