Compute Credible (Bayesian Confidence) Intervals for a BAS predict object
Source:R/confint.R
confint.pred.RdCompute credible intervals for in-sample or out of sample prediction or for the regression function
Usage
# S3 method for class 'pred.bas'
confint(object, parm, level = 0.95, nsim = 10000, ...)Arguments
- object
an object created by
predict.bas- parm
character variable, "mean" or "pred". If missing parm='pred'.
- level
the nominal level of the (point-wise) credible interval
- nsim
number of Monte Carlo simulations for sampling methods with BMA
- ...
optional arguments to pass on to next function call; none at this time.
Value
a matrix with lower and upper level * 100 percent credible intervals for either the mean of the regression function or predicted values.
Details
This constructs approximate 95 percent Highest Posterior Density intervals for 'pred.bas' objects. If the estimator is based on model selection, the intervals use a Student t distribution using the estimate of g. If the estimator is based on BMA, then nsim draws from the mixture of Student t distributions are obtained with the HPD interval obtained from the Monte Carlo draws.
See also
Other bas methods:
BAS,
bas.lm(),
coef.bas(),
confint.coef.bas(),
diagnostics(),
fitted.bas(),
force.heredity.bas(),
image.bas(),
plot.confint.bas(),
predict.bas(),
predict.basglm(),
summary.bas(),
update.bas(),
variable.names.pred.bas()
Other CI methods:
confint.coef.bas(),
plot.confint.bas()
Examples
data("Hald")
hald.gprior = bas.lm(Y~ ., data=Hald, alpha=13, prior="g-prior")
hald.pred = predict(hald.gprior, estimator="BPM", predict=FALSE, se.fit=TRUE)
confint(hald.pred, parm="mean")
#> 2.5% 97.5% mean
#> [1,] 72.85939 86.44363 79.65151
#> [2,] 69.50215 79.45478 74.47846
#> [3,] 101.93102 108.91264 105.42183
#> [4,] 85.14928 94.51420 89.83174
#> [5,] 89.98634 101.26964 95.62799
#> [6,] 101.30948 107.88283 104.59616
#> [7,] 97.81813 109.19556 103.50684
#> [8,] 71.46153 82.55525 77.00839
#> [9,] 87.73810 96.41333 92.07571
#> [10,] 106.51357 121.70394 114.10876
#> [11,] 77.32978 88.03488 82.68233
#> [12,] 106.79101 115.29472 111.04286
#> [13,] 105.64736 115.28746 110.46741
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"
confint(hald.pred) #default
#> 2.5% 97.5% pred
#> [1,] 67.79843 91.50459 79.65151
#> [2,] 63.56396 85.39296 74.47846
#> [3,] 95.09960 115.74406 105.42183
#> [4,] 79.04805 100.61543 89.83174
#> [5,] 84.39452 106.86146 95.62799
#> [6,] 94.34116 114.85115 104.59616
#> [7,] 92.24966 114.76402 103.50684
#> [8,] 65.82223 88.19456 77.00839
#> [9,] 81.43722 102.71421 92.07571
#> [10,] 101.77792 126.43959 114.10876
#> [11,] 71.59123 93.77342 82.68233
#> [12,] 100.43905 121.64668 111.04286
#> [13,] 99.62326 121.31156 110.46741
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"
hald.pred = predict(hald.gprior, estimator="BMA", predict=FALSE, se.fit=TRUE)
confint(hald.pred)
#> 2.5% 97.5% pred
#> [1,] 67.23406 91.12380 79.68307
#> [2,] 63.17610 85.99745 74.69127
#> [3,] 94.71629 116.33984 105.63258
#> [4,] 78.78921 100.87371 89.91648
#> [5,] 83.77651 106.24002 95.67480
#> [6,] 94.14218 115.25006 104.57616
#> [7,] 92.39226 115.34526 103.47945
#> [8,] 65.19871 87.67945 76.96808
#> [9,] 81.66889 103.30376 92.22184
#> [10,] 101.34079 126.39888 113.84918
#> [11,] 70.82794 93.94590 82.59035
#> [12,] 100.04372 121.73462 110.87673
#> [13,] 99.63459 121.62523 110.34001
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"