# A tibble: 36 × 4
`Plant Parts` TAA TPC TFC
<chr> <dbl> <dbl> <dbl>
1 Leaves 1365. 125. 466.
2 Leaves 1371. 121. 467.
3 Leaves 1426. 117. 471.
4 Leaves 1529. 128. 494.
5 Leaves 1404. 123. 474.
6 Leaves 1450. 120. 482.
7 Leaves 1402. 124. 505.
8 Leaves 1400. 119. 486.
9 Leaves 1426. 124. 476.
10 Rhizomes 1400. 1367. 144.
# … with 26 more rows
# ℹ Use `print(n = ...)` to see more rows
Descriptive Statistics on TAA
Attaching package: 'dplyr'
The following object is masked from 'package:gridExtra':
combine
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# A tibble: 4 × 4
`Plant Parts` count mean sd
<chr> <int> <dbl> <dbl>
1 Leaves 9 1419. 49.1
2 Pericarp 9 100. 2.59
3 Rhizomes 9 1341. 67.1
4 Seeds 9 4109. 237.
Descriptive Statistics on TPC
# A tibble: 4 × 4
`Plant Parts` count mean sd
<chr> <int> <dbl> <dbl>
1 Leaves 9 122. 49.1
2 Pericarp 9 339. 2.59
3 Rhizomes 9 1343. 67.1
4 Seeds 9 15038. 237.
Descriptive Statistics on TAA
# A tibble: 4 × 4
`Plant Parts` count mean sd
<chr> <int> <dbl> <dbl>
1 Leaves 9 480. 49.1
2 Pericarp 9 14.9 2.59
3 Rhizomes 9 145. 67.1
4 Seeds 9 217. 237.
Significant Difference Among Plant Parts based on TAA
Df Sum Sq Mean Sq F value Pr(>F)
Barriga$`Plant Parts` 3 77061769 25687256 1628 <2e-16 ***
Residuals 32 504984 15781
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post Hoc Test
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Barriga$TAA ~ Barriga$`Plant Parts`)
$`Barriga$`Plant Parts``
diff lwr upr p adj
Pericarp-Leaves -1318.87640 -1479.3207 -1158.43208 0.0000000
Rhizomes-Leaves -78.52684 -238.9712 81.91748 0.5536158
Seeds-Leaves 2689.66599 2529.2217 2850.11031 0.0000000
Rhizomes-Pericarp 1240.34956 1079.9052 1400.79388 0.0000000
Seeds-Pericarp 4008.54239 3848.0981 4168.98671 0.0000000
Seeds-Rhizomes 2768.19283 2607.7485 2928.63715 0.0000000
Significant Difference Among Plant Parts based on TPC
Df Sum Sq Mean Sq F value Pr(>F)
Barriga$`Plant Parts` 3 1.414e+09 471493445 21363 <2e-16 ***
Residuals 32 7.063e+05 22071
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post Hoc Test
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Barriga$TPC ~ Barriga$`Plant Parts`)
$`Barriga$`Plant Parts``
diff lwr upr p adj
Pericarp-Leaves 217.0526 27.30819 406.797 0.0199631
Rhizomes-Leaves 1220.2242 1030.47983 1409.969 0.0000000
Seeds-Leaves 14915.9438 14726.19937 15105.688 0.0000000
Rhizomes-Pericarp 1003.1716 813.42725 1192.916 0.0000000
Seeds-Pericarp 14698.8912 14509.14679 14888.636 0.0000000
Seeds-Rhizomes 13695.7195 13505.97515 13885.464 0.0000000
Significant Difference Among Plant Parts based on TFC
Df Sum Sq Mean Sq F value Pr(>F)
Barriga$`Plant Parts` 3 1037450 345817 5319 <2e-16 ***
Residuals 32 2081 65
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Post Hoc Test
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Barriga$TFC ~ Barriga$`Plant Parts`)
