Sal vs. IL Dommer wheel and motion
exp1DW <- read.csv("~/Desktop/experiment1stats2wayD.csv")
exp1DW
## Group.E1DWheel Day.E1DWheel Data.E1DWheel Group.E1DMotion Day.E1DMotion
## 1 Saline 1 81.57382 Saline 1
## 2 Saline 1 62.37675 Saline 1
## 3 Saline 1 20.70532 Saline 1
## 4 Saline 1 106.91492 Saline 1
## 5 Saline 1 56.68948 Saline 1
## 6 Saline 1 67.36369 Saline 1
## 7 Saline 1 87.97646 Saline 1
## 8 Saline 1 120.46230 Saline 1
## 9 Saline 1 50.85003 Saline 1
## 10 IL-1B 1 0.00000 IL-1B 2
## 11 IL-1B 1 0.01275 IL-1B 2
## 12 IL-1B 1 65.49307 IL-1B 2
## 13 IL-1B 1 14.11292 IL-1B 2
## 14 IL-1B 1 17.50888 IL-1B 2
## 15 IL-1B 1 20.83834 IL-1B 2
## 16 IL-1B 1 47.26633 IL-1B 2
## 17 IL-1B 1 65.77487 IL-1B 2
## 18 IL-1B 1 112.30327 IL-1B 2
## 19 Saline 2 58.73412 Saline 3
## 20 Saline 2 40.53128 Saline 3
## 21 Saline 2 12.06786 Saline 3
## 22 Saline 2 126.00050 Saline 3
## 23 Saline 2 69.02292 Saline 3
## 24 Saline 2 132.19242 Saline 3
## 25 Saline 2 64.11641 Saline 3
## 26 Saline 2 100.31302 Saline 3
## 27 Saline 2 105.68863 Saline 3
## 28 IL-1B 2 1.80399 IL-1B 1
## 29 IL-1B 2 0.21902 IL-1B 1
## 30 IL-1B 2 67.53658 IL-1B 1
## 31 IL-1B 2 17.48256 IL-1B 1
## 32 IL-1B 2 19.94245 IL-1B 1
## 33 IL-1B 2 15.89365 IL-1B 1
## 34 IL-1B 2 64.39357 IL-1B 1
## 35 IL-1B 2 4.16481 IL-1B 1
## 36 IL-1B 2 175.66857 IL-1B 1
## 37 Saline 3 60.66438 Saline 2
## 38 Saline 3 9.69562 Saline 2
## 39 Saline 3 9.12771 Saline 2
## 40 Saline 3 114.33789 Saline 2
## 41 Saline 3 44.04209 Saline 2
## 42 Saline 3 124.21084 Saline 2
## 43 Saline 3 86.36566 Saline 2
## 44 Saline 3 88.29643 Saline 2
## 45 Saline 3 99.35696 Saline 2
## 46 IL-1B 3 67.68583 IL-1B 3
## 47 IL-1B 3 7.22342 IL-1B 3
## 48 IL-1B 3 43.17195 IL-1B 3
## 49 IL-1B 3 107.38815 IL-1B 3
## 50 IL-1B 3 88.97880 IL-1B 3
## 51 IL-1B 3 74.05380 IL-1B 3
## 52 IL-1B 3 88.23440 IL-1B 3
## 53 IL-1B 3 68.63493 IL-1B 3
## 54 IL-1B 3 81.01714 IL-1B 3
## Data.E1DMotion
## 1 152.885
## 2 91.255
## 3 63.176
## 4 93.006
## 5 73.731
## 6 55.652
## 7 62.788
## 8 68.375
## 9 49.885
## 10 61.100
## 11 55.589
## 12 54.300
## 13 89.921
## 14 61.300
## 15 434.478
## 16 56.632
## 17 56.107
## 18 85.980
## 19 109.696
## 20 43.960
## 21 59.263
## 22 100.310
## 23 59.445
## 24 234.200
## 25 66.409
## 26 57.802
## 27 82.359
## 28 7.100
## 29 3.928
## 30 25.238
## 31 52.537
## 32 48.828
## 33 47.112
## 34 54.448
## 35 66.617
## 36 115.921
## 37 27.657
## 38 10.087
## 39 17.928
## 40 74.940
## 41 62.155
## 42 88.453
## 43 69.472
## 44 7.647
## 45 100.466
## 46 65.138
## 47 33.508
## 48 44.693
## 49 134.440
## 50 112.886
## 51 220.760
## 52 93.310
## 53 84.714
## 54 104.221
tapply(exp1DW$Data.E1DWheel, exp1DW$Group.E1DWheel, sd)
## IL-1B Saline
## 43.52 36.70
tapply(exp1DW$Data.E1DWheel, exp1DW$Day.E1DWheel, sd)
## 1 2 3
## 37.60 51.42 35.45
exp1DW$Group.E1DWheel <- as.factor(exp1DW$Group.E1DWheel)
