predict.lazy {lazy} | R Documentation |
Obtains predictions from a lazy learning object
## S3 method for class 'lazy': predict(object, newdata=NULL, t.out=FALSE, k.out=FALSE, S.out=FALSE, T.out=FALSE, I.out=FALSE, ...)
object |
Object of class inheriting from lazy . |
newdata |
Data frame (or matrix, vector, etc...) defining of the query points for which a prediction is to be produced. |
t.out |
Logical switch indicating if the function should return the parameters of the local models used to perform each estimation. |
k.out |
Logical switch indicating if the function should return the number of neighbors used to perform each estimation. |
S.out |
Logical switch indicating if the function should return the estimated variance of the prediction suggested by all the models identified for each query point. |
T.out |
Logical switch indicating if the function should return the parameters of all the models identified for each query point. |
I.out |
Logical switch indicating if the function should return
the index i of all the samples (X[i,],Y[i]) used to
perform each estimation. |
... |
Arguments passed to or from other methods. |
The output of the method is a list containing the following components:
h |
Vector of q elements, where q is the number of
rows in newdata , i.e. the number of query points. The element
in position i is the estimate of the value of the unknown function
in the query point newdata[i,] . The component h is
always returned. |
t |
Matrix of z*q elements, where z=z2 i.e., number of
parameters of a quadratic model if at least one model of degree 2
was identified (see quaIdPar in lazy.control ),
otherwise z=z1 i.e.,
number of parameters of a linear model if at least one model of
degree 1 was identified (see linIdPar in
lazy.control ), or z=1 if only
models of degree 0 where considered. In the general case,
the elements of the vector t[,j]=c(a0, a1,..., an, a11,
a12,..., a22, a23,..., a33, a34,..., ann) are
the parameters of the local model used for estimating
the function in the j th query point: the cross-terms terms
a11,a12,...,ann wil be missing if no quadratic model is
identified and the terms a1,...,an , will be missing if
no linear model is identified. If, according to cmbPar (see
lazy.control ), estimations are to be performed by a
combination of models, the elements of t[,j] are a weighted
average of the parameters
of the selected models where the weight of each model is the
inverse of the a leave-one-out estimate of the variances of the
model itself. REMARK: a translation of the axes is considered
which centers all the local models in the respective query point. |
k |
Vector of q elements. Selected number of neighbors
for each query point. If, according to cmbPar (see
lazy.control ), a local
combination of models is considered, k[j] is the largest
value among the number of neighbors used by the selected models
for estimating the value in the j th query point. |
S |
List of up to 3 components: Each component is a matrix
containing an estimate, obtained through a leave-one-out
cross-valication, of the variance of local models.
S is returned only if S.out=TRUE in
the function call. |
T |
List of up to 3 components:
T is returned only if
T.out=TRUE in the function call. |
I |
Matrix of idM*q elements, where idM is the
largest of idM0 , idM1 , and idM2 . Contains the
index of the neighbors of each query point in newdata .
In particular, I[i,j] is the i th nearest neighbor of
the q th query point. |
Mauro Birattari and Gianluca Bontempi
data(cars) cars.lazy <- lazy(dist ~ speed, cars) predict(cars.lazy, data.frame(speed = seq(5, 30, 1)))