tests/testthat/_snaps/vascr_anova.md
In JamesHucklesby/vascr: Process Biological Impedance Sensing Data
Can make a significance table
Code
vascr_make_significance_table(growth.df, 50, "R", 4000, 0.95)
Output
# A tibble: 8 x 2
Sample Label
<chr> <chr>
1 0_cells x HCMEC D3_line "15,000_cells x HCMEC D3_line **\n20,000_cells x~
2 10,000_cells x HCMEC D3_line "25,000_cells x HCMEC D3_line **\n30,000_cells x~
3 15,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line **\n30,000_cells x HCME~
4 20,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ***\n30,000_cells x HCM~
5 25,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ****\n10,000_cells x HC~
6 30,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ****\n10,000_cells x HC~
7 35,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ****\n10,000_cells x HC~
8 5,000_cells x HCMEC D3_line "20,000_cells x HCMEC D3_line *\n25,000_cells x ~
Code
vascr_make_significance_table(growth.df, 50, "R", 4000, 0.95, format = "Tukey_data")
Output
# A tibble: 31 x 9
term group1 group2 null.value estimate conf.low conf.high p.adj
* <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Experiment 1 : Experim~ 2 : E~ 0 -24.8 -79.3 29.8 4.79e-1
2 Experiment 1 : Experim~ 3 : E~ 0 -91.8 -146. -37.3 1.61e-3
3 Experiment 2 : Experim~ 3 : E~ 0 -67.1 -122. -12.6 1.59e-2
4 Sample 0_cells x H~ 10,00~ 0 109. -11.1 229. 8.84e-2
5 Sample 0_cells x H~ 15,00~ 0 158. 37.9 278. 6.78e-3
6 Sample 0_cells x H~ 20,00~ 0 202. 82.2 322. 7 e-4
7 Sample 0_cells x H~ 25,00~ 0 268. 148. 388. 3.4 e-5
8 Sample 0_cells x H~ 30,00~ 0 344. 224. 464. 1.76e-6
9 Sample 0_cells x H~ 35,00~ 0 424. 304. 544. 1.25e-7
10 Sample 0_cells x H~ 5,000~ 0 54.9 -65.1 175. 7.35e-1
# i 21 more rows
# i 1 more variable: p.adj.signif <chr>
Vascr LM
Code
vascr_lm(growth.df, "R", 4000, 100)
Output
Call:
lm(formula = formula, data = data.df)
Coefficients:
(Intercept) Experiment2 : Experiment2
302.97 -81.22
Experiment3 : Experiment3 Sample10,000_cells + HCMEC D3_line
-123.63 318.87
Sample15,000_cells + HCMEC D3_line Sample20,000_cells + HCMEC D3_line
366.04 365.43
Sample25,000_cells + HCMEC D3_line Sample30,000_cells + HCMEC D3_line
357.85 349.05
Sample35,000_cells + HCMEC D3_line Sample5,000_cells + HCMEC D3_line
320.43 140.83
Vascr_residuals
Code
vascr_residuals(growth.df, "R", "4000", 100)
Output
1 2 3 4 5 6
-71.2600254 15.1556080 56.1044174 43.5198942 0.2053120 -43.7252062
7 8 9 10 11 12
41.2513697 -11.8259343 -29.4254354 29.3639323 -31.1078063 1.7438740
13 14 15 16 17 18
17.5040807 -16.5831750 -0.9209057 3.8446770 -4.2829199 0.4382429
19 20 21 22 23 24
-21.3740220 -1.3609699 22.7349919 -42.8499065 49.7998853 -6.9499789
Vascr Shapiro test checks
Code
vascr_shapiro(growth.df, "R", 4000, 100)
Output
Shapiro-Wilk normality test
data: aov_residuals
W = 0.97874, p-value = 0.8716
