One–way anova model for functional data

One–way anova model for k independent samples of functional data. The function contrasts the null hypothesis of equality of mean functions of functional data based on the an asymptotic version of the anova F–test. $$H_0:\, m_1=\ldots=m_k$$

Usage

fanova.onefactor(
  object,
  group,
  nboot = 100,
  plot = FALSE,
  verbose = FALSE,
  ...
)

Arguments

object: functional response data. fdata class object with n curves.
group: a factor specifying the class for each curve.
nboot: number of bootstrap samples.
plot: if TRUE, plot the mean of each factor level and the results of test.
verbose: if TRUE, print intermediate results.
...: further arguments passed to or from other methods.

Value

Returns:

p-value: Probability of rejecting the null hypothesis H0 at a significance level.
stat: Statistic value of the test.
wm: Statistic values of bootstrap resamples.

Details

The function returns the p–value of test using one–way anova model over nboot runs.

Note

anova.onefactor deprecated.

References

Cuevas, A., Febrero, M., & Fraiman, R. (2004). An anova test for functional data. Computational statistics & data analysis, 47(1), 111-122.

Author

Juan A. Cuesta-Albertos, Manuel Febrero-Bande, Manuel Oviedo de la Fuente
manuel.oviedo@udc.es

Examples

if (FALSE) { # \dontrun{
data(MCO)
grupo<-MCO$classintact
datos<-MCO$intact
res=fanova.onefactor(datos,grupo,nboot=50,plot=TRUE)
grupo <- MCO$classpermea
datos <- MCO$permea
res=fanova.onefactor(datos,grupo,nboot=50,plot=TRUE)
} # }