tests/testthat/_snaps/diagnostics_IC.md
In MartinSchobben/point: Reading, Processing, and Analysing Raw Ion Count Data
diagnostics wrapper on synthetic data is consistent
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm)
Output
# A tibble: 9 x 7
execution type.nm spot.nm ratio.nm M_R_Xt.pr F_R_Xt.pr p_R_Xt.pr
<dbl> <chr> <int> <chr> <dbl> <dbl> <dbl>
1 1 asymmetric 1 13C/12C 0.0111 63.2 1.28e-39
2 1 asymmetric 2 13C/12C 0.0111 74.9 1.09e-46
3 1 asymmetric 3 13C/12C 0.0111 71.9 6.70e-45
4 1 ideal 1 13C/12C 0.0112 0.267 8.49e- 1
5 1 ideal 2 13C/12C 0.0112 0.219 8.83e- 1
6 1 ideal 3 13C/12C 0.0112 1.73 1.59e- 1
7 1 symmetric 1 13C/12C 0.0110 105. 2.00e-64
8 1 symmetric 2 13C/12C 0.0110 127. 2.81e-77
9 1 symmetric 3 13C/12C 0.0110 122. 1.42e-74
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .method = "CV")
Output
# A tibble: 9 x 3
type.nm spot.nm hyp
<chr> <int> <chr>
1 symmetric 1 H0 (homoskedasticity)
2 symmetric 2 H0 (homoskedasticity)
3 symmetric 3 H0 (homoskedasticity)
4 asymmetric 1 H0 (homoskedasticity)
5 asymmetric 2 H0 (homoskedasticity)
6 asymmetric 3 H0 (homoskedasticity)
7 ideal 1 H0 (homoskedasticity)
8 ideal 2 H0 (homoskedasticity)
9 ideal 3 H0 (homoskedasticity)
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "complete")
