update of documentation

man/mafa.Rd

@@ -4,7 +4,7 @@
 \alias{mafa}
 \title{Min/Max Autocorrelation Factors Analysis (MAFA) in time (or 1D)}
 \usage{
-mafa(x, time = NULL, nreal = 1000, nmafs = 3, seed = NULL, contr_var = 15)
+mafa(x, time = NULL, nreal = 1000, nmafs = 3, seed = NULL, contr_var = NULL)
 }
 \arguments{
 \item{x}{matrix or data frame containing time series of multiple variables.}
@@ -20,16 +20,22 @@ mafa(x, time = NULL, nreal = 1000, nmafs = 3, seed = NULL, contr_var = 15)
 \item{contr_var}{integer; the number of variables with highest contribution to show in the loading plots.}
 }
 \description{
-This is an example showing how to document functions to be included in the package.
-final MAFs were a median profile of the MAFs over all realizations.
+Min/Max Autocorrelation Factors Analysis (MAFA) in time (or 1D)
 }
 \details{
 MAFA is a multivariate statistical method, that allows the set of initial variables to be
 decomposed into factors. The autocorrelation of which decreases, or the variogram increases,
 when going from the first factors to the last ones. Hence the very first factors extract
 the part of the variables which is the most continuous in time.
+
+The function for this code is adopted from Wollez et al. 2009 (\url{https://doi.org/10.1051/alr/2009020})
 }
 \examples{
+df <- data.frame(variable1 = 1:20, variable2= rnorm(20, 100, 30), variable3 = 1:20 + rnorm(20))
+mafa(x = df, time = 1981:2000)
+df <- matrix(rnorm(100), ncol = 5)
+colnames(df) <- letters[1:5]
+mafa(df)
 
 }
 \seealso{