/D-probability

D-probabilities of parametric models using nonparametric model reference

Primary LanguageRMIT LicenseMIT

D-probability

D-probabilities of parametric models using nonparametric model reference.

Examples using Ozone data

The followling demonstration is also in the "example.R" script. The returned variables are saved in "demo.RData" for quick access.

Calculate two types of D-probabilities (absolute and conditional)

rm(list = ls())
source("sourceCode.R")

### Ozone data
library(earth)
data("ozone1")
y = ozone1[, 'O3']
x = as.matrix(ozone1[, -c(1, 10)]) 

# rescale x such that x \in [0, 1]^d 
rescale_x = x
for (i in 1:ncol(x)){
  rescale_x[,i] = (x[, i] - min(x[,i])) / (max(x[,i]) - min(x[,i]))
}
x = rescale_x

# fit the reference model 
g.ab = 0 
ret = GP.fit(x, y, a = g.ab, b = g.ab, jitter = 0)
# mean((y - ret$detail$Y.hat)^2) # residual sum of squares 

p = ncol(x)
m <- t(sapply(1:2^p,function(x){ as.integer(intToBits(x))[1:p]}))
idx = lapply(1:2^p, function(k) which(m[k, ] == 1))

# calculate Kullback-Leibler divergences for all sub-models 
# g = Inf means we use flat priors for coefficients
KL.all = matrix(NA, nrow = 2, ncol = 2^p) 
for (g.type in 1:2){
  KL.all[g.type, ] = unlist(lapply(idx, function(k)
    data2KL(X.j = cbind(1, x[, k]), y, g = Inf, GP = ret, type = g.type, a = g.ab, b = g.ab)))
}

Use absolute D-probabilities to assess goodness-of-fit

n = length(y)
abs.prob = exp(-n * KL.all)
rel.prob = abs.prob / rowSums(abs.prob)

## chose the best model according to D-probabilities 
# use (type 1, type 2) D-probability 
idx[c(which.max(abs.prob[1, ]), which.max(abs.prob[2, ]))]

## use absolute D-probabilities to assess model fitting 
c(Type1=max(abs.prob[1,]), Type2=max(abs.prob[2,]))
# small D-probabilities suggest a lack of fit for all models 
# output: 
#                                   
#       Type1        Type2 
# 5.608455e-27 1.653213e-22

Out-of-sample prediction

## out-of-sample prediction error for selected models and the reference model 
set.seed(20161)
n = length(y)
x_train_id = sample(1:n, round(0.5 * n))
object = GP.fit(x[x_train_id, ], y[x_train_id], a = 0, b = 0, jitter = 0)
yhat_ref = predict(object, x_train = x[x_train_id, ],x_new = x[-x_train_id, ])
RMSE_ref = sqrt(mean((y[-x_train_id] - yhat_ref)^2))


x_select = x[, idx[[which.max(abs.prob[1, ])]]]
object = lm(y[x_train_id] ~ x_select[x_train_id, ])
yhat1 = cbind(1, x_select[-x_train_id, ]) %*% coef(object)
RMSE1 = sqrt(mean((y[-x_train_id] - yhat1)^2))

x_select = x[, idx[[which.max(abs.prob[2, ])]]]
object = lm(y[x_train_id] ~ x_select[x_train_id, ])
yhat2 = cbind(1, x_select[-x_train_id, ]) %*% coef(object)
RMSE2 = sqrt(mean((y[-x_train_id] - yhat2)^2))

# compare root MSE
c(Type1=RMSE1, Type2 = RMSE2, Reference=RMSE_ref)
# output: 
#     Type1     Type2 Reference 
#  4.384930  4.391196  3.910962

Reference:

Li, M. and Dunson, D. (2018). Comparing and weighting imperfect models using D-probabilities. Revision submitted. arXiv:1611.01241v3