This methods applies column-wise on the coe of any
Coe object but relies on a function that can be used on any matrix. It
simply uses rnorm with the mean and sd calculated for every column (or row).
For a Coe object, on every colum, randomly generates coefficients values
centered on the mean of the column, and with a sd equals to it standard deviates
multiplied by rate.
Usage
breed(x, ...)
# S3 method for default
breed(x, fac, margin = 2, size, rate = 1, ...)
# S3 method for Coe
breed(x, fac, size, rate = 1, ...)Arguments
- x
the object to permute
- ...
useless here
- fac
a column, a formula or a column id from
$fac- margin
numeric whether 1 or 2 (rows or columns)
- size
numeric the required size for the final object, same size by default
- rate
numeric the number of sd for rnorm, 1 by default.
See also
Other farming:
perm()
Examples
m <- matrix(1:12, nrow=3)
breed(m, margin=2, size=4)
#> [,1] [,2] [,3] [,4]
#> [1,] 2.670620 4.597120 8.401255 9.157338
#> [2,] 1.150985 5.719108 9.356390 10.720259
#> [3,] 3.066805 4.819941 8.019227 9.469230
#> [4,] 1.992539 6.046191 7.530582 13.546154
breed(m, margin=1, size=10)
#> [,1] [,2] [,3]
#> [1,] 1.30970018 1.769321 -1.5597512
#> [2,] -0.01744358 6.486750 0.5512378
#> [3,] 7.13301779 9.320377 7.0219317
#> [4,] 8.52622563 4.558342 10.8915399
#> [5,] 2.01645375 7.194958 8.0983293
#> [6,] 3.54852287 6.733022 14.7962926
#> [7,] 4.57290872 6.963033 4.4282392
#> [8,] 10.02364430 6.407625 10.4446975
#> [9,] 2.40882230 3.826442 11.5882377
#> [10,] 10.19002487 4.197451 3.2041191
bot.f <- efourier(bot, 12)
#> 'norm=TRUE' is used and this may be troublesome. See ?efourier #Details
bot.m <- breed(bot.f, size=80)
bot.m %>% PCA %>% plot
#> will be deprecated soon, see ?plot_PCA
# breed fac wise
# bot.f %>% breed(~type, size=50) %>% PCA %>% plot(~type)