Output
# A tibble: 27,000 x 56
execution type.nm spot.nm t.nm trend.nm.13C trend.nm.12C base.nm.13C
<dbl> <chr> <int> <int> <dbl> <dbl> <dbl>
1 1 asymmetric 1 1 120 120 0
2 1 asymmetric 1 2 120 120 0
3 1 asymmetric 1 3 120 120 0
4 1 asymmetric 1 4 120 120 0
5 1 asymmetric 1 5 120 120 0
6 1 asymmetric 1 6 120 120 0
7 1 asymmetric 1 7 120 120 0
8 1 asymmetric 1 8 120 120 0
9 1 asymmetric 1 9 120 120 0
10 1 asymmetric 1 10 120 120 0
# ... with 26,990 more rows, and 49 more variables: base.nm.12C <dbl>,
# force.nm.13C <dbl>, force.nm.12C <dbl>, bl.nm.13C <int>, bl.nm.12C <int>,
# n.rw.13C <dbl>, n.rw.12C <dbl>, N.pr.13C <dbl>, N.pr.12C <dbl>,
# Xt.pr.13C <dbl>, Xt.pr.12C <dbl>, n_t.nm.13C <int>, n_t.nm.12C <int>,
# tot_N.pr.13C <dbl>, tot_N.pr.12C <dbl>, M_Xt.pr.13C <dbl>,
# M_Xt.pr.12C <dbl>, S_Xt.pr.13C <dbl>, S_Xt.pr.12C <dbl>,
# RS_Xt.pr.13C <dbl>, RS_Xt.pr.12C <dbl>, SeM_Xt.pr.13C <dbl>, ...
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "augmented")
Output
# A tibble: 104,980 x 12
execution type.nm trend.nm base.nm force.nm t.nm bl.nm n.rw spot.nm
<dbl> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <int>
1 1 symmetric 120 0 -60 1 1 3000 1
2 1 symmetric 120 0 -60 1 1 3000 1
3 1 symmetric 120 0 -60 1 1 3000 2
4 1 symmetric 120 0 -60 1 1 3000 2
5 1 symmetric 120 0 -60 1 1 3000 3
6 1 symmetric 120 0 -60 1 1 3000 3
7 1 symmetric 120 0 -60 2 1 3000 1
8 1 symmetric 120 0 -60 2 1 3000 1
9 1 symmetric 120 0 -60 2 1 3000 2
10 1 symmetric 120 0 -60 2 1 3000 2
# ... with 104,970 more rows, and 3 more variables: species.nm <chr>,
# N.pr <dbl>, Xt.pr <dbl>
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "diagnostic")
Output
# A tibble: 27,000 x 54
execution type.nm spot.nm t.nm trend.nm.13C trend.nm.12C base.nm.13C
<dbl> <chr> <int> <int> <dbl> <dbl> <dbl>
1 1 symmetric 1 1 120 120 0
2 1 symmetric 1 2 120 120 0
3 1 symmetric 1 3 120 120 0
4 1 symmetric 1 4 120 120 0
5 1 symmetric 1 5 120 120 0
6 1 symmetric 1 6 120 120 0
7 1 symmetric 1 7 120 120 0
8 1 symmetric 1 8 120 120 0
9 1 symmetric 1 9 120 120 0
10 1 symmetric 1 10 120 120 0
# ... with 26,990 more rows, and 47 more variables: base.nm.12C <dbl>,
# force.nm.13C <dbl>, force.nm.12C <dbl>, bl.nm.13C <int>, bl.nm.12C <int>,
# n.rw.13C <dbl>, n.rw.12C <dbl>, N.pr.13C <dbl>, N.pr.12C <dbl>,
# Xt.pr.13C <dbl>, Xt.pr.12C <dbl>, n_t.nm.13C <int>, n_t.nm.12C <int>,
# tot_N.pr.13C <dbl>, tot_N.pr.12C <dbl>, M_Xt.pr.13C <dbl>,
# M_Xt.pr.12C <dbl>, S_Xt.pr.13C <dbl>, S_Xt.pr.12C <dbl>,
# RS_Xt.pr.13C <dbl>, RS_Xt.pr.12C <dbl>, SeM_Xt.pr.13C <dbl>, ...
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "outlier")
Output
# A tibble: 27,000 x 22
execution type.nm spot.nm t.nm trend.nm.13C trend.nm.12C base.nm.13C
<dbl> <chr> <int> <int> <dbl> <dbl> <dbl>
1 1 symmetric 1 1 120 120 0
2 1 symmetric 1 2 120 120 0
3 1 symmetric 1 3 120 120 0
4 1 symmetric 1 4 120 120 0
5 1 symmetric 1 5 120 120 0
6 1 symmetric 1 6 120 120 0
7 1 symmetric 1 7 120 120 0
8 1 symmetric 1 8 120 120 0
9 1 symmetric 1 9 120 120 0
10 1 symmetric 1 10 120 120 0