Levene test
Code
vascr_levene(growth.df, "R", 4000, 100)
Output
# A tibble: 1 x 4
df1 df2 statistic p
<int> <int> <dbl> <dbl>
1 7 16 0.484 0.832
Tukey tests
Code
vascr_tukey(growth.df, "R", 4000, 100)
Output
# A tibble: 31 x 9
term group1 group2 null.value estimate conf.low conf.high p.adj
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Experiment 1 : Experim~ 2 : E~ 0 -81.2 -134. -28.5 3.33e-3
2 Experiment 1 : Experim~ 3 : E~ 0 -124. -176. -70.9 7.24e-5
3 Experiment 2 : Experim~ 3 : E~ 0 -42.4 -95.2 10.4 1.25e-1
4 Sample 0_cells x H~ 10,00~ 0 319. 203. 435. 2.99e-6
5 Sample 0_cells x H~ 15,00~ 0 366. 250. 482. 5.41e-7
6 Sample 0_cells x H~ 20,00~ 0 365. 249. 482. 5.53e-7
7 Sample 0_cells x H~ 25,00~ 0 358. 242. 474. 7.19e-7
8 Sample 0_cells x H~ 30,00~ 0 349. 233. 465. 9.8 e-7
9 Sample 0_cells x H~ 35,00~ 0 320. 204. 437. 2.81e-6
10 Sample 0_cells x H~ 5,000~ 0 141. 24.7 257. 1.3 e-2
# i 21 more rows
# i 1 more variable: p.adj.signif <chr>
Code
vascr_tukey(growth.df, "R", 4000, 100, raw = TRUE)
Output
# A tibble: 31 x 9
term group1 group2 null.value estimate conf.low conf.high p.adj
* <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Experiment 1 : Experim~ 2 : E~ 0 -81.2 -134. -28.5 3.33e-3
2 Experiment 1 : Experim~ 3 : E~ 0 -124. -176. -70.9 7.24e-5
3 Experiment 2 : Experim~ 3 : E~ 0 -42.4 -95.2 10.4 1.25e-1
4 Sample 0_cells x H~ 10,00~ 0 319. 203. 435. 2.99e-6
5 Sample 0_cells x H~ 15,00~ 0 366. 250. 482. 5.41e-7
6 Sample 0_cells x H~ 20,00~ 0 365. 249. 482. 5.53e-7
7 Sample 0_cells x H~ 25,00~ 0 358. 242. 474. 7.19e-7
8 Sample 0_cells x H~ 30,00~ 0 349. 233. 465. 9.8 e-7
9 Sample 0_cells x H~ 35,00~ 0 320. 204. 437. 2.81e-6
10 Sample 0_cells x H~ 5,000~ 0 141. 24.7 257. 1.3 e-2
# i 21 more rows
# i 1 more variable: p.adj.signif <chr>
Dunnett test works
Code
vascr_dunnett(growth.df, "R", 4000, 50, 8)
Output
# A tibble: 7 x 17
Time Unit Frequency Sample Instrument sd totaln n min max Well
<dbl> <fct> <dbl> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <chr>
1 50 R 4000 10,000_~ ECIS 27.5 9 3 321. 376. F01,~
2 50 R 4000 15,000_~ ECIS 42.4 9 3 350. 432. E01,~
3 50 R 4000 20,000_~ ECIS 52.5 9 3 394. 498. D01,~
4 50 R 4000 25,000_~ ECIS 81.0 9 3 428. 590. C01,~
5 50 R 4000 30,000_~ ECIS 108. 9 3 464. 675. B01,~
6 50 R 4000 35,000_~ ECIS 79.0 9 3 574. 721. A01,~
7 50 R 4000 5,000_c~ ECIS 20.4 9 3 281. 318. G01,~
# i 6 more variables: Value <dbl>, Experiment <chr>, sem <dbl>, P <dbl>,
# Label <chr>, P_round <chr>
Code
vascr_dunnett(growth.df, "R", 4000, list(50, 100), 8)
Output
# A tibble: 14 x 17
Time Unit Frequency Sample Instrument sd totaln n min max Well
<dbl> <fct> <dbl> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <chr>
1 50 R 4000 10,000~ ECIS 27.5 9 3 321. 376. F01,~
2 50 R 4000 15,000~ ECIS 42.4 9 3 350. 432. E01,~
3 50 R 4000 20,000~ ECIS 52.5 9 3 394. 498. D01,~
