Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: lme4
Loading required package: Matrix
Attaching package: 'lmerTest'
The following object is masked from 'package:lme4':
lmer
The following object is masked from 'package:stats':
step
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.0 ✔ readr 2.1.5
✔ ggplot2 3.5.1 ✔ stringr 1.5.1
✔ lubridate 1.9.3 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::expand() masks Matrix::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ tidyr::pack() masks Matrix::pack()
✖ tidyr::unpack() masks Matrix::unpack()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(modelr)
library(purrr)
library(emmeans)
Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'
Attaching package: 'gridExtra'
The following object is masked from 'package:dplyr':
combine
library(writexl)
library(gt)
library(webshot2)
library(broom.mixed)
library(ggplot2)
load("Z:/Isaac/Visual Features/1-5/step2.RData")
# model with mean value
aug_res_10
# A tibble: 117,623 × 19
feature sow time_group_10 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -18.2 63367.
2 Area 1 2 2025-04-01 09:20:00 9 -18.1 57510.
3 Area 1 3 2025-04-01 09:30:01 9 -17.9 57500.
4 Area 1 4 2025-04-01 09:40:02 9 -17.8 57232.
5 Area 1 5 2025-04-01 09:50:00 9 -17.6 56487.
6 Area 1 6 2025-04-01 10:00:01 10 -17.4 57331.
7 Area 1 7 2025-04-01 10:10:02 10 -17.3 55908.
8 Area 1 8 2025-04-01 10:20:00 10 -17.1 59313.
9 Area 1 9 2025-04-01 10:30:00 10 -16.9 54803.
10 Area 1 10 2025-04-01 10:40:01 10 -16.8 57151.
# ℹ 117,613 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 58,973 × 19
feature sow time_group_20 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -18.2 63367.
2 Area 1 2 2025-04-01 09:20:00 9 -18.0 57505.
3 Area 1 3 2025-04-01 09:40:02 9 -17.7 56858.
4 Area 1 4 2025-04-01 10:00:01 10 -17.4 56622.
5 Area 1 5 2025-04-01 10:20:00 10 -17.0 57058.
6 Area 1 6 2025-04-01 10:40:01 10 -16.7 57006.
7 Area 1 7 2025-04-01 11:00:00 11 -16.4 59473.
8 Area 1 8 2025-04-01 11:20:02 11 -16.0 58641.
9 Area 1 9 2025-04-01 11:40:01 11 -15.7 56930.
10 Area 1 10 2025-04-01 12:00:00 12 -15.4 51047.
# ℹ 58,963 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 39,423 × 19
feature sow time_group_30 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -18.2 59814.
2 Area 1 2 2025-04-01 09:30:01 9 -17.8 57073.
3 Area 1 3 2025-04-01 10:00:01 10 -17.3 57520.
4 Area 1 4 2025-04-01 10:30:00 10 -16.8 56271.
5 Area 1 5 2025-04-01 11:00:00 11 -16.3 58836.
6 Area 1 6 2025-04-01 11:30:00 11 -15.8 57861.
7 Area 1 7 2025-04-01 12:00:00 12 -15.3 51257.
8 Area 1 8 2025-04-01 12:30:00 12 -14.8 48846.
9 Area 1 9 2025-04-01 13:00:02 13 -14.3 47773.
10 Area 1 10 2025-04-01 13:30:02 13 -13.8 47942.
# ℹ 39,413 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 19,890 × 19
feature sow time_group_60 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -17.9 58046.
2 Area 1 2 2025-04-01 10:00:01 10 -17.0 56895.
3 Area 1 3 2025-04-01 11:00:00 11 -16.0 58349.
4 Area 1 4 2025-04-01 12:00:00 12 -15.0 50050.
5 Area 1 5 2025-04-01 13:00:02 13 -14.0 47858.
6 Area 1 6 2025-04-01 14:00:02 14 -13.0 50943.
7 Area 1 7 2025-04-01 15:00:01 15 -12.0 50301.
8 Area 1 8 2025-04-01 16:00:01 16 -11.0 53684.
9 Area 1 9 2025-04-01 17:00:01 17 -10.0 47713.
10 Area 1 10 2025-04-01 18:00:00 18 -9.03 54449.
# ℹ 19,880 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# model with var value
aug_res_10_var
# A tibble: 117,623 × 19
feature sow time_group_10 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -18.2 63367.
2 Area 1 2 2025-04-01 09:20:00 9 -18.1 57510.
3 Area 1 3 2025-04-01 09:30:01 9 -17.9 57500.
4 Area 1 4 2025-04-01 09:40:02 9 -17.8 57232.
5 Area 1 5 2025-04-01 09:50:00 9 -17.6 56487.
6 Area 1 6 2025-04-01 10:00:01 10 -17.4 57331.
7 Area 1 7 2025-04-01 10:10:02 10 -17.3 55908.
8 Area 1 8 2025-04-01 10:20:00 10 -17.1 59313.
9 Area 1 9 2025-04-01 10:30:00 10 -16.9 54803.
10 Area 1 10 2025-04-01 10:40:01 10 -16.8 57151.
# ℹ 117,613 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 58,973 × 19
feature sow time_group_20 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -18.2 63367.
