sx: Construct BayesX Model Terms in A Formula
In datacamp/R2BayesX: Estimate Structured Additive Regression Models with 'BayesX'

Description Usage Arguments Details Value Note Author(s) See Also Examples

Function sx is a model term constructor function for terms used within the formula argument of function bayesx. The function does not evaluate matrices etc., the behavior is similar to function s from package mgcv. It purely exists to build a basic setup for the model term which can be processed by function bayesx.construct.

1	sx(x, z = NULL, bs = "ps", by = NA, ...)

`x`	the covariate the model term is a function of.
`z`	a second covariate.
`bs`	a `character` string, specifying the basis/type which is used for this model term.
`by`	a `numeric` or `factor` variable of the same dimension as each covariate. In the `numeric` vector case the elements multiply the smooth, evaluated at the corresponding covariate values (a ‘varying coefficient model’ results). In the `factor` case the term is replicated for each `factor` level. Note that centering of the term may be needed, please see the notes.
`...`	special controlling arguments or objects used for the model term, see also the examples and function `bayesx.term.options` for all possible optional parameters.

The following term types may be specified using argument bs:

"rw1", "rw2": Zero degree P-splines: Defines a zero degree P-spline with first or second order difference penalty. A zero degree P-spline typically estimates for every distinct covariate value in the dataset a separate parameter. Usually there is no reason to prefer zero degree P-splines over higher order P-splines. An exception are ordinal covariates or continuous covariates with only a small number of different values. For ordinal covariates higher order P-splines are not meaningful while zero degree P-splines might be an alternative to modeling nonlinear relationships via a dummy approach with completely unrestricted regression parameters.
"season": Seasonal effect of a time scale.
"ps", "psplinerw1", "psplinerw2": P-spline with first or second order difference penalty.
"te", "pspline2dimrw1": Defines a two-dimensional P-spline based on the tensor product of one-dimensional P-splines with a two-dimensional first order random walk penalty for the parameters of the spline.
"kr", "kriging": Kriging with stationary Gaussian random fields.
"gk", "geokriging": Geokriging with stationary Gaussian random fields: Estimation is based on the centroids of a map object provided in boundary format (see function read.bnd and shp2bnd) as an additional argument named map within function sx, or supplied within argument xt when using function s, e.g., xt = list(map = MapBnd).
"gs", "geospline": Geosplines based on two-dimensional P-splines with a two-dimensional first order random walk penalty for the parameters of the spline. Estimation is based on the coordinates of the centroids of the regions of a map object provided in boundary format (see function read.bnd and shp2bnd) as an additional argument named map (see above).
"mrf", "spatial": Markov random fields: Defines a Markov random field prior for a spatial covariate, where geographical information is provided by a map object in boundary or graph file format (see function read.bnd, read.gra and shp2bnd), as an additional argument named map (see above).
"bl", "baseline": Nonlinear baseline effect in hazard regression or multi-state models: Defines a P-spline with second order random walk penalty for the parameters of the spline for the log-baseline effect log(λ(time)).
"factor": Special BayesX specifier for factors, especially meaningful if method = "STEP", since the factor term is then treated as a full term, which is either included or removed from the model.
"ridge", "lasso", "nigmix": Shrinkage of fixed effects: defines a shrinkage-prior for the corresponding parameters γ_j, j = 1, …, q, q ≥q 1 of the linear effects x_1, …, x_q. There are three priors possible: ridge-, lasso- and Normal Mixture of inverse Gamma prior.
"re": Gaussian i.i.d. Random effects of a unit or cluster identification covariate.

A list of class "xx.smooth.spec", where "xx" is a basis/type identifying code given by the bs argument of f.

Some care has to be taken with the identifiability of varying coefficients terms. The standard in BayesX is to center nonlinear main effects terms around zero whereas varying coefficient terms are not centered. This makes sense since main effects nonlinear terms are not identifiable and varying coefficients terms are usually identifiable. However, there are situations where a varying coefficients term is not identifiable. Then the term must be centered. Since centering is not automatically accomplished it has to be enforced by the user by adding option center = TRUE in function f. To give an example, the varying coefficient terms in η = … + g_1(z_1)z + g_2(z_2)z + γ_0 + γ_1 z + … are not identified, whereas in η = … + g_1(z_1)z + γ_0 + …, the varying coefficient term is identifiable. In the first case, centering is necessary, in the second case, it is not.

Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.

bayesx, bayesx.term.options, s, bayesx.construct.