4 50 R 4000 25,000~ ECIS 81.0 9 3 428. 590. C01,~
5 50 R 4000 30,000~ ECIS 108. 9 3 464. 675. B01,~
6 50 R 4000 35,000~ ECIS 79.0 9 3 574. 721. A01,~
7 50 R 4000 5,000_~ ECIS 20.4 9 3 281. 318. G01,~
8 100 R 4000 10,000~ ECIS 106. 9 3 454. 665. F01,~
9 100 R 4000 15,000~ ECIS 99.5 9 3 516. 710. E01,~
10 100 R 4000 20,000~ ECIS 84.7 9 3 547. 698. D01,~
11 100 R 4000 25,000~ ECIS 75.5 9 3 536. 678. C01,~
12 100 R 4000 30,000~ ECIS 65.2 9 3 529. 656. B01,~
13 100 R 4000 35,000~ ECIS 41.6 9 3 523. 602. A01,~
14 100 R 4000 5,000_~ ECIS 54.3 9 3 313. 412. G01,~
# i 6 more variables: Value <dbl>, Experiment <chr>, sem <dbl>, P <dbl>,
# Label <chr>, P_round <chr>
Code
vascr_dunnett(growth.df, "R", 4000, 50, "0_cells + HCMEC D3_line")
Output
# A tibble: 7 x 17
Time Unit Frequency Sample Instrument sd totaln n min max Well
<dbl> <fct> <dbl> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <chr>
1 50 R 4000 10,000_~ ECIS 27.5 9 3 321. 376. F01,~
2 50 R 4000 15,000_~ ECIS 42.4 9 3 350. 432. E01,~
3 50 R 4000 20,000_~ ECIS 52.5 9 3 394. 498. D01,~
4 50 R 4000 25,000_~ ECIS 81.0 9 3 428. 590. C01,~
5 50 R 4000 30,000_~ ECIS 108. 9 3 464. 675. B01,~
6 50 R 4000 35,000_~ ECIS 79.0 9 3 574. 721. A01,~
7 50 R 4000 5,000_c~ ECIS 20.4 9 3 281. 318. G01,~
# i 6 more variables: Value <dbl>, Experiment <chr>, sem <dbl>, P <dbl>,
# Label <chr>, P_round <chr>
JamesHucklesby/vascr documentation built on July 16, 2025, 8:16 p.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Can make a significance table
Code
vascr_make_significance_table(growth.df, 50, "R", 4000, 0.95)
Output
# A tibble: 8 x 2
Sample Label
<chr> <chr>
1 0_cells x HCMEC D3_line "15,000_cells x HCMEC D3_line **\n20,000_cells x~
2 10,000_cells x HCMEC D3_line "25,000_cells x HCMEC D3_line **\n30,000_cells x~
3 15,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line **\n30,000_cells x HCME~
4 20,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ***\n30,000_cells x HCM~
5 25,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ****\n10,000_cells x HC~
6 30,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ****\n10,000_cells x HC~
7 35,000_cells x HCMEC D3_line "0_cells x HCMEC D3_line ****\n10,000_cells x HC~
8 5,000_cells x HCMEC D3_line "20,000_cells x HCMEC D3_line *\n25,000_cells x ~
Code
vascr_make_significance_table(growth.df, 50, "R", 4000, 0.95, format = "Tukey_data")
Output
# A tibble: 31 x 9
term group1 group2 null.value estimate conf.low conf.high p.adj
* <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Experiment 1 : Experim~ 2 : E~ 0 -24.8 -79.3 29.8 4.79e-1
2 Experiment 1 : Experim~ 3 : E~ 0 -91.8 -146. -37.3 1.61e-3
3 Experiment 2 : Experim~ 3 : E~ 0 -67.1 -122. -12.6 1.59e-2
4 Sample 0_cells x H~ 10,00~ 0 109. -11.1 229. 8.84e-2
5 Sample 0_cells x H~ 15,00~ 0 158. 37.9 278. 6.78e-3