2 Area 1 2 2025-04-01 09:20:00 9 -18.0 57505.
3 Area 1 3 2025-04-01 09:40:02 9 -17.7 56858.
4 Area 1 4 2025-04-01 10:00:01 10 -17.4 56622.
5 Area 1 5 2025-04-01 10:20:00 10 -17.0 57058.
6 Area 1 6 2025-04-01 10:40:01 10 -16.7 57006.
7 Area 1 7 2025-04-01 11:00:00 11 -16.4 59473.
8 Area 1 8 2025-04-01 11:20:02 11 -16.0 58641.
9 Area 1 9 2025-04-01 11:40:01 11 -15.7 56930.
10 Area 1 10 2025-04-01 12:00:00 12 -15.4 51047.
# ℹ 58,963 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 39,423 × 19
feature sow time_group_30 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -18.2 59814.
2 Area 1 2 2025-04-01 09:30:01 9 -17.8 57073.
3 Area 1 3 2025-04-01 10:00:01 10 -17.3 57520.
4 Area 1 4 2025-04-01 10:30:00 10 -16.8 56271.
5 Area 1 5 2025-04-01 11:00:00 11 -16.3 58836.
6 Area 1 6 2025-04-01 11:30:00 11 -15.8 57861.
7 Area 1 7 2025-04-01 12:00:00 12 -15.3 51257.
8 Area 1 8 2025-04-01 12:30:00 12 -14.8 48846.
9 Area 1 9 2025-04-01 13:00:02 13 -14.3 47773.
10 Area 1 10 2025-04-01 13:30:02 13 -13.8 47942.
# ℹ 39,413 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 19,890 × 19
feature sow time_group_60 datetime hour ttf mean_value
<fct> <fct> <fct> <dttm> <fct> <dbl> <dbl>
1 Area 1 1 2025-04-01 09:13:30 9 -17.9 58046.
2 Area 1 2 2025-04-01 10:00:01 10 -17.0 56895.
3 Area 1 3 2025-04-01 11:00:00 11 -16.0 58349.
4 Area 1 4 2025-04-01 12:00:00 12 -15.0 50050.
5 Area 1 5 2025-04-01 13:00:02 13 -14.0 47858.
6 Area 1 6 2025-04-01 14:00:02 14 -13.0 50943.
7 Area 1 7 2025-04-01 15:00:01 15 -12.0 50301.
8 Area 1 8 2025-04-01 16:00:01 16 -11.0 53684.
9 Area 1 9 2025-04-01 17:00:01 17 -10.0 47713.
10 Area 1 10 2025-04-01 18:00:00 18 -9.03 54449.
# ℹ 19,880 more rows
# ℹ 12 more variables: sd_value <dbl>, var_value <dbl>, n_obs <int>,
# .fitted <dbl>, .fixed <dbl>, .mu <dbl>, .offset <dbl>, .sqrtXwt <dbl>,
# .sqrtrwt <dbl>, .weights <dbl>, .wtres <dbl>, .resid <dbl>
# A tibble: 17 × 4
feature sow Residual repeatability
<fct> <dbl> <dbl> <dbl>
1 Area 10653. 5575. 0.656
2 Centroid.X 23.3 21.1 0.525
3 Centroid.Y 16.7 11.2 0.597
4 Concavity 0.0310 0.0484 0.390
5 Convex.Area 12243. 8730. 0.584
6 Convex.Perimeter 90.1 53.8 0.626
7 Eccentricity 0.0266 0.0506 0.345
8 Elasticity 0.159 0.143 0.526
9 Elongation 0.144 0.239 0.376
10 Height 22.0 27.0 0.450
11 Major.Axis.Length 34.0 22.2 0.605
12 Minor.Axis.Length 22.1 27.0 0.450
13 Perimeter 173. 197. 0.467
14 Rightmost.X 27.4 14.7 0.650
15 Rightmost.Y 23.6 46.0 0.339
16 Roundness 0.0956 0.0871 0.523
17 Width 34.0 22.2 0.605
# A tibble: 17 × 4
feature sow Residual repeatability
<fct> <dbl> <dbl> <dbl>
1 Area 10606. 5288. 0.667
2 Centroid.X 23.3 19.8 0.541
3 Centroid.Y 16.6 10.5 0.614
4 Concavity 0.0302 0.0448 0.403
5 Convex.Area 12142. 8179. 0.598
6 Convex.Perimeter 89.8 50.0 0.642
7 Eccentricity 0.0266 0.0479 0.357
8 Elasticity 0.159 0.134 0.542
9 Elongation 0.143 0.224 0.390
10 Height 21.6 25.3 0.461
11 Major.Axis.Length 34.1 20.8 0.621
12 Minor.Axis.Length 21.6 25.2 0.461
13 Perimeter 170. 184. 0.480
14 Rightmost.X 27.2 13.6 0.667
15 Rightmost.Y 23.4 42.0 0.358
16 Roundness 0.0952 0.0806 0.541
17 Width 34.1 20.8 0.621
# A tibble: 17 × 4
feature sow Residual repeatability
<fct> <dbl> <dbl> <dbl>
1 Area 10549. 5096. 0.674
2 Centroid.X 23.2 19.0 0.550
3 Centroid.Y 16.4 9.87 0.624
4 Concavity 0.0302 0.0425 0.415
5 Convex.Area 12109. 7788. 0.609
6 Convex.Perimeter 89.0 47.6 0.651
7 Eccentricity 0.0266 0.0462 0.365
8 Elasticity 0.159 0.130 0.550
9 Elongation 0.144 0.215 0.402
10 Height 21.7 24.0 0.475
11 Major.Axis.Length 33.6 20.0 0.627
12 Minor.Axis.Length 21.8 24.0 0.475
13 Perimeter 169. 177. 0.489
14 Rightmost.X 26.7 12.9 0.675
15 Rightmost.Y 23.1 39.6 0.369
16 Roundness 0.0950 0.0770 0.552
17 Width 33.7 20.0 0.627
# A tibble: 17 × 4
feature sow Residual repeatability
<fct> <dbl> <dbl> <dbl>
1 Area 10465. 4669. 0.691
2 Centroid.X 23.2 17.8 0.567
3 Centroid.Y 16.5 8.48 0.660
4 Concavity 0.0292 0.0384 0.432
5 Convex.Area 11981. 7002. 0.631
6 Convex.Perimeter 89.7 42.9 0.677
7 Eccentricity 0.0261 0.0425 0.381
8 Elasticity 0.159 0.119 0.573
9 Elongation 0.141 0.195 0.419
10 Height 20.8 21.7 0.490
11 Major.Axis.Length 34.4 18.4 0.651
12 Minor.Axis.Length 20.8 21.7 0.490
13 Perimeter 168. 161. 0.511
14 Rightmost.X 27.5 11.7 0.701
15 Rightmost.Y 23.0 34.1 0.402
16 Roundness 0.0954 0.0701 0.576
17 Width 34.5 18.4 0.651
# model with mean value
df_10_min_vars
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 65405. 2535. 25.8
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 66019. 2535. 26.0
