# 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>
aug_resid <- aug_res_60$.resid[16]
aug_resid
y <- aug_res_60$mean_value[16]
est <- aug_res_60$.fitted[16]
obs_resid <- y-est
obs_resid
Loading required package: lmerTest
Warning: package 'lmerTest' was built under R version 4.4.3
Loading required package: lme4
Warning: package 'lme4' was built under R version 4.4.3
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
# 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>
$augment
# A tibble: 17 × 2
feature aug
<fct> <list>
1 Area <tibble [1,170 × 17]>
2 Centroid.X <tibble [1,170 × 17]>
3 Centroid.Y <tibble [1,170 × 17]>
4 Concavity <tibble [1,170 × 17]>
5 Convex.Area <tibble [1,170 × 17]>
6 Convex.Perimeter <tibble [1,170 × 17]>
7 Eccentricity <tibble [1,170 × 17]>
8 Elasticity <tibble [1,170 × 17]>
9 Elongation <tibble [1,170 × 17]>
10 Height <tibble [1,170 × 17]>
11 Major.Axis.Length <tibble [1,170 × 17]>
12 Minor.Axis.Length <tibble [1,170 × 17]>
13 Perimeter <tibble [1,170 × 17]>
14 Rightmost.X <tibble [1,170 × 17]>
15 Rightmost.Y <tibble [1,170 × 17]>
16 Roundness <tibble [1,170 × 17]>
17 Width <tibble [1,170 × 17]>
df_60_min_vars$fixed$estimate[1]