spl1D function

Fit P-splines

Fit multi dimensional P-splines using sparse implementation.

spl1D(
  x,
  nseg,
  pord = 2,
  degree = 3,
  scaleX = TRUE,
  xlim = range(x),
  cond = NULL,
  level = NULL
)

spl2D(
  x1,
  x2,
  nseg,
  pord = 2,
  degree = 3,
  scaleX = TRUE,
  x1lim = range(x1),
  x2lim = range(x2),
  cond = NULL,
  level = NULL
)

spl3D(
  x1,
  x2,
  x3,
  nseg,
  pord = 2,
  degree = 3,
  scaleX = TRUE,
  x1lim = range(x1),
  x2lim = range(x2),
  x3lim = range(x3)
)

Arguments

• x, x1, x2, x3: The variables in the data containing the values of the x covariates.
• nseg: The number of segments
• pord: The order of penalty, default pord = 2
• degree: The degree of B-spline basis, default degree = 3
• scaleX: Should the fixed effects be scaled.
• xlim, x1lim, x2lim, x3lim: A numerical vector of length 2 containing the domain of the corresponding x covariate where the knots should be placed. Default set to NULL, when the covariate range will be used.
• cond: Conditional factor: splines are defined conditional on the level. Default NULL.
• level: The level of the conditional factor. Default NULL.

Returns

A list with the following elements:

• X - design matrix for fixed effect. The intercept is not included.
• Z - design matrix for random effect.
• lGinv - a list of precision matrices
• knots - a list of vectors with knot positions
• dim.f - the dimensions of the fixed effect.
• dim.r - the dimensions of the random effect.
• term.labels.f - the labels for the fixed effect terms.
• term.labels.r - the labels for the random effect terms.
• x - a list of vectors for the spline variables.
• pord - the order of the penalty.
• degree - the degree of the B-spline basis.
• scaleX - logical indicating if the fixed effects are scaled.
• EDnom - the nominal effective dimensions.

Functions

• spl2D(): 2-dimensional splines
• spl3D(): 3-dimensional splines

Examples

## Fit model on john.alpha data from agridat package.
data(john.alpha, package = "agridat")

## Fit a model with a 1-dimensional spline at the plot level.
LMM1_spline <- LMMsolve(fixed = yield ~ rep + gen,
                       spline = ~spl1D(x = plot, nseg = 20),
                       data = john.alpha)

summary(LMM1_spline)

## Fit model on US precipitation data from spam package.
data(USprecip, package = "spam")

## Only use observed data
USprecip <- as.data.frame(USprecip)
USprecip <- USprecip[USprecip$infill == 1, ]

## Fit a model with a 2-dimensional P-spline.
LMM2_spline <- LMMsolve(fixed = anomaly ~ 1,
                       spline = ~spl2D(x1 = lon, x2 = lat, nseg = c(41, 41)),
                       data = USprecip)

summary(LMM2_spline)

See Also

LMMsolve