6 Sample 0_cells x H~ 20,00~ 0 202. 82.2 322. 7 e-4
7 Sample 0_cells x H~ 25,00~ 0 268. 148. 388. 3.4 e-5
8 Sample 0_cells x H~ 30,00~ 0 344. 224. 464. 1.76e-6
9 Sample 0_cells x H~ 35,00~ 0 424. 304. 544. 1.25e-7
10 Sample 0_cells x H~ 5,000~ 0 54.9 -65.1 175. 7.35e-1
# i 21 more rows
# i 1 more variable: p.adj.signif <chr>
Vascr LM
Code
vascr_lm(growth.df, "R", 4000, 100)
Output
Call:
lm(formula = formula, data = data.df)
Coefficients:
(Intercept) Experiment2 : Experiment2
302.97 -81.22
Experiment3 : Experiment3 Sample10,000_cells + HCMEC D3_line
-123.63 318.87
Sample15,000_cells + HCMEC D3_line Sample20,000_cells + HCMEC D3_line
366.04 365.43
Sample25,000_cells + HCMEC D3_line Sample30,000_cells + HCMEC D3_line
357.85 349.05
Sample35,000_cells + HCMEC D3_line Sample5,000_cells + HCMEC D3_line
320.43 140.83
Vascr_residuals
Code
vascr_residuals(growth.df, "R", "4000", 100)
Output
1 2 3 4 5 6
-71.2600254 15.1556080 56.1044174 43.5198942 0.2053120 -43.7252062
7 8 9 10 11 12
41.2513697 -11.8259343 -29.4254354 29.3639323 -31.1078063 1.7438740
13 14 15 16 17 18
17.5040807 -16.5831750 -0.9209057 3.8446770 -4.2829199 0.4382429
19 20 21 22 23 24
-21.3740220 -1.3609699 22.7349919 -42.8499065 49.7998853 -6.9499789
Vascr Shapiro test checks
Code
vascr_shapiro(growth.df, "R", 4000, 100)
Output
Shapiro-Wilk normality test
data: aov_residuals
W = 0.97874, p-value = 0.8716
Levene test
Code
vascr_levene(growth.df, "R", 4000, 100)
Output
# A tibble: 1 x 4
df1 df2 statistic p
<int> <int> <dbl> <dbl>
1 7 16 0.484 0.832
Tukey tests
Code
vascr_tukey(growth.df, "R", 4000, 100)
Output
# A tibble: 31 x 9
term group1 group2 null.value estimate conf.low conf.high p.adj
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Experiment 1 : Experim~ 2 : E~ 0 -81.2 -134. -28.5 3.33e-3
2 Experiment 1 : Experim~ 3 : E~ 0 -124. -176. -70.9 7.24e-5
3 Experiment 2 : Experim~ 3 : E~ 0 -42.4 -95.2 10.4 1.25e-1
4 Sample 0_cells x H~ 10,00~ 0 319. 203. 435. 2.99e-6
5 Sample 0_cells x H~ 15,00~ 0 366. 250. 482. 5.41e-7
6 Sample 0_cells x H~ 20,00~ 0 365. 249. 482. 5.53e-7
7 Sample 0_cells x H~ 25,00~ 0 358. 242. 474. 7.19e-7
8 Sample 0_cells x H~ 30,00~ 0 349. 233. 465. 9.8 e-7
9 Sample 0_cells x H~ 35,00~ 0 320. 204. 437. 2.81e-6
10 Sample 0_cells x H~ 5,000~ 0 141. 24.7 257. 1.3 e-2
# i 21 more rows
# i 1 more variable: p.adj.signif <chr>
Code
vascr_tukey(growth.df, "R", 4000, 100, raw = TRUE)
Output
# A tibble: 31 x 9
term group1 group2 null.value estimate conf.low conf.high p.adj
* <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Experiment 1 : Experim~ 2 : E~ 0 -81.2 -134. -28.5 3.33e-3
2 Experiment 1 : Experim~ 3 : E~ 0 -124. -176. -70.9 7.24e-5
3 Experiment 2 : Experim~ 3 : E~ 0 -42.4 -95.2 10.4 1.25e-1
4 Sample 0_cells x H~ 10,00~ 0 319. 203. 435. 2.99e-6
5 Sample 0_cells x H~ 15,00~ 0 366. 250. 482. 5.41e-7
