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- [1] "speed"
- Component 1 :
- Df Sum Sq Mean Sq F value Pr(>F)
- data$group 3 763.19 254.396 25.949 4.404e-12 ***
- Residuals 87 852.92 9.804
- ---
- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
- Tukey multiple comparisons of means
- 95% family-wise confidence level
- Fit: aov(formula = data$median_speed ~ data$group)
- $`data$group`
- diff lwr upr p adj
- Control_actin2-Control_actin1 -6.6750135 -9.0935098 -4.256517 0.0000000
- Control_actin3 -Control_actin1 -4.6679652 -7.1137900 -2.222140 0.0000174
- Experimental_actin-Control_actin1 -0.1541438 -2.5726401 2.264353 0.9983325
- Control_actin3 -Control_actin2 2.0070483 -0.4387766 4.452873 0.1458660
- Experimental_actin-Control_actin2 6.5208697 4.1023734 8.939366 0.0000000
- Experimental_actin-Control_actin3 4.5138214 2.0679966 6.959646 0.0000335
- [1] "angledev"
- Pairwise comparisons using Wilcoxon rank sum exact test
- data: data$stripe_deviation and data$group
- Control_actin1 Control_actin2 Control_actin3
- Control_actin2 5.8e-12 - -
- Control_actin3 0.00016 9.5e-05 -
- Experimental_actin 0.01064 4.3e-05 1.00000
- P value adjustment method: bonferroni
- [1] "speed"
- Component 1 :
- Df Sum Sq Mean Sq F value Pr(>F)
- data$group 3 502.60 167.53 13.119 5.454e-07 ***
- Residuals 76 970.54 12.77
- ---
- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
- Tukey multiple comparisons of means
- 95% family-wise confidence level
- Fit: aov(formula = data$median_speed ~ data$group)
- $`data$group`
- diff lwr upr p adj
- Control2_nsyb-Control1_nsyb -3.1700277 -6.1384564 -0.2015991 0.0317212
- Control3_nsyb-Control1_nsyb -0.9424625 -3.9108912 2.0259661 0.8381294
- Experimental_nsyb-Control1_nsyb 3.7711940 0.8027653 6.7396226 0.0070397
- Control3_nsyb-Control2_nsyb 2.2275652 -0.7408635 5.1959938 0.2079812
- Experimental_nsyb-Control2_nsyb 6.9412217 3.9727930 9.9096503 0.0000002
- Experimental_nsyb-Control3_nsyb 4.7136565 1.7452278 7.6820851 0.0004552
- [1] "angledev"
- Pairwise comparisons using Wilcoxon rank sum exact test
- data: data$stripe_deviation and data$group
- Control1_nsyb Control2_nsyb Control3_nsyb
- Control2_nsyb 8.0e-07 - -
- Control3_nsyb 0.31 1.4e-05 -
- Experimental_nsyb 0.16 4.5e-06 1.00
- P value adjustment method: bonferroni
- [1] "speed"
- Component 1 :
- Df Sum Sq Mean Sq F value Pr(>F)
- data$group 3 430.67 143.558 9.6774 7.149e-06 ***
- Residuals 148 2195.48 14.834
- ---
- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
- Tukey multiple comparisons of means
- 95% family-wise confidence level
- Fit: aov(formula = data$median_speed ~ data$group)
- $`data$group`
- diff lwr upr p adj
- control2_tdc1-control1_tdc1 -4.7811273 -7.09754415 -2.4647104 0.0000018
- Control3_tdc1-control1_tdc1 -2.5504128 -4.91024689 -0.1905787 0.0285960
- Experimental_tdc1-control1_tdc1 -2.9170876 -5.23350450 -0.6006707 0.0071914
- Control3_tdc1-control2_tdc1 2.2307145 -0.05206843 4.5134974 0.0580936
- Experimental_tdc1-control2_tdc1 1.8640397 -0.37383166 4.1019110 0.1381025
- Experimental_tdc1-Control3_tdc1 -0.3666748 -2.64945772 1.9161081 0.9754320
- [1] "angledev"
- Pairwise comparisons using Wilcoxon rank sum exact test
- data: data$stripe_deviation and data$group
- control1_tdc1 control2_tdc1 Control3_tdc1
- control2_tdc1 9.0e-14 - -
- Control3_tdc1 0.0029 2.7e-09 -
- Experimental_tdc1 1.0000 3.0e-14 0.0123
- P value adjustment method: bonferroni
- [1] "speed"
- Component 1 :
- Df Sum Sq Mean Sq F value Pr(>F)
- data$group 3 151.26 50.421 6.4778 0.0006382 ***
- Residuals 68 529.29 7.784
- ---
- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
- Tukey multiple comparisons of means
- 95% family-wise confidence level
- Fit: aov(formula = data$median_speed ~ data$group)
- $`data$group`
- diff lwr upr p adj
- control2_tdc2-control1_tdc2 0.1060279 -2.2479530 2.460009 0.9993971
- control3_tdc2-control1_tdc2 -0.6010208 -3.3104377 2.108396 0.9365084
- Experimental_tdc2-control1_tdc2 3.0460583 0.7195427 5.372574 0.0052574
- control3_tdc2-control2_tdc2 -0.7070487 -3.3901173 1.976020 0.8990273
- Experimental_tdc2-control2_tdc2 2.9400304 0.6442534 5.235807 0.0066048
- Experimental_tdc2-control3_tdc2 3.6470791 0.9880743 6.306084 0.0031623
- [1] "angledev"
- Pairwise comparisons using Wilcoxon rank sum exact test
- data: data$stripe_deviation and data$group
- control1_tdc2 control2_tdc2 control3_tdc2
- control2_tdc2 3.3e-09 - -
- control3_tdc2 0.00109 0.00036 -
- Experimental_tdc2 1.7e-06 6.5e-07 1.00000
- P value adjustment method: bonferroni
- [1] "speed"
- Component 1 :
- Df Sum Sq Mean Sq F value Pr(>F)
- data$group 3 662.4 220.801 13.405 3.59e-07 ***
- Residuals 80 1317.7 16.471
- ---
- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
- Tukey multiple comparisons of means
- 95% family-wise confidence level
- Fit: aov(formula = data$median_speed ~ data$group)
- $`data$group`
- diff lwr upr p adj
- control2_np7088-control1_np7088 -6.3940148 -9.680326 -3.107704 0.0000129
- control3_np7088-control1_np7088 -5.9551877 -9.241499 -2.668877 0.0000505
- Experimental_np7088-control1_np7088 -1.2810092 -4.567320 2.005302 0.7366318
- control3_np7088-control2_np7088 0.4388272 -2.847484 3.725138 0.9851304
- Experimental_np7088-control2_np7088 5.1130056 1.826695 8.399316 0.0005978
- Experimental_np7088-control3_np7088 4.6741784 1.387868 7.960489 0.0019721
- [1] "angledev"
- Pairwise comparisons using Wilcoxon rank sum exact test
- data: data$stripe_deviation and data$group
- control1_np7088 control2_np7088 control3_np7088
- control2_np7088 8.9e-11 - -
- control3_np7088 0.4688 0.0015 -
- Experimental_np7088 1.0000 1.8e-05 1.0000
- P value adjustment method: bonferroni
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