lp_matrix | R Documentation |
lp_matrix()
is a wrapper to predict(..., type = "lpmatrix")
for returning
the linear predictor matrix for the model training data (when data = NULL
),
or user-specified data values supplied via data
.
lp_matrix(model, ...)
## S3 method for class 'gam'
lp_matrix(model, data = NULL, ...)
model |
a fitted model |
... |
arguments passed to other methods and |
data |
a data frame of values at which to return the linear prediction matrix. |
The linear prediction matrix \mathbf{X}_p
is a matrix that maps values
of parameters \hat{\mathbf{\beta}}_p
to values on the linear
predictor of the model \hat{\eta}_p = \mathbf{X}_p
\hat{\mathbf{\beta}}_p
. \mathbf{X}_p
is the model matrix where spline
covariates have been replaced by the values of the basis functions evaluated
at the respective covariates. Parametric covariates are also included.
The linear prediction matrix is returned as a matrix. The object
returned is of class "lp_matrix"
, which inherits from classes "matrix"
and "array"
. The special class allows the printing of the matrix to be
controlled, which we do by printing the matrix as a tibble.
load_mgcv()
df <- data_sim("eg1", seed = 1)
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df)
# linear prediction matrix for observed data
xp <- lp_matrix(m)
## IGNORE_RDIFF_BEGIN
xp
## IGNORE_RDIFF_END
# the object `xp` *is* a matrix
class(xp)
# but we print like a tibble to avoid spamming the R console
# linear predictor matrix for new data set
ds <- data_slice(m, x2 = evenly(x2))
xp <- lp_matrix(m, data = ds)
## IGNORE_RDIFF_BEGIN
xp
## IGNORE_RDIFF_END
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