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 65846. 2535. 26.0
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 66139. 2535. 26.1
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 65748. 2536. 25.9
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 64887. 2536. 25.6
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 65198. 2535. 25.7
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 65832. 2535. 26.0
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 65243. 2535. 25.7
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 65294. 2533. 25.8
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.07e+4 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.58e+3 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 2.33e+1 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 2.11e+1 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.67e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.12e+1 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 3.10e-2 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 4.84e-2 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.22e+4 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 8.73e+3 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 6919 5.58e+3 -6.94e4 1.39e5 139002. 138772.
2 Centroid.X <tibble> <lmrMdLmT> 6919 2.11e+1 -3.09e4 6.19e4 62053. 61823.
3 Centroid.Y <tibble> <lmrMdLmT> 6919 1.12e+1 -2.66e4 5.32e4 53416. 53186.
4 Concavity <tibble> <lmrMdLmT> 6919 4.84e-2 1.10e4 -2.19e4 -21746. -21976.
5 Convex.Ar… <tibble> <lmrMdLmT> 6919 8.73e+3 -7.25e4 1.45e5 145175. 144945.
6 Convex.Pe… <tibble> <lmrMdLmT> 6919 5.38e+1 -3.74e4 7.48e4 75006. 74776.
7 Eccentric… <tibble> <lmrMdLmT> 6919 5.06e-2 1.07e4 -2.13e4 -21144. -21374.
8 Elasticity <tibble> <lmrMdLmT> 6919 1.43e-1 3.49e3 -6.93e3 -6751. -6980.
9 Elongation <tibble> <lmrMdLmT> 6919 2.39e-1 -2.03e1 9.26e1 271. 40.6
10 Height <tibble> <lmrMdLmT> 6919 2.70e+1 -3.26e4 6.53e4 65450. 65220.
11 Major.Axi… <tibble> <lmrMdLmT> 6919 2.22e+1 -3.13e4 6.26e4 62802. 62572.
12 Minor.Axi… <tibble> <lmrMdLmT> 6919 2.70e+1 -3.26e4 6.53e4 65450. 65220.
13 Perimeter <tibble> <lmrMdLmT> 6919 1.97e+2 -4.63e4 9.27e4 92904. 92674.
14 Rightmost… <tibble> <lmrMdLmT> 6919 1.47e+1 -2.84e4 5.69e4 57120. 56890.
15 Rightmost… <tibble> <lmrMdLmT> 6919 4.60e+1 -3.63e4 7.26e4 72790. 72560.
16 Roundness <tibble> <lmrMdLmT> 6919 8.71e-2 6.93e3 -1.38e4 -13633. -13863.
17 Width <tibble> <lmrMdLmT> 6919 2.22e+1 -3.13e4 6.26e4 62804. 62574.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [6,919 × 17]>
2 Centroid.X <tibble [6,919 × 17]>
3 Centroid.Y <tibble [6,919 × 17]>
4 Concavity <tibble [6,919 × 17]>
5 Convex.Area <tibble [6,919 × 17]>
6 Convex.Perimeter <tibble [6,919 × 17]>
7 Eccentricity <tibble [6,919 × 17]>
8 Elasticity <tibble [6,919 × 17]>
9 Elongation <tibble [6,919 × 17]>
10 Height <tibble [6,919 × 17]>
11 Major.Axis.Length <tibble [6,919 × 17]>
12 Minor.Axis.Length <tibble [6,919 × 17]>
13 Perimeter <tibble [6,919 × 17]>
14 Rightmost.X <tibble [6,919 × 17]>
15 Rightmost.Y <tibble [6,919 × 17]>
16 Roundness <tibble [6,919 × 17]>
17 Width <tibble [6,919 × 17]>
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 65422. 2544. 25.7
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 66033. 2543. 26.0
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 65864. 2543. 25.9
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 66156. 2543. 26.0
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 65768. 2544. 25.9
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 64903. 2544. 25.5
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 65212. 2543. 25.6
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 65857. 2542. 25.9
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 65232. 2542. 25.7
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 65321. 2539. 25.7
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.06e+4 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.29e+3 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 2.33e+1 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.98e+1 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.66e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.05e+1 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 3.02e-2 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 4.48e-2 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.21e+4 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 8.18e+3 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 3469 5.29e+3 -34535. 69122. 69282. 69070.
