Make Model Matrix from Data Frame
Converts a data frame with numeric and factor contents into a matrix,
suitable for use with bart. Unlike in linear regression,
factors containing more than two levels result in dummy variables being
created for each level.
makeModelMatrixFromDataFrame(x, drop = TRUE) makeind(x, all = TRUE) makeTestModelMatrix(data, newdata)
x |
Data frame of explanatory variables. |
drop |
Logical or list controling whether or not columns that are constants or factor
levels with no instances are omitted from the result. When a list, must be of
length equal to
|
all |
Not currently implemented. |
data |
An existing |
newdata |
Test data frame. |
Character vectors are included as factors. If you have numeric data coded as characters,
convert it using as.numeric first.
Note that if you have train and test data frames, it may be best
to rbind the two together, apply makeModelMatrixFromDataFrame
to the result, and then pull them back apart. Alternatively, save the drop
attribute used in creating the train data and use it when creating a matrix
from the test data. Example given below.
Use of these functions is not required when using bart, bart2, or
dbartsSampler; they exist to allow the user finer control and to assist
with writing packages that separate the creation of training from test data.
A matrix with columns corresponding to the elements of the data frame. If drop = TRUE
or is a list, the attribute drop on the result is set to the list used when creating
the matrix.
Vincent Dorie: vdorie@gmail.com.
iv <- 1:10
rv <- runif(10)
f <- factor(rep(seq.int(3), c(4L, 4L, 2L)),
labels = c("alice", "bob", "charlie"))
df <- data.frame(iv, rv, f)
mm <- makeModelMatrixFromDataFrame(df)
## create test and train matrices with disjoint factor levels
train.df <- df[1:8,]
test.df <- df[9:10,]
train.mm <- makeModelMatrixFromDataFrame(train.df)
test.mm <- makeModelMatrixFromDataFrame(test.df, attr(train.mm, "drop"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.