Global2 | R Documentation |
The function implements the Global Testing procedure for testing mean differences between two functional populations. Functional data are tested locally and unadjusted and adjusted p-value functions are provided. The unadjusted p-value function controls the point-wise error rate. The adjusted p-value function controls the interval-wise error rate.
Global2(
data1,
data2,
mu = 0,
B = 1000L,
paired = FALSE,
dx = NULL,
stat = "Integral"
)
data1 |
First population's data. Either pointwise evaluations of the
functional data set on a uniform grid, or a |
data2 |
Second population's data. Either pointwise evaluations of the
functional data set on a uniform grid, or a |
mu |
Functional mean difference under the null hypothesis. Three
possibilities are available for |
B |
The number of iterations of the MC algorithm to evaluate the
p-values of the permutation tests. The defualt is |
paired |
A logical indicating whether a paired test has to be performed.
Default is |
dx |
Used only if a |
stat |
Test statistic used for the global test. Possible values are:
|
An object of class fdatest2
, containing the following components:
test
: String vector indicating the type of test performed. In this case
equal to "2pop"
.
mu
: Evaluation on a grid of the functional mean difference under the
null hypothesis (as entered by the user).
unadjusted_pval
: Evaluation on a grid of the unadjusted p-value
function (it is a constant function according to the global testing
procedure).
adjusted_pval
: Evaluation on a grid of the adjusted p-value function.
data.eval
: Evaluation on a grid of the functional data.
ord_labels
: Vector of labels indicating the group membership of
data.eval
.
A. Pini and S. Vantini (2017). The Interval Testing Procedure: Inference for Functional Data Controlling the Family Wise Error Rate on Intervals. Biometrics 73(3): 835–845.
Pini, A., & Vantini, S. (2017). Interval-wise testing for functional data. Journal of Nonparametric Statistics, 29(2), 407-424
See also IWT2
for local inference. See
plot.fdatest2
for plotting the results.
# Importing the NASA temperatures data set
data(NASAtemp)
# Performing the Global for two populations
Global.result <- Global2(NASAtemp$paris, NASAtemp$milan)
# Plotting the results of the Global
plot(
Global.result,
xrange = c(0, 12),
main = 'Global results for testing mean differences'
)
# Selecting the significant components at 5% level
which(Global.result$adjusted_pval < 0.05)
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