2 Centroid.X <tibble> <lmrMdLmT> 3469 1.98e+1 -15271. 30594. 30754. 30542.
3 Centroid.Y <tibble> <lmrMdLmT> 3469 1.05e+1 -13081. 26214. 26374. 26162.
4 Concavity <tibble> <lmrMdLmT> 3469 4.48e-2 5715. -11378. -11218. -11430.
5 Convex.Ar… <tibble> <lmrMdLmT> 3469 8.18e+3 -36032. 72116. 72276. 72064.
6 Convex.Pe… <tibble> <lmrMdLmT> 3469 5.00e+1 -18477. 37007. 37167. 36955.
7 Eccentric… <tibble> <lmrMdLmT> 3469 4.79e-2 5486. -10920. -10760. -10972.
8 Elasticity <tibble> <lmrMdLmT> 3469 1.34e-1 1925. -3798. -3638. -3850.
9 Elongation <tibble> <lmrMdLmT> 3469 2.24e-1 168. -285. -125. -337.
10 Height <tibble> <lmrMdLmT> 3469 2.53e+1 -16109. 32270. 32430. 32218.
11 Major.Axi… <tibble> <lmrMdLmT> 3469 2.08e+1 -15451. 30954. 31114. 30902.
12 Minor.Axi… <tibble> <lmrMdLmT> 3469 2.52e+1 -16109. 32270. 32430. 32218.
13 Perimeter <tibble> <lmrMdLmT> 3469 1.84e+2 -22957. 45966. 46126. 45914.
14 Rightmost… <tibble> <lmrMdLmT> 3469 1.36e+1 -13987. 28026. 28186. 27974.
15 Rightmost… <tibble> <lmrMdLmT> 3469 4.20e+1 -17851. 35754. 35914. 35702.
16 Roundness <tibble> <lmrMdLmT> 3469 8.06e-2 3683. -7315. -7155. -7367.
17 Width <tibble> <lmrMdLmT> 3469 2.08e+1 -15451. 30955. 31115. 30903.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [3,469 × 17]>
2 Centroid.X <tibble [3,469 × 17]>
3 Centroid.Y <tibble [3,469 × 17]>
4 Concavity <tibble [3,469 × 17]>
5 Convex.Area <tibble [3,469 × 17]>
6 Convex.Perimeter <tibble [3,469 × 17]>
7 Eccentricity <tibble [3,469 × 17]>
8 Elasticity <tibble [3,469 × 17]>
9 Elongation <tibble [3,469 × 17]>
10 Height <tibble [3,469 × 17]>
11 Major.Axis.Length <tibble [3,469 × 17]>
12 Minor.Axis.Length <tibble [3,469 × 17]>
13 Perimeter <tibble [3,469 × 17]>
14 Rightmost.X <tibble [3,469 × 17]>
15 Rightmost.Y <tibble [3,469 × 17]>
16 Roundness <tibble [3,469 × 17]>
17 Width <tibble [3,469 × 17]>
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 65444. 2547. 25.7
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 66035. 2546. 25.9
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 65885. 2546. 25.9
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 66166. 2546. 26.0
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 65789. 2547. 25.8
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 64921. 2547. 25.5
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 65227. 2546. 25.6
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 65883. 2545. 25.9
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 65307. 2545. 25.7
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 65299. 2541. 25.7
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.05e+4 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.10e+3 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 2.32e+1 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.90e+1 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.64e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 9.87e+0 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 3.02e-2 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 4.25e-2 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.21e+4 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 7.79e+3 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 2319 5.10e+3 -22951. 45955. 46104. 45903.
