This is an internal function, which constructs a model matrix for new covariate data, based on the formula and scaling info in an existing BayesMfp object. The matrix can then be passed to prediction functions.

1 | ```
constructNewdataMatrix(BayesMfpObject, newdata)
``` |

`BayesMfpObject` |
a valid |

`newdata` |
the new covariate data as a data.frame (with the same
covariate names as in the call to |

The (uncentered!) model matrix with the FP columns shifted and scaled as for the original data.

Daniel Saban\'es Bov\'e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## construct a BayesMfp object
set.seed(19)
x1 <- rnorm (n=15)
x2 <- rbinom (n=15, size=20, prob=0.5)
x3 <- rexp (n=15)
y <- rt (n=15, df=2)
test <- BayesMfp (y ~ bfp (x2, max = 4) + uc (x1 + x3), nModels = 200, method="exhaustive")
## some new covariate data
newdata <- data.frame(x1 = 1:10, x2 = rbinom(n=10, size=20, prob=0.6),
x3 = 2:11)
## construct the new design matrix:
newmatrix <- bfp:::constructNewdataMatrix(BayesMfpObject=test, newdata=newdata)
## check the result:
stopifnot(identical(newmatrix,
structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.6,
1.2, 1.2, 1.2, 1.3, 1.3, 1.4, 1.1,
1.2, 1.1, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11),
.Dim = c(10L, 4L),
.Dimnames =
list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10"),
c("(Intercept)", "x2", "x1", "x3")),
assign = 0:3)))
``` |

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