GPA.test: Significance test for GPA

Description

Performs a permutation significance test based on total variance explained for Generalized Procrustes Analysis. The function uses GPA.

Usage

GPA.test(df, group, tolerance = 10^-10, nbiteration = 200, scale = TRUE,
  nperm = 999, progress = TRUE)

Arguments

a data frame with n rows (individuals) and p columns (quantitative varaibles), in which all data frames are combined.

group

a vector indicating the number of variables in each group (i.e. data frame).

tolerance

a threshold with respect to which the algorithm stops, i.e. when the difference between the GPA loss function at step n and n+1 is less than tolerance.

nbiteration

the maximum number of iterations until the algorithm stops.

scale

logical, if TRUE (default) scaling is required.

nperm

number of permutations.

progress

logical indicating if the progress bar should be displayed.

Value

method

a character string indicating the name of the test.

data.name

a character string giving the name(s) of the data, plus additional information.

statistic

the value of the test statistics.

permutations

the number of permutations.

p.value

the p-value of the test.

Details

Rows of each data frame are randomly and independently permuted.

The function deals with the limitted floating point precision, which can bias calculation of p-values based on a discrete test statistic distribution.

References

Wakeling IN, Raats MM and MacFie HJH (1992) A new significance test for consensus in Generalized Procrustes Analysis. Journal of Sensory Studies 7:91-96.

Examples

Run this code

# NOT RUN {
require(FactoMineR)
data(wine)

# }
# NOT RUN {
GPA.test(wine[,-(1:2)],group=c(5,3,10,9,2))
# }

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