2 Centroid.X <tibble> <lmrMdLmT> 2319 1.90e+1 -10107. 20265. 20415. 20213.
3 Centroid.Y <tibble> <lmrMdLmT> 2319 9.87e+0 -8611. 17273. 17423. 17221.
4 Concavity <tibble> <lmrMdLmT> 2319 4.25e-2 3904. -7755. -7606. -7807.
5 Convex.Area <tibble> <lmrMdLmT> 2319 7.79e+3 -23920. 47892. 48041. 47840.
6 Convex.Peri… <tibble> <lmrMdLmT> 2319 4.76e+1 -12225. 24501. 24651. 24449.
7 Eccentricity <tibble> <lmrMdLmT> 2319 4.62e-2 3717. -7381. -7232. -7433.
8 Elasticity <tibble> <lmrMdLmT> 2319 1.30e-1 1332. -2612. -2463. -2664.
9 Elongation <tibble> <lmrMdLmT> 2319 2.15e-1 185. -318. -168. -370.
10 Height <tibble> <lmrMdLmT> 2319 2.40e+1 -10645. 21342. 21492. 21290.
11 Major.Axis.… <tibble> <lmrMdLmT> 2319 2.00e+1 -10230. 20513. 20662. 20461.
12 Minor.Axis.… <tibble> <lmrMdLmT> 2319 2.40e+1 -10645. 21342. 21492. 21290.
13 Perimeter <tibble> <lmrMdLmT> 2319 1.77e+2 -15232. 30516. 30666. 30464.
14 Rightmost.X <tibble> <lmrMdLmT> 2319 1.29e+1 -9222. 18497. 18646. 18445.
15 Rightmost.Y <tibble> <lmrMdLmT> 2319 3.96e+1 -11784. 23620. 23769. 23568.
16 Roundness <tibble> <lmrMdLmT> 2319 7.70e-2 2532. -5011. -4862. -5063.
17 Width <tibble> <lmrMdLmT> 2319 2.00e+1 -10230. 20513. 20662. 20461.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [2,319 × 17]>
2 Centroid.X <tibble [2,319 × 17]>
3 Centroid.Y <tibble [2,319 × 17]>
4 Concavity <tibble [2,319 × 17]>
5 Convex.Area <tibble [2,319 × 17]>
6 Convex.Perimeter <tibble [2,319 × 17]>
7 Eccentricity <tibble [2,319 × 17]>
8 Elasticity <tibble [2,319 × 17]>
9 Elongation <tibble [2,319 × 17]>
10 Height <tibble [2,319 × 17]>
11 Major.Axis.Length <tibble [2,319 × 17]>
12 Minor.Axis.Length <tibble [2,319 × 17]>
13 Perimeter <tibble [2,319 × 17]>
14 Rightmost.X <tibble [2,319 × 17]>
15 Rightmost.Y <tibble [2,319 × 17]>
16 Roundness <tibble [2,319 × 17]>
17 Width <tibble [2,319 × 17]>
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 65463. 2567. 25.5
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 66084. 2565. 25.8
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 65903. 2565. 25.7
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 66152. 2565. 25.8
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 65806. 2568. 25.6
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 64931. 2566. 25.3
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 65227. 2564. 25.4
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 65901. 2564. 25.7
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 65195. 2559. 25.5
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 65254. 2556. 25.5
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.05e+4 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 4.67e+3 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 2.32e+1 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.78e+1 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.65e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 8.48e+0 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 2.92e-2 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 3.84e-2 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.20e+4 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 7.00e+3 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 1170 4.67e+3 -11400. 22853. 22984. 22801.
2 Centroid.X <tibble> <lmrMdLmT> 1170 1.78e+1 -5006. 10064. 10196. 10012.
3 Centroid.Y <tibble> <lmrMdLmT> 1170 8.48e+0 -4166. 8384. 8516. 8332.
4 Concavity <tibble> <lmrMdLmT> 1170 3.84e-2 2035. -4017. -3886. -4069.
5 Convex.Area <tibble> <lmrMdLmT> 1170 7.00e+3 -11860. 23772. 23904. 23720.
6 Convex.Peri… <tibble> <lmrMdLmT> 1170 4.29e+1 -6023. 12098. 12230. 12046.
7 Eccentricity <tibble> <lmrMdLmT> 1170 4.25e-2 1923. -3795. -3663. -3847.
8 Elasticity <tibble> <lmrMdLmT> 1170 1.19e-1 733. -1414. -1283. -1466.
9 Elongation <tibble> <lmrMdLmT> 1170 1.95e-1 172. -292. -160. -344.
10 Height <tibble> <lmrMdLmT> 1170 2.17e+1 -5228. 10508. 10640. 10456.
11 Major.Axis.… <tibble> <lmrMdLmT> 1170 1.84e+1 -5055. 10162. 10294. 10110.
12 Minor.Axis.… <tibble> <lmrMdLmT> 1170 2.17e+1 -5228. 10508. 10640. 10456.
13 Perimeter <tibble> <lmrMdLmT> 1170 1.61e+2 -7528. 15107. 15239. 15055.
14 Rightmost.X <tibble> <lmrMdLmT> 1170 1.17e+1 -4539. 9130. 9261. 9078.
15 Rightmost.Y <tibble> <lmrMdLmT> 1170 3.41e+1 -5744. 11540. 11672. 11488.
16 Roundness <tibble> <lmrMdLmT> 1170 7.01e-2 1336. -2620. -2488. -2672.
17 Width <tibble> <lmrMdLmT> 1170 1.84e+1 -5055. 10162. 10294. 10110.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [1,170 × 17]>
2 Centroid.X <tibble [1,170 × 17]>
3 Centroid.Y <tibble [1,170 × 17]>
4 Concavity <tibble [1,170 × 17]>
5 Convex.Area <tibble [1,170 × 17]>
6 Convex.Perimeter <tibble [1,170 × 17]>
7 Eccentricity <tibble [1,170 × 17]>
8 Elasticity <tibble [1,170 × 17]>
9 Elongation <tibble [1,170 × 17]>
10 Height <tibble [1,170 × 17]>
11 Major.Axis.Length <tibble [1,170 × 17]>
12 Minor.Axis.Length <tibble [1,170 × 17]>
13 Perimeter <tibble [1,170 × 17]>
14 Rightmost.X <tibble [1,170 × 17]>
15 Rightmost.Y <tibble [1,170 × 17]>
16 Roundness <tibble [1,170 × 17]>
17 Width <tibble [1,170 × 17]>
# model with var value
df_10_min_vars_var
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 15045670. 4251959. 3.54
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 18889841. 4248734. 4.45
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 19688705. 4247170. 4.64
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 20313839. 4249100. 4.78
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 22066486. 4253208. 5.19
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 22613153. 4252304. 5.32
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 21000999. 4247091. 4.94
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 19612317. 4243761. 4.62
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 25603955. 4243057. 6.03
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 26846034. 4227631. 6.35
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.69e+7 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 2.39e+7 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.14e+2 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.78e+2 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.10e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 6.56e+1 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 1.33e-3 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.49e-3 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.54e+7 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.00e+7 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 6919 2.39e+7 -1.27e5 254117. 254295. 254065.