# ... with 26,990 more rows, and 15 more variables: base.nm.12C <dbl>,
# force.nm.13C <dbl>, force.nm.12C <dbl>, bl.nm.13C <int>, bl.nm.12C <int>,
# n.rw.13C <dbl>, n.rw.12C <dbl>, N.pr.13C <dbl>, N.pr.12C <dbl>,
# Xt.pr.13C <dbl>, Xt.pr.12C <dbl>, hat_S_N.pr.13C <dbl>,
# hat_Xt.pr.13C <dbl>, CooksD <dbl>, flag <fct>
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .label = "latex")
Output
# A tibble: 9 x 7
execution type.nm spot.nm ratio.nm `$\\bar{R}$` `$F_{R}$` `$p_{R}$`
<dbl> <chr> <int> <chr> <dbl> <dbl> <dbl>
1 1 asymmetric 1 13C/12C 0.0111 63.2 1.28e-39
2 1 asymmetric 2 13C/12C 0.0111 74.9 1.09e-46
3 1 asymmetric 3 13C/12C 0.0111 71.9 6.70e-45
4 1 ideal 1 13C/12C 0.0112 0.267 8.49e- 1
5 1 ideal 2 13C/12C 0.0112 0.219 8.83e- 1
6 1 ideal 3 13C/12C 0.0112 1.73 1.59e- 1
7 1 symmetric 1 13C/12C 0.0110 105. 2.00e-64
8 1 symmetric 2 13C/12C 0.0110 127. 2.81e-77
9 1 symmetric 3 13C/12C 0.0110 122. 1.42e-74
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .nest = type.nm, .label = "latex")
Output
# A tibble: 9 x 11
execution type.nm spot.nm ratio.nm `$\\bar{R}$` `$F_{R}$` `$p_{R}$`
<dbl> <chr> <int> <chr> <dbl> <dbl> <dbl>
1 1 asymmetric 1 13C/12C 0.0111 63.2 1.28e-39
2 1 asymmetric 2 13C/12C 0.0111 74.9 1.09e-46
3 1 asymmetric 3 13C/12C 0.0111 71.9 6.70e-45
4 1 ideal 1 13C/12C 0.0112 0.267 8.49e- 1
5 1 ideal 2 13C/12C 0.0112 0.219 8.83e- 1
6 1 ideal 3 13C/12C 0.0112 1.73 1.59e- 1
7 1 symmetric 1 13C/12C 0.0110 105. 2.00e-64
8 1 symmetric 2 13C/12C 0.0110 127. 2.81e-77
9 1 symmetric 3 13C/12C 0.0110 122. 1.42e-74
# ... with 4 more variables: `$\\hat{\\bar{R}}$` <dbl>,
# `$\\hat{\\epsilon}_{\\bar{R}}$ (\\text{\\textperthousand})` <dbl>,
# `$\\Delta AIC_{\\bar{R}}$` <dbl>, `$p_{\\bar{R}}$` <dbl>
QQ diagnostic on synthetic data is consistent
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .method = "QQ")
Output
# A tibble: 9 x 3
type.nm spot.nm hyp
<chr> <int> <chr>
1 symmetric 1 H0 (normal)
2 symmetric 2 H0 (normal)
3 symmetric 3 H0 (normal)
4 asymmetric 1 Ha (non-normal)
5 asymmetric 2 Ha (non-normal)
6 asymmetric 3 Ha (non-normal)
7 ideal 1 H0 (normal)
8 ideal 2 H0 (normal)
9 ideal 3 H0 (normal)
IR diagnostic on synthetic data is consistent
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .method = "IR")
Output
# A tibble: 9 x 3
type.nm spot.nm hyp
<chr> <int> <chr>
1 symmetric 1 Ha (dependence of residuals)
2 symmetric 2 Ha (dependence of residuals)
3 symmetric 3 Ha (dependence of residuals)
4 asymmetric 1 Ha (dependence of residuals)
5 asymmetric 2 Ha (dependence of residuals)
6 asymmetric 3 Ha (dependence of residuals)
7 ideal 1 H0 (independence of residuals)
8 ideal 2 H0 (independence of residuals)
9 ideal 3 H0 (independence of residuals)
diagnostics preserve metadata
Code
diag_R(real_IC, "13C", "12C", file.nm)
Output
# A tibble: 3 x 6
execution file.nm ratio.nm M_R_Xt.pr F_R_Xt.pr p_R_Xt.pr
<dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 1 2018-01-19-GLENDON_1_1 13C/12C 0.0110 2.13 0.0940
2 1 2018-01-19-GLENDON_1_2 13C/12C 0.0110 1.37 0.250
3 1 2018-01-19-GLENDON_1_3 13C/12C 0.0110 4.43 0.00410
Code
unfold(diag_R(real_IC, "13C", "12C", file.nm, .meta = TRUE))
