npoly.Rd
Calculates natural polynomial coefficients using stats::lm
npoly( x, degree, raw, drop_coo, from_coo = coo, to_coe = { { coe } }, ... ) npoly_i(x, nb_pts, from_coe, ...)
x | |
---|---|
degree | polynomial degree for the fit ( |
raw |
|
drop_coo |
|
from_coo, to_coe | column names |
... | for generics. Useless here. |
nb_pts | number of points to sample and on which to calculate polynomials |
from_coe | column name for inverse method |
a list with components when applied on a single shape:
coeff
the coefficients (including the intercept)
ortho
whether orthogonal or natural polynomials were fitted
degree
degree of the fit (could be retrieved through coeff
though)
baseline1
the first baseline point (so far the first point)
baseline2
the second baseline point (so far the last point)
r2
the r2 from the fit
mod
the raw lm model
Curves must be registered on a bookstein baseline, with the first coordinates on (-0.5, 0) and the last on (0.5, 0). Use coo_bookstein
A polynomial of degree n
use this fit:
\(y = a_{0} + a_{1}x^1 + a_{...}x^{...} + a_{n}x^{n}\)
And thus returns n+1
coefficients. The +1
being $a_0$ ie the intercept.
In retired Momocs, baseline was free and not necessarily Bookstein. Not sure this was really helpful but I'm sure this overcomplicated coding. If your shapes are not registered on Bookstein coordinates, you will be messaged (not for coo_single though)
npoly_i
: inverse npoly method
op <- o %>% npoly(degree=3) op#> # A tibble: 1 x 4 #> a0 a1 a2 a3 #> <dbl> <dbl> <dbl> <dbl> #> 1 0.339 0.0264 -1.16 0.0253 #> ❯coe_single with 4 coefficientsop %>% class#> [1] "npoly_single" "coe_single" "tbl_df" "tbl" "data.frame"#> ! npoly: 'degree' not provided and set to 5#> # A tibble: 3 x 7 #> coo var domes view ind coe coe_i #> <list<coo_single[> <fct> <fct> <fct> <fct> <list<coe_single> <list<coo_single> #> 1 <tibble [99 × 2]> Aglan cult VD O10 <tibble [1 × 6]> <tibble [120 × 2… #> 2 <tibble [96 × 2]> Aglan cult VL O10 <tibble [1 × 6]> <tibble [120 × 2… #> 3 <tibble [95 × 2]> Aglan cult VD O11 <tibble [1 × 6]> <tibble [120 × 2… #> ❯mom_tbl