2 Centroid.X <tibble> <lmrMdLmT> 6919 5.78e+2 -5.37e4 107522. 107700. 107470.
3 Centroid.Y <tibble> <lmrMdLmT> 6919 6.56e+1 -3.87e4 77510. 77688. 77458.
4 Concavity <tibble> <lmrMdLmT> 6919 1.49e-3 3.50e4 -69910. -69732. -69962.
5 Convex.Ar… <tibble> <lmrMdLmT> 6919 5.00e+7 -1.32e5 264278. 264456. 264226.
6 Convex.Pe… <tibble> <lmrMdLmT> 6919 3.25e+3 -6.56e4 131337. 131514. 131285.
7 Eccentric… <tibble> <lmrMdLmT> 6919 1.68e-3 3.41e4 -68245. -68067. -68297.
8 Elasticity <tibble> <lmrMdLmT> 6919 1.91e-2 1.74e4 -34774. -34596. -34826.
9 Elongation <tibble> <lmrMdLmT> 6919 3.20e-2 1.39e4 -27649. -27471. -27701.
10 Height <tibble> <lmrMdLmT> 6919 3.89e+2 -5.10e4 102023. 102201. 101971.
11 Major.Axi… <tibble> <lmrMdLmT> 6919 7.48e+2 -5.55e4 111094. 111272. 111042.
12 Minor.Axi… <tibble> <lmrMdLmT> 6919 3.89e+2 -5.10e4 102023. 102200. 101971.
13 Perimeter <tibble> <lmrMdLmT> 6919 3.60e+4 -8.22e4 164510. 164688. 164458.
14 Rightmost… <tibble> <lmrMdLmT> 6919 5.53e+2 -5.34e4 106935. 107113. 106883.
15 Rightmost… <tibble> <lmrMdLmT> 6919 1.74e+3 -6.13e4 122737. 122915. 122685.
16 Roundness <tibble> <lmrMdLmT> 6919 6.64e-3 2.47e4 -49333. -49155. -49385.
17 Width <tibble> <lmrMdLmT> 6919 7.50e+2 -5.55e4 111126. 111304. 111074.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [6,919 × 17]>
2 Centroid.X <tibble [6,919 × 17]>
3 Centroid.Y <tibble [6,919 × 17]>
4 Concavity <tibble [6,919 × 17]>
5 Convex.Area <tibble [6,919 × 17]>
6 Convex.Perimeter <tibble [6,919 × 17]>
7 Eccentricity <tibble [6,919 × 17]>
8 Elasticity <tibble [6,919 × 17]>
9 Elongation <tibble [6,919 × 17]>
10 Height <tibble [6,919 × 17]>
11 Major.Axis.Length <tibble [6,919 × 17]>
12 Minor.Axis.Length <tibble [6,919 × 17]>
13 Perimeter <tibble [6,919 × 17]>
14 Rightmost.X <tibble [6,919 × 17]>
15 Rightmost.Y <tibble [6,919 × 17]>
16 Roundness <tibble [6,919 × 17]>
17 Width <tibble [6,919 × 17]>
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 18030944. 4841684. 3.72
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 22787806. 4835368. 4.71
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 24080677. 4832302. 4.98
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 24114989. 4836076. 4.99
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 25864423. 4844098. 5.34
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 28754669. 4840584. 5.94
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 24357682. 4830408. 5.04
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 23201033. 4823844. 4.81
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 29576149. 4818136. 6.14
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 30668516. 4789157. 6.40
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.82e+7 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 2.52e+7 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.10e+2 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.62e+2 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.40e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 7.69e+1 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 1.34e-3 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.58e-3 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.29e+7 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.26e+7 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 3469 2.52e+7 -63698. 127448. 127608. 127396.
