ILS (version 0.3)

mandel.fqcs: This function is used to compute the FDA Mandel's h and k statistic

Description

It develops an object of 'mandel.fqcs' class to perform statistical quality control analysis. This function is used to compute the functional approach of Mandel's h and k statistic. It is specifically designed to deal with experimental data results defined by curves such as thermograms and spectra.

Usage

mandel.fqcs(x, ...)
# S3 method for default
mandel.fqcs(
  x,
  p = NULL,
  index.laboratory = NULL,
  argvals = NULL,
  rangeval = NULL,
  names = NULL,
  ...
)
# S3 method for ils.fqcdata
mandel.fqcs(
  x,
  fdep = depth.mode,
  outlier = TRUE,
  trim = 0.01,
  alpha = 0.01,
  nb = 200,
  smo = 0.05,
  ...
)

Arguments

x

A (n x m) matrix or data-frame. The m is the number of points observed in each curve, and n is the number of curves for each laboratory.

...

Other arguments passed to or from other methods.

p

The number of laboratories.

index.laboratory

The laboratory index. The index laboratory length should be equal a p.

argvals

Argvals, by default: 1:m.

rangeval

The range of discretization points, by default: range(argvals).

names

Optional. A list with tree components: main an overall title, xlab title for x axis and ylab title for y axis.

fdep

Type of depth measure, by default depth.mode.

outlier

= TRUE

trim

The alpha of the trimming.

alpha

Significance level, by defaul 1%.

nb

The number of bootstrap samples.

smo

The smoothing parameter for the bootstrap samples.

References

Febrero-Bande, M. and Oviedo, M. (2012), "Statistical computing in functional data analysis: the R package fda.usc". Journal of Statistical Software 51 (4), 1-28.

Cuevas A., Febrero-Bande, M. and Fraiman, R. (2006), "On the use of the bootstrap for estimating functions with functional data". Computational Statistics & Data Analysis 51, 2, 1063-1074.

Naya, S., Tarrio-Saavedra. J., Lopez- Beceiro, J., Francisco Fernandez, M., Flores, M. and Artiaga, R. (2014), "Statistical functional approach for interlaboratory studies with thermal data". Journal of Thermal Analysis and Calorimetry, 118,1229-1243.

Examples

if (FALSE) {
library(ILS)
data(TG)
delta <- seq(from = 40 ,to = 850 ,length.out = 1000 )
fqcdata <- ils.fqcdata(TG, p = 7, argvals = delta)
mandel.tg <- mandel.fqcs(fqcdata.tg,nb = 200)
plot(mandel.tg,legend = F,col=c(rep(3,5),1,1))
  }