mandel.fqcs | R Documentation |
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.
mandel.fqcs(x, ...) ## Default S3 method: mandel.fqcs( x, p = NULL, index.laboratory = NULL, argvals = NULL, rangeval = NULL, names = NULL, ... ) ## S3 method for class 'ils.fqcdata' mandel.fqcs( x, fdep = depth.mode, outlier = TRUE, trim = 0.01, alpha = 0.01, nb = 200, smo = 0.05, ... )
x |
A |
... |
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 |
argvals |
Argvals, by default: |
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. |
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.
## Not run: 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)) ## End(Not run)
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