dad (version 4.1.6)

print.fpcat: Printing results of a functional PCA of probability densities among time

Description

Applies to an object of class "fpcat". Prints the numeric results returned by the fpcat function.

Usage

# S3 method for fpcat
print(x, mean.print = FALSE, var.print = FALSE,
  cor.print = FALSE, skewness.print = FALSE, kurtosis.print = FALSE,
  digits = 2, ...)

Arguments

x

object of class "fpcat", returned by the fpcat function.

mean.print

logical. If TRUE, prints for each observation time the means and standard deviations of the variables and the norm of the density.

var.print

logical. If TRUE, prints for each observation time the variances and covariances of the variables.

cor.print

logical. If TRUE, prints for each observation time the correlations between the variables.

skewness.print

logical. If TRUE, prints for each observation time the skewness coefficients of the variables.

kurtosis.print

logical. If TRUE, prints for each observation time the kurtosis coefficients of the variables.

digits

numeric. Number of significant digits for the display of numeric results.

...

optional arguments to print methods.

Author

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard

Details

By default, are printed the vector of observation times (numeric, ordered factor or object of class "Date"), the inertia explained by the nb.values (see fpcat) first principal components, the contributions, the qualities of representation of the densities along the nb.factors (see fpcat) first principal components, and the principal scores.

References

Boumaza, R., Yousfi, S., Demotes-Mainard, S. (2015). Interpreting the principal component analysis of multivariate density functions. Communications in Statistics - Theory and Methods, 44 (16), 3321-3339.

See Also

fpcat; plot.fpcat; print.

Examples

times <- as.Date(c("2017-03-01", "2017-04-01", "2017-05-01", "2017-06-01"))
x1 <- data.frame(z1=rnorm(6,1,5), z2=rnorm(6,3,3))
x2 <- data.frame(z1=rnorm(6,4,6), z2=rnorm(6,5,2))
x3 <- data.frame(z1=rnorm(6,7,2), z2=rnorm(6,8,4))
x4 <- data.frame(z1=rnorm(6,9,3), z2=rnorm(6,10,2))
ft <- foldert(x1, x2, x3, x4, times = times, rows.select="intersect")
print(ft)
result <- fpcat(ft)

print(result)
print(result, mean.print = TRUE, var.print = TRUE)