Output
# A tibble: 81,900 x 43
execution file.nm ratio.nm M_R_Xt.pr F_R_Xt.pr p_R_Xt.pr t.nm species.nm
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 0.54 12C
2 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 1.08 12C
3 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 1.62 12C
4 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 2.16 12C
5 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 2.7 12C
6 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 3.24 12C
7 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 3.78 12C
8 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 4.32 12C
9 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 4.86 12C
10 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 5.4 12C
# ... with 81,890 more rows, and 35 more variables: sample.nm <chr>,
# bl.nm <int>, num.mt <dbl>, bfield.mt <dbl>, rad.mt <dbl>, mass.mt <chr>,
# tc.mt <dbl>, coord.mt <chr>, file_raw.mt <chr>, bl_num.mt <dbl>,
# meas_bl.mt <dbl>, rejection.mt <dbl>, slit.mt <chr>, lens.mt <chr>,
# presput.mt <chr>, rast_com.mt <dbl>, frame.mt <chr>, blank_rast.mt <chr>,
# raster.mt <chr>, tune.mt <chr>, reg_mode.mt <chr>, chk_frm.mt <dbl>,
# sec_ion_cent.mt <chr>, frame_sec_ion_cent.mt <chr>, width_hor.mt <dbl>, ...
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diagnostics wrapper on synthetic data is consistent
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm)
Output
# A tibble: 9 x 7
execution type.nm spot.nm ratio.nm M_R_Xt.pr F_R_Xt.pr p_R_Xt.pr
<dbl> <chr> <int> <chr> <dbl> <dbl> <dbl>
1 1 asymmetric 1 13C/12C 0.0111 63.2 1.28e-39
2 1 asymmetric 2 13C/12C 0.0111 74.9 1.09e-46
3 1 asymmetric 3 13C/12C 0.0111 71.9 6.70e-45
4 1 ideal 1 13C/12C 0.0112 0.267 8.49e- 1
5 1 ideal 2 13C/12C 0.0112 0.219 8.83e- 1
6 1 ideal 3 13C/12C 0.0112 1.73 1.59e- 1
7 1 symmetric 1 13C/12C 0.0110 105. 2.00e-64
8 1 symmetric 2 13C/12C 0.0110 127. 2.81e-77
9 1 symmetric 3 13C/12C 0.0110 122. 1.42e-74
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .method = "CV")
Output
# A tibble: 9 x 3
type.nm spot.nm hyp
<chr> <int> <chr>
1 symmetric 1 H0 (homoskedasticity)
2 symmetric 2 H0 (homoskedasticity)
3 symmetric 3 H0 (homoskedasticity)
4 asymmetric 1 H0 (homoskedasticity)
5 asymmetric 2 H0 (homoskedasticity)
6 asymmetric 3 H0 (homoskedasticity)
7 ideal 1 H0 (homoskedasticity)
8 ideal 2 H0 (homoskedasticity)
9 ideal 3 H0 (homoskedasticity)
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "complete")
