Predictions under model averaging or other estimators from a BMA object of class inheriting from 'bas'.
Usage
# S3 method for bas
predict(
object,
newdata,
se.fit = FALSE,
type = "link",
top = NULL,
estimator = "BMA",
na.action = na.pass,
...
)Arguments
- object
An object of class BAS, created by
bas- newdata
dataframe for predictions. If missing, then use the dataframe used for fitting for obtaining fitted and predicted values.
- se.fit
indicator for whether to compute se of fitted and predicted values
- type
Type of predictions required. "link" which is on the scale of the linear predictor is the only option currently for linear models, which for the normal model is equivalent to type='response'.
- top
a scalar integer M. If supplied, subset the top M models, based on posterior probabilities for model predictions and BMA.
- estimator
estimator used for predictions. Currently supported options include:
'HPM' the highest probability model
'BMA' Bayesian model averaging, using optionally only the 'top' models
'MPM' the median probability model of Barbieri and Berger.
'BPM' the model that is closest to BMA predictions under squared error loss. BMA may be computed using only the 'top' models if supplied- na.action
function determining what should be done with missing values in newdata. The default is to predict NA.
- ...
optional extra arguments
Value
a list of
- fit
fitted values based on the selected estimator
- Ybma
predictions using BMA, the same as fit for non-BMA methods for compatibility; will be deprecated
- Ypred
matrix of predictions under each model for BMA
- se.fit
se of fitted values; in the case of BMA this will be a matrix
- se.pred
se for predicted values; in the case of BMA this will be a matrix
- se.bma.fit
vector of posterior sd under BMA for posterior mean of the regression function. This will be NULL if estimator is not 'BMA'
- se.bma.pred
vector of posterior sd under BMA for posterior predictive values. this will be NULL if estimator is not 'BMA'
- best
index of top models included
- bestmodels
subset of bestmodels used for fitting or prediction
- best.vars
names of variables in the top model; NULL if estimator='BMA'
- df
scalar or vector of degrees of freedom for models
- estimator
estimator upon which 'fit' is based.
Details
Use BMA and/or model selection to form predictions using the top highest probability models.
See also
bas, fitted.bas,
confint.pred.bas, variable.names.pred.bas
Other predict methods:
fitted.bas(),
predict.basglm(),
variable.names.pred.bas()
Other bas methods:
BAS,
bas.lm(),
coef.bas(),
confint.coef.bas(),
confint.pred.bas(),
diagnostics(),
fitted.bas(),
force.heredity.bas(),
image.bas(),
plot.confint.bas(),
predict.basglm(),
summary.bas(),
update.bas(),
variable.names.pred.bas()
Examples
data("Hald")
hald.gprior = bas.lm(Y ~ ., data=Hald, alpha=13, prior="g-prior")
predict(hald.gprior, newdata=Hald, estimator="BPM", se.fit=TRUE)
#> $fit
#> [1] 79.65151 74.47846 105.42183 89.83174 95.62799 104.59616 103.50684
#> [8] 77.00839 92.07571 114.10876 82.68233 111.04286 110.46741
#> attr(,"model")
#> [1] 0 1 2 4
#> attr(,"best")
#> [1] 2
#> attr(,"estimator")
#> [1] "BPM"
#>
#> $Ybma
#> [,1]
#> [1,] 79.68307
#> [2,] 74.69127
#> [3,] 105.63258
#> [4,] 89.91648
#> [5,] 95.67480
#> [6,] 104.57616
#> [7,] 103.47945
#> [8,] 76.96808
#> [9,] 92.22184
#> [10,] 113.84918
#> [11,] 82.59035
#> [12,] 110.87673
#> [13,] 110.34001
#>
#> $Ypred
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 81.17036 74.83464 105.07248 89.69881 97.15898 104.45753 103.38927
#> [2,] 77.70296 74.24113 105.85537 90.46267 93.09565 104.71517 103.13993
#> [3,] 79.70437 74.40553 105.21752 89.76253 95.63309 104.57088 103.52541
#> [4,] 79.65151 74.47846 105.42183 89.83174 95.62799 104.59616 103.50684
#> [5,] 79.84321 74.31409 104.90632 89.65651 95.70301 104.52849 103.54760
#> [6,] 79.26248 74.75042 106.28314 90.16732 95.37830 104.70862 103.39893
#> [7,] 78.80123 75.69457 108.22151 90.62061 95.54467 104.84276 103.50032
#> [8,] 81.66556 77.02098 106.35099 88.18423 99.83232 103.89115 105.74346
#> [9,] 85.78065 79.39070 104.28069 87.30339 103.43704 102.66524 106.03984