$`Barriga$`Plant Parts``
diff lwr upr p adj
Pericarp-Leaves -465.37099 -475.66964 -455.0724 0
Rhizomes-Leaves -335.00208 -345.30072 -324.7034 0
Seeds-Leaves -263.51202 -273.81066 -253.2134 0
Rhizomes-Pericarp 130.36891 120.07027 140.6676 0
Seeds-Pericarp 201.85897 191.56033 212.1576 0
Seeds-Rhizomes 71.49006 61.19142 81.7887 0
Correlation between TAA and TPC
[1] 0.9356174
Correlation between TAA and TFC
[1] 0.2759627
Correlation between TPC and TFC
[1] -0.02135804
# A tibble: 36 × 4
`Plant Parts` TAA TPC TFC
<chr> <dbl> <dbl> <dbl>
1 Leaves 1365. 125. 466.
2 Leaves 1371. 121. 467.
3 Leaves 1426. 117. 471.
4 Leaves 1529. 128. 494.
5 Leaves 1404. 123. 474.
6 Leaves 1450. 120. 482.
7 Leaves 1402. 124. 505.
8 Leaves 1400. 119. 486.
9 Leaves 1426. 124. 476.
10 Rhizomes 1400. 1367. 144.
# … with 26 more rows
# ℹ Use `print(n = ...)` to see more rows
Correlation For Leaves, etc Correlation
For Leaves
# A tibble: 9 × 4
`Plant Parts` TAA TPC TFC
<chr> <dbl> <dbl> <dbl>
1 Leaves 1365. 125. 466.
2 Leaves 1371. 121. 467.
3 Leaves 1426. 117. 471.
4 Leaves 1529. 128. 494.
5 Leaves 1404. 123. 474.
6 Leaves 1450. 120. 482.
7 Leaves 1402. 124. 505.
8 Leaves 1400. 119. 486.
9 Leaves 1426. 124. 476.
Correlation between TAA and TPC
[1] 0.3491988
Correlation between TAA and TFC
[1] 0.480656
Correlation between TPC and TFC
[1] 0.3232221
For Rhizomes
# A tibble: 9 × 4
`Plant Parts` TAA TPC TFC
<chr> <dbl> <dbl> <dbl>
1 Rhizomes 1400. 1367. 144.
2 Rhizomes 1381. 1317. 138.
3 Rhizomes 1440. 1289. 138.
4 Rhizomes 1367. 1377. 149.
5 Rhizomes 1363. 1394. 147.
6 Rhizomes 1258. 1387. 150.
7 Rhizomes 1338. 1339. 157.
8 Rhizomes 1267. 1306. 145.
9 Rhizomes 1254. 1306. 140.
Correlation between TAA and TPC
[1] -0.02764104
Correlation between TAA and TFC
[1] -0.2879499
Correlation between TPC and TFC
[1] 0.5754173
For Seeds
# A tibble: 9 × 4
`Plant Parts` TAA TPC TFC
<chr> <dbl> <dbl> <dbl>
1 Seeds 3883. 15446. 221.
2 Seeds 3629. 15183. 215.
3 Seeds 4302. 15493. 208.
4 Seeds 4309. 15152. 219.
5 Seeds 4031. 14749. 213.
6 Seeds 4287. 14873. 231.
7 Seeds 4027. 14811. 213.
8 Seeds 4290. 14935. 211.
9 Seeds 4223. 14702. 219.
Correlation between TAA and TPC
[1] -0.1723348
Correlation between TAA and TFC
[1] 0.1015215
Correlation between TPC and TFC
[1] -0.1818465
For Pericarp
# A tibble: 9 × 4
`Plant Parts` TAA TPC TFC
<chr> <dbl> <dbl> <dbl>
1 Pericarp 102. 348. 13.0
2 Pericarp 95.4 327. 12.6
3 Pericarp 99.6 338. 12.6
4 Pericarp 102. 343. 14.8
5 Pericarp 100. 337. 16.0
6 Pericarp 101. 344. 17.1
7 Pericarp 97.6 351. 15.1
8 Pericarp 104. 329. 16.9
9 Pericarp 102. 336. 15.7
Correlation between TAA and TPC
[1] 0.1125879
Correlation between TAA and TFC
[1] 0.5376003
Correlation between TPC and TFC
[1] 0.02218679