Code
predict_ionize(tb_0, file.nm, .plot = FALSE)
Output
# A tibble: 23,400 x 11
file.nm species.nm t.nm sample.nm bl.nm Xt.pr N.pr Xt.nlt M_Xt.nlt Xt.l0
<chr> <chr> <dbl> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2018-01~ 12C 0.54 Belemnit~ 1 34460. 12040 35176. 30311. 29595.
2 2018-01~ 12C 1.08 Belemnit~ 1 34202. 11950 35176. 30311. 29337.
3 2018-01~ 12C 1.62 Belemnit~ 1 34632. 12100 35177. 30311. 29766.
4 2018-01~ 12C 2.16 Belemnit~ 1 34191. 11946 35177. 30311. 29325.
5 2018-01~ 12C 2.7 Belemnit~ 1 34855. 12178 35178. 30311. 29988.
6 2018-01~ 12C 3.24 Belemnit~ 1 34672. 12114 35179. 30311. 29804.
7 2018-01~ 12C 3.78 Belemnit~ 1 34766. 12147 35179. 30311. 29898.
8 2018-01~ 12C 4.32 Belemnit~ 1 34609. 12092 35180. 30311. 29740.
9 2018-01~ 12C 4.86 Belemnit~ 1 34414. 12024 35180. 30311. 29545.
10 2018-01~ 12C 5.4 Belemnit~ 1 34474. 12045 35181. 30311. 29605.
# ... with 23,390 more rows, and 1 more variable: N.l0 <dbl>
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