GET (version 0.1-3)

frank.fglm2d: F rank functional GLM for images

Description

Multiple testing in permutation inference for the general linear model (GLM)

Usage

frank.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 frank.fglm.

Value

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

References

Mrkvi<U+010D>ka, T., Myllym<U+00E4>ki, M. and Narisetty, N. N. (2019) New methods for multiple testing in permutation inference for the general linear model. arXiv:1906.09004 [stat.ME]

See Also

frank.fglm, graph.fglm2d

Examples

# NOT RUN {
data("imageset2")
# Testing discrete factor group
res.g <- frank.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 continuous factor z
res.z <- frank.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)
# }