posteriorTime_NaiveMLE: posteriorTime_FullBayesian

In oyvble/TSDpredict: Prediction of Time Since Decomposition

Description

Calculating the posterior distribution of time for new individuals

Usage

posteriorTime_NaiveMLE(
  New_Y,
  Data_Y,
  Data_time,
  maxTime = 12,
  delta = 0.01,
  priorTime = NULL
)

Arguments

New_Y	The observed response for new individuals (nsamples x nsites) matrix (also possible with vector)
Data_Y	This is observed multi-responses (nsamples x nsites)
Data_time	This is observed times (nsamples vector)
maxTime	maximum of time (upper limit)
delta	Grid size for the time variable
priorTime	A prior distribution for the time variable (NULL means uniform distribution)

Details

This function provides posterior distribution of time, based on a multivariate model (Naive MLE approach), where time as a continuous exploratory variable from zero to maxTime

Prediction: Applying Bayes Theorem: p(time|Y) = konstant x p(Y|time) x p(time) We assume an uniform prior for p(log(time))=unif, gives p(time)=1/t

Model: p(Y|time,theta)=MVN(b0 + b1*time, SIGMA), SIGMA= COVARIANCE MATRIX where theta is estimated using maximum likelihood estimation (MLE), provided as theta_hat

Predictor p(Ynew|time)=p(Ynew|time,theta=theta_hat)

Value

ret list with posterior distribution for each new individuals (separate list for univariate prediction and all combined)

Author(s)

Oyvind Bleka

Examples

## Not run:  
ntrain = 100
ntest = 100
dat = genData(ntrain+ ntest,seed=1)
Data_Y = dat$Data_Y[1:ntrain,]
Data_time = dat$Data_time[1:ntrain]
New_Y = dat$Data_Y[-(1:ntrain),]
New_time = dat$Data_time[-(1:ntrain)]
predObj = posteriorTime_NaiveMLE(New_Y, Data_Y, Data_time)

## End(Not run)

oyvble/TSDpredict documentation built on June 28, 2020, 10:42 a.m.