#> [10,] 74.86000 80.34349 102.27744 83.77067 93.36677 100.90656 111.87354
#> [11,] 79.18965 81.38793 101.17241 82.85345 98.24138 100.43966 112.16380
#> [12,] 76.29823 80.55874 101.93127 83.25765 95.26054 100.79397 112.20198
#> [13,] 94.62219 84.21059 101.56325 101.56325 94.62219 101.56325 87.68112
#> [14,] 95.42308 95.42308 95.42308 95.42308 95.42308 95.42308 95.42308
#> [15,] 91.78308 83.03167 101.29055 101.29055 91.78308 101.74970 88.24455
#> [16,] 102.15048 91.65573 99.81831 99.81831 102.15048 98.65223 89.32357
#> [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 76.06454 91.57174 113.17222 81.59906 111.22195 111.08841
#> [2,] 78.80193 92.68123 115.80581 84.50293 110.41616 109.07906
#> [3,] 77.08557 91.98604 114.17593 82.78145 111.11959 110.53210
#> [4,] 77.00839 92.07571 114.10876 82.68233 111.04286 110.46741
#> [5,] 77.15919 91.83513 114.23530 82.88128 111.23840 110.65147
#> [6,] 76.86019 92.49134 113.99208 82.44826 110.67744 110.08148
#> [7,] 76.13446 93.59932 112.64184 81.53107 109.86900 109.49863
#> [8,] 74.60469 93.86382 106.77052 80.21897 110.61958 111.73373
#> [9,] 74.19437 93.55892 101.91430 79.36984 110.13525 112.42979
#> [10,] 85.82697 100.90656 98.16482 92.68133 107.76092 107.76092
#> [11,] 82.85345 99.70690 94.57759 89.44827 108.50000 109.96552
#> [12,] 84.53056 100.50527 96.78718 91.37187 108.21267 108.79006
#> [13,] 84.21059 85.94586 118.91590 84.21059 101.56325 99.82798
#> [14,] 95.42308 95.42308 95.42308 95.42308 95.42308 95.42308
#> [15,] 86.24572 86.55641 120.92687 86.70486 101.74970 99.14326
#> [16,] 83.49316 88.15749 104.48264 82.32707 98.65223 99.81831
#>
#> $postprobs
#> [1] 2.432256e-01 1.684081e-01 1.312165e-01 1.224293e-01 1.220358e-01
#> [6] 1.145513e-01 6.888252e-02 2.709377e-02 1.481347e-03 2.891971e-04
#> [11] 2.559516e-04 5.597869e-05 4.790052e-05 1.177702e-05 1.000762e-05
#> [16] 5.009504e-06
#>
#> $se.fit
#> 1 2 3 4 5 6 7 8
#> 3.117350 2.283957 1.602160 2.149087 2.589321 1.508471 2.610923 2.545817
#> 9 10 11 12 13
#> 1.990817 3.485929 2.456636 1.951456 2.212238
#>
#> $se.pred
#> 1 2 3 4 5 6 7 8
#> 5.440156 5.009380 4.737547 4.949344 5.155775 4.706689 5.166658 5.134065
#> 9 10 11 12 13
#> 4.882702 5.659428 5.090431 4.866787 4.977090
#>
#> $se.bma.fit
#> NULL
#>
#> $se.bma.pred
#> NULL
#>
#> $df
#> [1] 12
#>
#> $best
#> [1] 2
#>
#> $bestmodel
#> [1] 0 1 2 4
#>
#> $best.vars
#> [1] "Intercept" "X1" "X2" "X4"
#>
#> $estimator
#> [1] "BPM"
#>
#> attr(,"class")
#> [1] "pred.bas"
# same as fitted
fitted(hald.gprior,estimator="BPM")
#> [1] 79.65151 74.47846 105.42183 89.83174 95.62799 104.59616 103.50684
#> [8] 77.00839 92.07571 114.10876 82.68233 111.04286 110.46741
# default is BMA and estimation of mean vector
hald.bma = predict(hald.gprior, top=5, se.fit=TRUE)
confint(hald.bma)
#> 2.5% 97.5% pred
#> [1,] 67.99502 91.55410 79.74246
#> [2,] 63.28937 85.43012 74.50010
#> [3,] 94.63112 116.09951 105.29268
#> [4,] 78.72267 100.97345 89.88693
#> [5,] 83.89443 106.32644 95.57177
#> [6,] 94.17953 114.85494 104.56409
#> [7,] 91.87656 114.50490 103.40145
#> [8,] 65.23186 88.11567 77.13668
#> [9,] 81.36200 102.91102 91.99731
#> [10,] 101.58940 126.61307 114.21325
#> [11,] 71.81605 94.41489 82.78446
#> [12,] 99.93465 121.89908 111.00723
#> [13,] 99.35807 121.10243 110.40160
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"
hald.bpm = predict(hald.gprior, newdata=Hald[1,],
se.fit=TRUE,
estimator="BPM")
confint(hald.bpm)
#> 2.5% 97.5% pred
#> [1,] 67.74454 91.66421 79.70437
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"
# extract variables
variable.names(hald.bpm)
#> [1] "Intercept" "X1" "X2" "X3" "X4"
hald.hpm = predict(hald.gprior, newdata=Hald[1,],
se.fit=TRUE,
estimator="HPM")
confint(hald.hpm)
#> 2.5% 97.5% pred
#> [1,] 70.15171 92.18902 81.17036
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"
variable.names(hald.hpm)
#> [1] "Intercept" "X1" "X2"
hald.mpm = predict(hald.gprior, newdata=Hald[1,],
se.fit=TRUE,
estimator="MPM")
confint(hald.mpm)
#> 2.5% 97.5% pred
#> [1,] 67.79843 91.50459 79.65151
#> attr(,"Probability")
#> [1] 0.95
#> attr(,"class")
#> [1] "confint.bas"
variable.names(hald.mpm)
#> [1] "Intercept" "X1" "X2" "X4"