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 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.basglm(),
predict.bas(),
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,] 67.80343 91.67019 79.68307
#> [2,] 63.89289 86.54128 74.69127
#> [3,] 94.13211 116.52556 105.63258
#> [4,] 78.98885 101.07927 89.91648
#> [5,] 84.27957 107.12754 95.67480
#> [6,] 94.13806 115.06044 104.57616
#> [7,] 92.03501 115.30514 103.47945
#> [8,] 65.49721 88.40664 76.96808
#> [9,] 81.98294 103.64824 92.22184
#> [10,] 101.21526 126.93095 113.84918
#> [11,] 71.13461 93.57363 82.59035
#> [12,] 99.87997 121.74584 110.87673
#> [13,] 99.28106 121.19577 110.34001
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"