2 Centroid.X <tibble> <lmrMdLmT> 3469 5.62e+2 -26788. 53629. 53788. 53577.
3 Centroid.Y <tibble> <lmrMdLmT> 3469 7.69e+1 -19937. 39926. 40086. 39874.
4 Concavity <tibble> <lmrMdLmT> 3469 1.58e-3 17228. -34404. -34244. -34456.
5 Convex.Ar… <tibble> <lmrMdLmT> 3469 5.26e+7 -66211. 132474. 132634. 132422.
6 Convex.Pe… <tibble> <lmrMdLmT> 3469 3.26e+3 -32856. 65763. 65923. 65711.
7 Eccentric… <tibble> <lmrMdLmT> 3469 1.85e-3 16692. -33333. -33173. -33385.
8 Elasticity <tibble> <lmrMdLmT> 3469 1.98e-2 8522. -16993. -16833. -17045.
9 Elongation <tibble> <lmrMdLmT> 3469 3.43e-2 6649. -13246. -13086. -13298.
10 Height <tibble> <lmrMdLmT> 3469 4.21e+2 -25778. 51608. 51768. 51556.
11 Major.Axi… <tibble> <lmrMdLmT> 3469 7.23e+2 -27670. 55393. 55553. 55341.
12 Minor.Axi… <tibble> <lmrMdLmT> 3469 4.21e+2 -25778. 51607. 51767. 51555.
13 Perimeter <tibble> <lmrMdLmT> 3469 3.81e+4 -41314. 82680. 82840. 82628.
14 Rightmost… <tibble> <lmrMdLmT> 3469 5.35e+2 -26630. 53312. 53472. 53260.
15 Rightmost… <tibble> <lmrMdLmT> 3469 1.74e+3 -30675. 61402. 61562. 61350.
16 Roundness <tibble> <lmrMdLmT> 3469 6.75e-3 12244. -24436. -24276. -24488.
17 Width <tibble> <lmrMdLmT> 3469 7.25e+2 -27677. 55407. 55567. 55355.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [3,469 × 17]>
2 Centroid.X <tibble [3,469 × 17]>
3 Centroid.Y <tibble [3,469 × 17]>
4 Concavity <tibble [3,469 × 17]>
5 Convex.Area <tibble [3,469 × 17]>
6 Convex.Perimeter <tibble [3,469 × 17]>
7 Eccentricity <tibble [3,469 × 17]>
8 Elasticity <tibble [3,469 × 17]>
9 Elongation <tibble [3,469 × 17]>
10 Height <tibble [3,469 × 17]>
11 Major.Axis.Length <tibble [3,469 × 17]>
12 Minor.Axis.Length <tibble [3,469 × 17]>
13 Perimeter <tibble [3,469 × 17]>
14 Rightmost.X <tibble [3,469 × 17]>
15 Rightmost.Y <tibble [3,469 × 17]>
16 Roundness <tibble [3,469 × 17]>
17 Width <tibble [3,469 × 17]>
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 18824955. 4709510. 4.00
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 25823286. 4694009. 5.50
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 27398840. 4694009. 5.84
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 25959099. 4694009. 5.53
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 28553864. 4713359. 6.06
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 31403593. 4704725. 6.67
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 26551568. 4687975. 5.66
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 24201810. 4679895. 5.17
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 31185796. 4674749. 6.67
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 31583112. 4621211. 6.83
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.61e+7 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 2.61e+7 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.69e+2 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.50e+2 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.04e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 8.18e+1 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 1.37e-3 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.60e-3 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.21e+7 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.42e+7 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 2319 2.61e+7 -42534. 85120. 85269. 85068.