exp1DW$Day.E1DWheel <- as.factor(exp1DW$Day.E1DWheel)
exp1DW$Data.E1DWheel <- as.numeric(exp1DW$Data.E1DWheel)
aov.exp1DW = aov(Data.E1DWheel ~ Group.E1DWheel * Day.E1DWheel, data = exp1DW)
summary(aov.exp1DW)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.E1DWheel 1 8137 8137 4.98 0.03 *
## Day.E1DWheel 2 2050 1025 0.63 0.54
## Group.E1DWheel:Day.E1DWheel 2 3744 1872 1.15 0.33
## Residuals 48 78471 1635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison <- aov(Data.E1DWheel ~ Group.E1DWheel * Day.E1DWheel, data = exp1DW)
TukeyHSD(comparison, "Group.E1DWheel")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1DWheel ~ Group.E1DWheel * Day.E1DWheel, data = exp1DW)
##
## $Group.E1DWheel
## diff lwr upr p adj
## Saline-IL-1B 24.55 2.425 46.68 0.0304
TukeyHSD(comparison, "Day.E1DWheel")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1DWheel ~ Group.E1DWheel * Day.E1DWheel, data = exp1DW)
##
## $Day.E1DWheel
## diff lwr upr p adj
## 2-1 4.308 -28.29 36.90 0.9453
## 3-1 14.681 -17.91 47.28 0.5253
## 3-2 10.373 -22.22 42.97 0.7233
print(model.tables(aov.exp1DW, "means"), digits = 9)
## Tables of means
## Grand mean
##
## 61.79
##
## Group.E1DWheel
## Group.E1DWheel
## IL-1B Saline
## 49.51 74.06
##
## Day.E1DWheel
## Day.E1DWheel
## 1 2 3
## 55.46 59.77 70.14
##
## Group.E1DWheel:Day.E1DWheel
## Day.E1DWheel
## Group.E1DWheel 1 2 3
## IL-1B 38.15 40.79 69.60
## Saline 72.77 78.74 70.68
exp1DM <- read.csv("~/Desktop/experiment1stats2wayD.csv")
exp1DW
## Group.E1DWheel Day.E1DWheel Data.E1DWheel Group.E1DMotion Day.E1DMotion
## 1 Saline 1 81.57382 Saline 1
## 2 Saline 1 62.37675 Saline 1
## 3 Saline 1 20.70532 Saline 1
## 4 Saline 1 106.91492 Saline 1
## 5 Saline 1 56.68948 Saline 1
## 6 Saline 1 67.36369 Saline 1
## 7 Saline 1 87.97646 Saline 1
## 8 Saline 1 120.46230 Saline 1
## 9 Saline 1 50.85003 Saline 1
## 10 IL-1B 1 0.00000 IL-1B 2
## 11 IL-1B 1 0.01275 IL-1B 2
## 12 IL-1B 1 65.49307 IL-1B 2
## 13 IL-1B 1 14.11292 IL-1B 2
## 14 IL-1B 1 17.50888 IL-1B 2
## 15 IL-1B 1 20.83834 IL-1B 2
## 16 IL-1B 1 47.26633 IL-1B 2
## 17 IL-1B 1 65.77487 IL-1B 2
## 18 IL-1B 1 112.30327 IL-1B 2
## 19 Saline 2 58.73412 Saline 3
## 20 Saline 2 40.53128 Saline 3
## 21 Saline 2 12.06786 Saline 3
## 22 Saline 2 126.00050 Saline 3
## 23 Saline 2 69.02292 Saline 3
## 24 Saline 2 132.19242 Saline 3
## 25 Saline 2 64.11641 Saline 3
## 26 Saline 2 100.31302 Saline 3
## 27 Saline 2 105.68863 Saline 3
## 28 IL-1B 2 1.80399 IL-1B 1
## 29 IL-1B 2 0.21902 IL-1B 1
## 30 IL-1B 2 67.53658 IL-1B 1
## 31 IL-1B 2 17.48256 IL-1B 1
## 32 IL-1B 2 19.94245 IL-1B 1
## 33 IL-1B 2 15.89365 IL-1B 1
## 34 IL-1B 2 64.39357 IL-1B 1
## 35 IL-1B 2 4.16481 IL-1B 1
## 36 IL-1B 2 175.66857 IL-1B 1
## 37 Saline 3 60.66438 Saline 2
## 38 Saline 3 9.69562 Saline 2
## 39 Saline 3 9.12771 Saline 2
## 40 Saline 