6 Sample 0_cells x H~ 20,00~ 0 365. 249. 482. 5.53e-7
7 Sample 0_cells x H~ 25,00~ 0 358. 242. 474. 7.19e-7
8 Sample 0_cells x H~ 30,00~ 0 349. 233. 465. 9.8 e-7
9 Sample 0_cells x H~ 35,00~ 0 320. 204. 437. 2.81e-6
10 Sample 0_cells x H~ 5,000~ 0 141. 24.7 257. 1.3 e-2
# i 21 more rows
# i 1 more variable: p.adj.signif <chr>
Dunnett test works
Code
vascr_dunnett(growth.df, "R", 4000, 50, 8)
Output
# A tibble: 7 x 17
Time Unit Frequency Sample Instrument sd totaln n min max Well
<dbl> <fct> <dbl> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <chr>
1 50 R 4000 10,000_~ ECIS 27.5 9 3 321. 376. F01,~
2 50 R 4000 15,000_~ ECIS 42.4 9 3 350. 432. E01,~
3 50 R 4000 20,000_~ ECIS 52.5 9 3 394. 498. D01,~
4 50 R 4000 25,000_~ ECIS 81.0 9 3 428. 590. C01,~
5 50 R 4000 30,000_~ ECIS 108. 9 3 464. 675. B01,~
6 50 R 4000 35,000_~ ECIS 79.0 9 3 574. 721. A01,~
7 50 R 4000 5,000_c~ ECIS 20.4 9 3 281. 318. G01,~
# i 6 more variables: Value <dbl>, Experiment <chr>, sem <dbl>, P <dbl>,
# Label <chr>, P_round <chr>
Code
vascr_dunnett(growth.df, "R", 4000, list(50, 100), 8)
Output
# A tibble: 14 x 17
Time Unit Frequency Sample Instrument sd totaln n min max Well
<dbl> <fct> <dbl> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <chr>
1 50 R 4000 10,000~ ECIS 27.5 9 3 321. 376. F01,~
2 50 R 4000 15,000~ ECIS 42.4 9 3 350. 432. E01,~
3 50 R 4000 20,000~ ECIS 52.5 9 3 394. 498. D01,~
4 50 R 4000 25,000~ ECIS 81.0 9 3 428. 590. C01,~
5 50 R 4000 30,000~ ECIS 108. 9 3 464. 675. B01,~
6 50 R 4000 35,000~ ECIS 79.0 9 3 574. 721. A01,~
7 50 R 4000 5,000_~ ECIS 20.4 9 3 281. 318. G01,~
8 100 R 4000 10,000~ ECIS 106. 9 3 454. 665. F01,~
9 100 R 4000 15,000~ ECIS 99.5 9 3 516. 710. E01,~
10 100 R 4000 20,000~ ECIS 84.7 9 3 547. 698. D01,~
11 100 R 4000 25,000~ ECIS 75.5 9 3 536. 678. C01,~
12 100 R 4000 30,000~ ECIS 65.2 9 3 529. 656. B01,~
13 100 R 4000 35,000~ ECIS 41.6 9 3 523. 602. A01,~
14 100 R 4000 5,000_~ ECIS 54.3 9 3 313. 412. G01,~
# i 6 more variables: Value <dbl>, Experiment <chr>, sem <dbl>, P <dbl>,
# Label <chr>, P_round <chr>
Code
vascr_dunnett(growth.df, "R", 4000, 50, "0_cells + HCMEC D3_line")
Output
# A tibble: 7 x 17
Time Unit Frequency Sample Instrument sd totaln n min max Well
<dbl> <fct> <dbl> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <chr>
1 50 R 4000 10,000_~ ECIS 27.5 9 3 321. 376. F01,~
2 50 R 4000 15,000_~ ECIS 42.4 9 3 350. 432. E01,~
3 50 R 4000 20,000_~ ECIS 52.5 9 3 394. 498. D01,~
4 50 R 4000 25,000_~ ECIS 81.0 9 3 428. 590. C01,~
5 50 R 4000 30,000_~ ECIS 108. 9 3 464. 675. B01,~
6 50 R 4000 35,000_~ ECIS 79.0 9 3 574. 721. A01,~
7 50 R 4000 5,000_c~ ECIS 20.4 9 3 281. 318. G01,~
# i 6 more variables: Value <dbl>, Experiment <chr>, sem <dbl>, P <dbl>,
# Label <chr>, P_round <chr>
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