Output
# A tibble: 27,000 x 56
execution type.nm spot.nm t.nm trend.nm.13C trend.nm.12C base.nm.13C
<dbl> <chr> <int> <int> <dbl> <dbl> <dbl>
1 1 asymmetric 1 1 120 120 0
2 1 asymmetric 1 2 120 120 0
3 1 asymmetric 1 3 120 120 0
4 1 asymmetric 1 4 120 120 0
5 1 asymmetric 1 5 120 120 0
6 1 asymmetric 1 6 120 120 0
7 1 asymmetric 1 7 120 120 0
8 1 asymmetric 1 8 120 120 0
9 1 asymmetric 1 9 120 120 0
10 1 asymmetric 1 10 120 120 0
# ... with 26,990 more rows, and 49 more variables: base.nm.12C <dbl>,
# force.nm.13C <dbl>, force.nm.12C <dbl>, bl.nm.13C <int>, bl.nm.12C <int>,
# n.rw.13C <dbl>, n.rw.12C <dbl>, N.pr.13C <dbl>, N.pr.12C <dbl>,
# Xt.pr.13C <dbl>, Xt.pr.12C <dbl>, n_t.nm.13C <int>, n_t.nm.12C <int>,
# tot_N.pr.13C <dbl>, tot_N.pr.12C <dbl>, M_Xt.pr.13C <dbl>,
# M_Xt.pr.12C <dbl>, S_Xt.pr.13C <dbl>, S_Xt.pr.12C <dbl>,
# RS_Xt.pr.13C <dbl>, RS_Xt.pr.12C <dbl>, SeM_Xt.pr.13C <dbl>, ...
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "augmented")
Output
# A tibble: 104,980 x 12
execution type.nm trend.nm base.nm force.nm t.nm bl.nm n.rw spot.nm
<dbl> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <int>
1 1 symmetric 120 0 -60 1 1 3000 1
2 1 symmetric 120 0 -60 1 1 3000 1
3 1 symmetric 120 0 -60 1 1 3000 2
4 1 symmetric 120 0 -60 1 1 3000 2
5 1 symmetric 120 0 -60 1 1 3000 3
6 1 symmetric 120 0 -60 1 1 3000 3
7 1 symmetric 120 0 -60 2 1 3000 1
8 1 symmetric 120 0 -60 2 1 3000 1
9 1 symmetric 120 0 -60 2 1 3000 2
10 1 symmetric 120 0 -60 2 1 3000 2
# ... with 104,970 more rows, and 3 more variables: species.nm <chr>,
# N.pr <dbl>, Xt.pr <dbl>
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "diagnostic")
Output
# A tibble: 27,000 x 54
execution type.nm spot.nm t.nm trend.nm.13C trend.nm.12C base.nm.13C
<dbl> <chr> <int> <int> <dbl> <dbl> <dbl>
1 1 symmetric 1 1 120 120 0
2 1 symmetric 1 2 120 120 0
3 1 symmetric 1 3 120 120 0
4 1 symmetric 1 4 120 120 0
5 1 symmetric 1 5 120 120 0
6 1 symmetric 1 6 120 120 0
7 1 symmetric 1 7 120 120 0
8 1 symmetric 1 8 120 120 0
9 1 symmetric 1 9 120 120 0
10 1 symmetric 1 10 120 120 0
# ... with 26,990 more rows, and 47 more variables: base.nm.12C <dbl>,
# force.nm.13C <dbl>, force.nm.12C <dbl>, bl.nm.13C <int>, bl.nm.12C <int>,
# n.rw.13C <dbl>, n.rw.12C <dbl>, N.pr.13C <dbl>, N.pr.12C <dbl>,
# Xt.pr.13C <dbl>, Xt.pr.12C <dbl>, n_t.nm.13C <int>, n_t.nm.12C <int>,
# tot_N.pr.13C <dbl>, tot_N.pr.12C <dbl>, M_Xt.pr.13C <dbl>,
# M_Xt.pr.12C <dbl>, S_Xt.pr.13C <dbl>, S_Xt.pr.12C <dbl>,
# RS_Xt.pr.13C <dbl>, RS_Xt.pr.12C <dbl>, SeM_Xt.pr.13C <dbl>, ...
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .output = "outlier")
Output
# A tibble: 27,000 x 22
execution type.nm spot.nm t.nm trend.nm.13C trend.nm.12C base.nm.13C
<dbl> <chr> <int> <int> <dbl> <dbl> <dbl>
1 1 symmetric 1 1 120 120 0
2 1 symmetric 1 2 120 120 0
3 1 symmetric 1 3 120 120 0
4 1 symmetric 1 4 120 120 0
5 1 symmetric 1 5 120 120 0
6 1 symmetric 1 6 120 120 0
7 1 symmetric 1 7 120 120 0
8 1 symmetric 1 8 120 120 0
9 1 symmetric 1 9 120 120 0
10 1 symmetric 1 10 120 120 0
# ... with 26,990 more rows, and 15 more variables: base.nm.12C <dbl>,
# force.nm.13C <dbl>, force.nm.12C <dbl>, bl.nm.13C <int>, bl.nm.12C <int>,
# n.rw.13C <dbl>, n.rw.12C <dbl>, N.pr.13C <dbl>, N.pr.12C <dbl>,
# Xt.pr.13C <dbl>, Xt.pr.12C <dbl>, hat_S_N.pr.13C <dbl>,
# hat_Xt.pr.13C <dbl>, CooksD <dbl>, flag <fct>
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .label = "latex")
Output
# A tibble: 9 x 7
execution type.nm spot.nm ratio.nm `$\\bar{R}$` `$F_{R}$` `$p_{R}$`
<dbl> <chr> <int> <chr> <dbl> <dbl> <dbl>
1 1 asymmetric 1 13C/12C 0.0111 63.2 1.28e-39
2 1 asymmetric 2 13C/12C 0.0111 74.9 1.09e-46
3 1 asymmetric 3 13C/12C 0.0111 71.9 6.70e-45
4 1 ideal 1 13C/12C 0.0112 0.267 8.49e- 1
