plot.mfboost: Plot effect estimates of mfboost model

In Almond-S/manifoldboost: Boosting Regression Models for Manifold Valued Data

Plot effect estimates of mfboost model

Description

Illustrate estimated effects by comparing predictions with corresponding reference poles.

Usage

## S3 method for class 'mfboost'
plot(x, which = NULL, ids = 1:6, multiplier = 1, ...)

Arguments

x	object of class mfboost for plotting
which	a subset of base-learners used for plotting. If which is given (as an integer vector or characters corresponding to base-learners) predictions only based on the corresponding learners are depicted. Per default all selected base-learners are plotted.
ids	response ids to be plotted.
multiplier	a number multiplied to the predictor before passing it to the response function. Can be used for re-scaling / amplification.
...	other arguments passed to mfGeometry plot method.

Details

Default plot function for mfboost objects. Example predictions are plotted using the plot method of x$family@mf, the geometry mfGeom class of the response.

Examples

# modeling the FORM/SIZE-AND-SHAPE of IRREGULAR curves -------------------

# load irregular cell data
data("cells", package = "manifoldboost")

# subsample (one for each covariate combination)
cellsub <- as.data.frame(cells[-which(names(cells)=="response")])
cellsub$myd <- factor(with(cellsub, 
                           paste0("a=", a, " r=", r, " b=", b, " m=", m)))
subids <- match(unique(cellsub$myd), cellsub$myd)
cellsub <- as.list(cellsub[subids, ])
cellsub$response <- cells$response[as.numeric(cells$response$id) %in% subids, ]

# fit model
cell_model <- mfboost(
  formula = response ~ bbsc(a, df = 3, knots = 5) + 
    bbsc(r, df = 3, knots = 5) + 
    bbsc(b, df = 3, knots = 5) + 
    bbsc(m, df = 3, knots = 5),
  obj.formula = value^dim ~ 
    bbs(arg, df = 1, differences = 0, knots = 5, 
        boundary.knots = c(0,1), cyclic = TRUE) | id, 
  data = cellsub,
  family = PlanarSizeShapeL2(
    weight_fun = trapez_weights,
    arg_range = c(0,1)),
  control = boost_control(mstop = 300)
  )

# # cross-validation
# set.seed(9382)
# cell_cv <- cvrisk(cell_model,
#                           folds = cvLong(
#                             id = cell_model$id,
#                             weights = cell_model$`(weights)`,
#                             type = "kfold"),
#                           grid = 0:mstop(cell_model))
# cell_model[mstop(cell_cv)]

# plot first four predictions
par(mfrow = c(2,2), mar = rep(2, 4) )
plot(cell_model, ids = 1:4, t = "l", 
     main = cellsub$myd[1:4], 
     seg_par = list(lty = "dashed"))
legend(x = "bottomright", lty = c(1,1, 2),
       legend = c("intercept", "prediction", "point correspondence"), 
       col = c("grey", "black", "grey"))

# compare with data
plot(cell_model, ids = 1:4, t = "l", y0_ = cell_model$family@mf$y_[1:4], 
     main = cellsub$myd[1:4], 
     seg_par = list(lty = "dashed"))
legend(x = "bottomright", lty = c(1,1, 2),
       legend = c("observation", "prediction", "point correspondence"), 
       col = c("grey", "black", "grey"))

# predict dense cells on grids
cellgrid <- cellsub
cellgrid$response <- with(cellgrid$response, expand.grid(
  id = unique(id), 
  arg = seq(0,1, len = 100),
  dim = unique(dim),
  value = NA))
cellgrid$response$value <- predict(cell_model, 
                                   newdata = cellgrid, type = "response")


# factorize effects
cell_fac <- factorize(cell_model)

vimp <- varimp(cell_fac$cov)
plot(vimp, auto.key = FALSE)

# plot two most important effect directions
this <- cell_fac$cov$which(head(names(vimp)[order(vimp, decreasing = TRUE)], 2))
par(mfcol = c(2,2))
plot(cell_fac$resp, which = this, y0_par = list(type="l"))
plot(cell_fac$cov, which = this)

Almond-S/manifoldboost documentation built on June 23, 2022, 11:06 a.m.