2 Centroid.X <tibble> <lmrMdLmT> 2319 5.50e+2 -17823. 35698. 35847. 35646.
3 Centroid.Y <tibble> <lmrMdLmT> 2319 8.18e+1 -13441. 26934. 27084. 26882.
4 Concavity <tibble> <lmrMdLmT> 2319 1.60e-3 11430. -22808. -22658. -22860.
5 Convex.Ar… <tibble> <lmrMdLmT> 2319 5.42e+7 -44197. 88446. 88596. 88394.
6 Convex.Pe… <tibble> <lmrMdLmT> 2319 3.28e+3 -21923. 43898. 44048. 43846.
7 Eccentric… <tibble> <lmrMdLmT> 2319 1.92e-3 11009. -21966. -21816. -22018.
8 Elasticity <tibble> <lmrMdLmT> 2319 1.90e-2 5749. -11447. -11297. -11499.
9 Elongation <tibble> <lmrMdLmT> 2319 3.48e-2 4370. -8688. -8538. -8740.
10 Height <tibble> <lmrMdLmT> 2319 4.31e+2 -17250. 34551. 34701. 34499.
11 Major.Axi… <tibble> <lmrMdLmT> 2319 7.08e+2 -18411. 36874. 37024. 36822.
12 Minor.Axi… <tibble> <lmrMdLmT> 2319 4.31e+2 -17250. 34551. 34701. 34499.
13 Perimeter <tibble> <lmrMdLmT> 2319 3.71e+4 -27489. 55031. 55180. 54979.
14 Rightmost… <tibble> <lmrMdLmT> 2319 5.44e+2 -17805. 35663. 35812. 35611.
15 Rightmost… <tibble> <lmrMdLmT> 2319 1.68e+3 -20381. 40815. 40964. 40763.
16 Roundness <tibble> <lmrMdLmT> 2319 6.52e-3 8214. -16376. -16226. -16428.
17 Width <tibble> <lmrMdLmT> 2319 7.10e+2 -18418. 36887. 37037. 36835.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [2,319 × 17]>
2 Centroid.X <tibble [2,319 × 17]>
3 Centroid.Y <tibble [2,319 × 17]>
4 Concavity <tibble [2,319 × 17]>
5 Convex.Area <tibble [2,319 × 17]>
6 Convex.Perimeter <tibble [2,319 × 17]>
7 Eccentricity <tibble [2,319 × 17]>
8 Elasticity <tibble [2,319 × 17]>
9 Elongation <tibble [2,319 × 17]>
10 Height <tibble [2,319 × 17]>
11 Major.Axis.Length <tibble [2,319 × 17]>
12 Minor.Axis.Length <tibble [2,319 × 17]>
13 Perimeter <tibble [2,319 × 17]>
14 Rightmost.X <tibble [2,319 × 17]>
15 Rightmost.Y <tibble [2,319 × 17]>
16 Roundness <tibble [2,319 × 17]>
17 Width <tibble [2,319 × 17]>
$fixed
# A tibble: 408 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> fixed <NA> hour0 22944641. 5630244. 4.08
2 Area <tibble> <lmrMdLmT> fixed <NA> hour1 29783917. 5600802. 5.32
3 Area <tibble> <lmrMdLmT> fixed <NA> hour2 33990628. 5600802. 6.07
4 Area <tibble> <lmrMdLmT> fixed <NA> hour3 32458138. 5600802. 5.80
5 Area <tibble> <lmrMdLmT> fixed <NA> hour4 35137151. 5637189. 6.23
6 Area <tibble> <lmrMdLmT> fixed <NA> hour5 41245301. 5605144. 7.36
7 Area <tibble> <lmrMdLmT> fixed <NA> hour6 28685729. 5573720. 5.15
8 Area <tibble> <lmrMdLmT> fixed <NA> hour7 29070947. 5573720. 5.22
9 Area <tibble> <lmrMdLmT> fixed <NA> hour8 33589645. 5502916. 6.10
10 Area <tibble> <lmrMdLmT> fixed <NA> hour9 36801718. 5452247. 6.75
# ℹ 398 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$random
# A tibble: 34 × 11
feature data model_fit effect group term estimate std.error statistic
<fct> <list> <list> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> ran_p… sow sd__… 1.60e+7 NA NA
2 Area <tibble> <lmrMdLmT> ran_p… Resi… sd__… 2.78e+7 NA NA
3 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 3.79e+2 NA NA
4 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.10e+2 NA NA
5 Centroid… <tibble> <lmrMdLmT> ran_p… sow sd__… 4.15e+1 NA NA
6 Centroid… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 8.91e+1 NA NA
7 Concavity <tibble> <lmrMdLmT> ran_p… sow sd__… 1.15e-3 NA NA
8 Concavity <tibble> <lmrMdLmT> ran_p… Resi… sd__… 1.53e-3 NA NA
9 Convex.A… <tibble> <lmrMdLmT> ran_p… sow sd__… 1.49e+7 NA NA
10 Convex.A… <tibble> <lmrMdLmT> ran_p… Resi… sd__… 5.59e+7 NA NA
# ℹ 24 more rows
# ℹ 2 more variables: df <dbl>, p.value <dbl>
$model_summaries
# A tibble: 17 × 10
feature data model_fit nobs sigma logLik AIC BIC REMLcrit
<fct> <list> <list> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Area <tibble> <lmrMdLmT> 1170 2.78e+7 -21339. 42730. 42862. 42678.
2 Centroid.X <tibble> <lmrMdLmT> 1170 5.10e+2 -8844. 17740. 17872. 17688.
3 Centroid.Y <tibble> <lmrMdLmT> 1170 8.91e+1 -6838. 13729. 13860. 13677.
4 Concavity <tibble> <lmrMdLmT> 1170 1.53e-3 5726. -11399. -11267. -11451.
5 Convex.Ar… <tibble> <lmrMdLmT> 1170 5.59e+7 -22128. 44308. 44440. 44256.
6 Convex.Pe… <tibble> <lmrMdLmT> 1170 3.26e+3 -10971. 21995. 22127. 21943.
7 Eccentric… <tibble> <lmrMdLmT> 1170 2.06e-3 5389. -10727. -10595. -10779.
8 Elasticity <tibble> <lmrMdLmT> 1170 1.92e-2 2825. -5599. -5467. -5651.
9 Elongation <tibble> <lmrMdLmT> 1170 3.46e-2 2162. -4271. -4140. -4323.
10 Height <tibble> <lmrMdLmT> 1170 4.32e+2 -8642. 17336. 17468. 17284.
11 Major.Axi… <tibble> <lmrMdLmT> 1170 6.61e+2 -9150. 18352. 18484. 18300.
12 Minor.Axi… <tibble> <lmrMdLmT> 1170 4.32e+2 -8642. 17336. 17468. 17284.
13 Perimeter <tibble> <lmrMdLmT> 1170 3.82e+4 -13791. 27633. 27765. 27581.
14 Rightmost… <tibble> <lmrMdLmT> 1170 5.13e+2 -8860. 17771. 17903. 17719.
15 Rightmost… <tibble> <lmrMdLmT> 1170 1.63e+3 -10177. 20406. 20537. 20354.
16 Roundness <tibble> <lmrMdLmT> 1170 6.06e-3 4159. -8267. -8135. -8319.
17 Width <tibble> <lmrMdLmT> 1170 6.63e+2 -9154. 18360. 18491. 18308.
# ℹ 1 more variable: df.residual <int>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [1,170 × 17]>
2 Centroid.X <tibble [1,170 × 17]>
3 Centroid.Y <tibble [1,170 × 17]>
4 Concavity <tibble [1,170 × 17]>
5 Convex.Area <tibble [1,170 × 17]>
6 Convex.Perimeter <tibble [1,170 × 17]>
7 Eccentricity <tibble [1,170 × 17]>
8 Elasticity <tibble [1,170 × 17]>
9 Elongation <tibble [1,170 × 17]>
10 Height <tibble [1,170 × 17]>
11 Major.Axis.Length <tibble [1,170 × 17]>
12 Minor.Axis.Length <tibble [1,170 × 17]>
13 Perimeter <tibble [1,170 × 17]>
14 Rightmost.X <tibble [1,170 × 17]>
15 Rightmost.Y <tibble [1,170 × 17]>
16 Roundness <tibble [1,170 × 17]>
17 Width <tibble [1,170 × 17]>