graph.fglm2d: Graphical functional GLM for images

Description

Non-parametric graphical tests of significance in functional general linear model (GLM) for images

Usage

graph.fglm2d(nsim, formula.full, formula.reduced, image_sets,
  factors = NULL, ...)

Arguments

nsim

The number of random permutations.

formula.full

The formula specifying the general linear model, see formula in lm.

formula.reduced

The formula of the reduced model with nuisance factors only.

image_sets

A named list of sets of images giving the dependent variable (Y), and possibly additionally all the factors. The dimensions of the elements should match with each other, i.e. the factor values should be given for each argument value and each function.

factors

A data frame of factors. An alternative way to specify factors when they are constant for all argument values. The number of rows of the data frame should be equal to the number of curves. Each column should specify the values of a factor.

...

Additional parameters to be passed to graph.fglm.

Value

A global_envelope2d or combined_global_envelope2d object, which can be printed and plotted directly.

References

Mrkvi<U+010D>ka, T., Roskovec, T. and Rost, M. (2019) A nonparametric graphical tests of significance in functional GLM. arXiv:1902.04926 [stat.ME]

Freedman, D., & Lane, D. (1983) A nonstochastic interpretation of reported significance levels. Journal of Business & Economic Statistics, 1(4), 292-298. doi:10.2307/1391660

Examples

Run this code

# NOT RUN {
data("imageset2")
# Testing discrete factor group
res.g <- graph.fglm2d(nsim = 19, # Increase nsim for serious analysis!
                      formula.full = Y ~ group + z,
                      formula.reduced = Y ~ z,
                      image_sets = list(Y = imageset2$image_set),
                      factors = data.frame(group = imageset2$Group,
                                           z = imageset2$z))
plot(res.g)
# Testing discrete factor group with contrasts
res.gc <- graph.fglm2d(nsim = 19, # Increase nsim for serious analysis!
                       formula.full = Y ~ group + z,
                       formula.reduced = Y ~ z,
                       image_sets = list(Y = imageset2$image_set),
                       factors = data.frame(group = imageset2$Group,
                                            z = imageset2$z),
                       summaryfun = "contrasts")
plot(res.gc)

# Testing continuous factor z
res.z <- graph.fglm2d(nsim = 19, # Increase nsim for serious analysis!
                      formula.full = Y ~ group + z,
                      formula.reduced = Y ~ group,
                      image_sets = list(Y = imageset2$image_set),
                      factors = data.frame(group = imageset2$Group,
                                           z = imageset2$z))
plot(res.z)
# }