## funktion sx() returns a list
## which is then processed by function
## bayesx.construct to build the
## BayesX model term structure
sx(x)

bayesx.construct(sx(x))
bayesx.construct(sx(x, bs = "rw1"))
bayesx.construct(sx(x, bs = "factor"))
bayesx.construct(sx(x, bs = "offset"))
bayesx.construct(sx(x, z, bs = "te"))

## varying coefficients
bayesx.construct(sx(x1, by = x2))
bayesx.construct(sx(x1, by = x2, center = TRUE))

## using a map for markov random fields
data("FantasyBnd")
plot(FantasyBnd)
bayesx.construct(sx(id, bs = "mrf", map = FantasyBnd))

## random effects
bayesx.construct(sx(id, bs = "re"))

## examples using optional controlling
## parameters and objects
bayesx.construct(sx(x, bs = "ps", knots = 20))
bayesx.construct(sx(x, bs = "ps", nrknots = 20))
bayesx.construct(sx(x, bs = "ps", knots = 20, nocenter = TRUE))

## use of bs with original 
## BayesX syntax 
bayesx.construct(sx(x, bs = "psplinerw1"))
bayesx.construct(sx(x, bs = "psplinerw2"))
bayesx.construct(sx(x, z, bs = "pspline2dimrw2"))

bayesx.construct(sx(id, bs = "spatial", map = FantasyBnd))
bayesx.construct(sx(x, z, bs = "kriging"))
bayesx.construct(sx(id, bs = "geospline", map = FantasyBnd, nrknots = 5))
bayesx.construct(sx(x, bs = "catspecific"))


## Not run: 
## generate some data
set.seed(111)
n <- 200

## regressor
dat <- data.frame(x = runif(n, -3, 3))

## response
dat$y <- with(dat, 1.5 + sin(x) + rnorm(n, sd = 0.6))

## estimate models with
## bayesx REML and MCMC
b1 <- bayesx(y ~ sx(x), method = "REML", data = dat)

## increase inner knots
## decrease degree of the P-spline
b2 <- bayesx(y ~ sx(x, knots = 30, degree = 2), method = "REML", data = dat)


## compare reported output
summary(c(b1, b2))

## plot the effect for both models
plot(c(b1, b2), residuals = TRUE)


## more examples
set.seed(111)
n <- 500

## regressors
dat <- data.frame(x = runif(n, -3, 3), z = runif(n, -3, 3),
  w = runif(n, 0, 6), fac = factor(rep(1:10, n/10)))

## response
dat$y <- with(dat, 1.5 + sin(x) + cos(z) * sin(w) +
  c(2.67, 5, 6, 3, 4, 2, 6, 7, 9, 7.5)[fac] + rnorm(n, sd = 0.6))

## estimate model
b <- bayesx(y ~ sx(x) + sx(z, w, bs = "te") + fac,
  data = dat, method = "MCMC")

summary(b)
plot(b)


## now a mrf example
## note: the regional identification
## covariate and the map regionnames
## should be coded as integer
set.seed(333)
     
## simulate some geographical data
data("MunichBnd")
N <- length(MunichBnd); n <- N*5
names(MunichBnd) <- 1:N
     
## regressors
dat <- data.frame(x1 = runif(n, -3, 3),
  id = as.factor(rep(names(MunichBnd), length.out = n)))
dat$sp <- with(dat, sort(runif(N, -2, 2), decreasing = TRUE)[id])
     
## response
dat$y <- with(dat, 1.5 + sin(x1) + sp + rnorm(n, sd = 1.2))

## estimate models with
## bayesx MCMC and REML
b <- bayesx(y ~ sx(x1) + sx(id, bs = "mrf", map = MunichBnd), 
  method = "REML", data = dat)

## summary statistics
summary(b)

## plot the effects
op <- par(no.readonly = TRUE)
par(mfrow = c(1,2))
plot(b, term = "sx(id)", map = MunichBnd, 
  main = "bayesx() estimate")
plotmap(MunichBnd, x = dat$sp, id = dat$id, 
  main = "Truth")
par(op)


## model with random effects
set.seed(333)
N <- 30
n <- N*10

## regressors
dat <- data.frame(id = sort(rep(1:N, n/N)), x1 = runif(n, -3, 3))
dat$re <- with(dat, rnorm(N, sd = 0.6)[id])

## response
dat$y <- with(dat, 1.5 + sin(x1) + re + rnorm(n, sd = 0.6))

## estimate model
b <- bayesx(y ~ sx(x1, bs = "psplinerw1") + sx(id, bs = "re"), data = dat)
summary(b)
plot(b)

## extract estimated random effects
## and compare with true effects
plot(fitted(b, term = "sx(id)")$Mean ~ unique(dat$re))

## End(Not run)