5 1 ideal 2 13C/12C 0.0112 0.219 8.83e- 1
6 1 ideal 3 13C/12C 0.0112 1.73 1.59e- 1
7 1 symmetric 1 13C/12C 0.0110 105. 2.00e-64
8 1 symmetric 2 13C/12C 0.0110 127. 2.81e-77
9 1 symmetric 3 13C/12C 0.0110 122. 1.42e-74
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .nest = type.nm, .label = "latex")
Output
# A tibble: 9 x 11
execution type.nm spot.nm ratio.nm `$\\bar{R}$` `$F_{R}$` `$p_{R}$`
<dbl> <chr> <int> <chr> <dbl> <dbl> <dbl>
1 1 asymmetric 1 13C/12C 0.0111 63.2 1.28e-39
2 1 asymmetric 2 13C/12C 0.0111 74.9 1.09e-46
3 1 asymmetric 3 13C/12C 0.0111 71.9 6.70e-45
4 1 ideal 1 13C/12C 0.0112 0.267 8.49e- 1
5 1 ideal 2 13C/12C 0.0112 0.219 8.83e- 1
6 1 ideal 3 13C/12C 0.0112 1.73 1.59e- 1
7 1 symmetric 1 13C/12C 0.0110 105. 2.00e-64
8 1 symmetric 2 13C/12C 0.0110 127. 2.81e-77
9 1 symmetric 3 13C/12C 0.0110 122. 1.42e-74
# ... with 4 more variables: `$\\hat{\\bar{R}}$` <dbl>,
# `$\\hat{\\epsilon}_{\\bar{R}}$ (\\text{\\textperthousand})` <dbl>,
# `$\\Delta AIC_{\\bar{R}}$` <dbl>, `$p_{\\bar{R}}$` <dbl>
QQ diagnostic on synthetic data is consistent
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .method = "QQ")
Output
# A tibble: 9 x 3
type.nm spot.nm hyp
<chr> <int> <chr>
1 symmetric 1 H0 (normal)
2 symmetric 2 H0 (normal)
3 symmetric 3 H0 (normal)
4 asymmetric 1 Ha (non-normal)
5 asymmetric 2 Ha (non-normal)
6 asymmetric 3 Ha (non-normal)
7 ideal 1 H0 (normal)
8 ideal 2 H0 (normal)
9 ideal 3 H0 (normal)
IR diagnostic on synthetic data is consistent
Code
diag_R(simu_IC, "13C", "12C", type.nm, spot.nm, .method = "IR")
Output
# A tibble: 9 x 3
type.nm spot.nm hyp
<chr> <int> <chr>
1 symmetric 1 Ha (dependence of residuals)
2 symmetric 2 Ha (dependence of residuals)
3 symmetric 3 Ha (dependence of residuals)
4 asymmetric 1 Ha (dependence of residuals)
5 asymmetric 2 Ha (dependence of residuals)
6 asymmetric 3 Ha (dependence of residuals)
7 ideal 1 H0 (independence of residuals)
8 ideal 2 H0 (independence of residuals)
9 ideal 3 H0 (independence of residuals)
diagnostics preserve metadata
Code
diag_R(real_IC, "13C", "12C", file.nm)
Output
# A tibble: 3 x 6
execution file.nm ratio.nm M_R_Xt.pr F_R_Xt.pr p_R_Xt.pr
<dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 1 2018-01-19-GLENDON_1_1 13C/12C 0.0110 2.13 0.0940
2 1 2018-01-19-GLENDON_1_2 13C/12C 0.0110 1.37 0.250
3 1 2018-01-19-GLENDON_1_3 13C/12C 0.0110 4.43 0.00410
Code
unfold(diag_R(real_IC, "13C", "12C", file.nm, .meta = TRUE))
Output
# A tibble: 81,900 x 43
execution file.nm ratio.nm M_R_Xt.pr F_R_Xt.pr p_R_Xt.pr t.nm species.nm
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 0.54 12C
2 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 1.08 12C
3 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 1.62 12C
4 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 2.16 12C
5 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 2.7 12C
6 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 3.24 12C
7 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 3.78 12C
8 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 4.32 12C
9 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 4.86 12C
10 1 2018-01-19~ 13C/12C 0.0110 2.13 0.0940 5.4 12C
# ... with 81,890 more rows, and 35 more variables: sample.nm <chr>,
# bl.nm <int>, num.mt <dbl>, bfield.mt <dbl>, rad.mt <dbl>, mass.mt <chr>,
# tc.mt <dbl>, coord.mt <chr>, file_raw.mt <chr>, bl_num.mt <dbl>,
# meas_bl.mt <dbl>, rejection.mt <dbl>, slit.mt <chr>, lens.mt <chr>,
# presput.mt <chr>, rast_com.mt <dbl>, frame.mt <chr>, blank_rast.mt <chr>,
# raster.mt <chr>, tune.mt <chr>, reg_mode.mt <chr>, chk_frm.mt <dbl>,
# sec_ion_cent.mt <chr>, frame_sec_ion_cent.mt <chr>, width_hor.mt <dbl>, ...
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