3 114.33789 Saline 2
## 41 Saline 3 44.04209 Saline 2
## 42 Saline 3 124.21084 Saline 2
## 43 Saline 3 86.36566 Saline 2
## 44 Saline 3 88.29643 Saline 2
## 45 Saline 3 99.35696 Saline 2
## 46 IL-1B 3 67.68583 IL-1B 3
## 47 IL-1B 3 7.22342 IL-1B 3
## 48 IL-1B 3 43.17195 IL-1B 3
## 49 IL-1B 3 107.38815 IL-1B 3
## 50 IL-1B 3 88.97880 IL-1B 3
## 51 IL-1B 3 74.05380 IL-1B 3
## 52 IL-1B 3 88.23440 IL-1B 3
## 53 IL-1B 3 68.63493 IL-1B 3
## 54 IL-1B 3 81.01714 IL-1B 3
## Data.E1DMotion
## 1 152.885
## 2 91.255
## 3 63.176
## 4 93.006
## 5 73.731
## 6 55.652
## 7 62.788
## 8 68.375
## 9 49.885
## 10 61.100
## 11 55.589
## 12 54.300
## 13 89.921
## 14 61.300
## 15 434.478
## 16 56.632
## 17 56.107
## 18 85.980
## 19 109.696
## 20 43.960
## 21 59.263
## 22 100.310
## 23 59.445
## 24 234.200
## 25 66.409
## 26 57.802
## 27 82.359
## 28 7.100
## 29 3.928
## 30 25.238
## 31 52.537
## 32 48.828
## 33 47.112
## 34 54.448
## 35 66.617
## 36 115.921
## 37 27.657
## 38 10.087
## 39 17.928
## 40 74.940
## 41 62.155
## 42 88.453
## 43 69.472
## 44 7.647
## 45 100.466
## 46 65.138
## 47 33.508
## 48 44.693
## 49 134.440
## 50 112.886
## 51 220.760
## 52 93.310
## 53 84.714
## 54 104.221
tapply(exp1DW$Data.E1DMotion, exp1DW$Group.E1DMotion, sd)
## IL-1B Saline
## 82.21 44.84
tapply(exp1DW$Data.E1DMotion, exp1DW$Day.E1DMotion, sd)
## 1 2 3
## 35.68 92.83 55.42
exp1DW$Group.E1DMotion <- as.factor(exp1DW$Group.E1DMotion)
exp1DW$Day.E1DMotion <- as.factor(exp1DW$Day.E1DMotion)
exp1DW$Data.E1DMotion <- as.numeric(exp1DW$Data.E1DMotion)
aov.exp1DW = aov(Data.E1DMotion ~ Group.E1DMotion * Day.E1DMotion, data = exp1DW)
summary(aov.exp1DW)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.E1DMotion 1 1534 1534 0.37 0.55
## Day.E1DMotion 2 9173 4587 1.09 0.34
## Group.E1DMotion:Day.E1DMotion 2 17165 8583 2.04 0.14
## Residuals 48 201648 4201
comparison <- aov(Data.E1DMotion ~ Group.E1DMotion * Day.E1DMotion, data = exp1DW)
TukeyHSD(comparison, "Group.E1DMotion")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1DMotion ~ Group.E1DMotion * Day.E1DMotion, data = exp1DW)
##
## $Group.E1DMotion
## diff lwr upr p adj
## Saline-IL-1B -10.66 -46.13 24.81 0.5485
TukeyHSD(comparison, "Day.E1DMotion")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1DMotion ~ Group.E1DMotion * Day.E1DMotion, data = exp1DW)
##
## $Day.E1DMotion
## diff lwr upr p adj
## 2-1 15.65 -36.60 67.90 0.7503
## 3-1 31.92 -20.33 84.18 0.3105
## 3-2 16.27 -35.98 68.52 0.7332
print(model.tables(aov.exp1DW, "means"), digits = 9)
## Tables of means
## Grand mean
##
## 78.77
##
## Group.E1DMotion
## Group.E1DMotion
## IL-1B Saline
## 84.10 73.44
##
## Day.E1DMotion
## Day.E1DMotion
## 1 2 3
## 62.92 78.57 94.84
##
## Group.E1DMotion:Day.E1DMotion
## Day.E1DMotion
## Group.E1DMotion 1 2 3
## IL-1B 46.86 106.16 99.30
## Saline 78.97 50.98 90.38