Sal vs. IL falafel wheel and motion
exp1FW <- read.csv("~/Desktop/experiment1stats2wayF.csv")
exp1FW
## Group.E1FWheel Day.E1FWheel Data.E1FWheel Group.E1FMotion Day.E1FMotion
## 1 Saline 1 40.887 Saline 1
## 2 Saline 1 34.673 Saline 1
## 3 Saline 1 49.657 Saline 1
## 4 Saline 1 53.185 Saline 1
## 5 Saline 1 60.284 Saline 1
## 6 Saline 1 66.177 Saline 1
## 7 Saline 1 95.697 Saline 1
## 8 Saline 1 23.537 Saline 1
## 9 Saline 1 67.823 Saline 1
## 10 Saline 1 73.622 Saline 1
## 11 Saline 2 45.855 Saline 2
## 12 Saline 2 68.579 Saline 2
## 13 Saline 2 65.586 Saline 2
## 14 Saline 2 54.962 Saline 2
## 15 Saline 2 97.371 Saline 2
## 16 Saline 2 49.348 Saline 2
## 17 Saline 2 93.660 Saline 2
## 18 Saline 2 32.341 Saline 2
## 19 Saline 2 76.628 Saline 2
## 20 Saline 2 85.472 Saline 2
## 21 Saline 3 46.476 Saline 3
## 22 Saline 3 66.465 Saline 3
## 23 Saline 3 84.222 Saline 3
## 24 Saline 3 51.044 Saline 3
## 25 Saline 3 92.015 Saline 3
## 26 Saline 3 60.039 Saline 3
## 27 Saline 3 70.089 Saline 3
## 28 Saline 3 38.171 Saline 3
## 29 Saline 3 113.237 Saline 3
## 30 Saline 3 67.147 Saline 3
## 31 IL-1B 1 43.018 IL-1B 1
## 32 IL-1B 1 3.747 IL-1B 1
## 33 IL-1B 1 18.807 IL-1B 1
## 34 IL-1B 1 8.585 IL-1B 1
## 35 IL-1B 1 11.869 IL-1B 1
## 36 IL-1B 1 15.967 IL-1B 1
## 37 IL-1B 1 20.367 IL-1B 1
## 38 IL-1B 1 7.981 IL-1B 1
## 39 IL-1B 1 16.951 IL-1B 1
## 40 IL-1B 1 19.785 IL-1B 1
## 41 IL-1B 2 43.357 IL-1B 2
## 42 IL-1B 2 12.851 IL-1B 2
## 43 IL-1B 2 27.114 IL-1B 2
## 44 IL-1B 2 36.562 IL-1B 2
## 45 IL-1B 2 38.793 IL-1B 2
## 46 IL-1B 2 41.972 IL-1B 2
## 47 IL-1B 2 33.353 IL-1B 2
## 48 IL-1B 2 45.532 IL-1B 2
## 49 IL-1B 2 35.421 IL-1B 2
## 50 IL-1B 2 58.227 IL-1B 2
## 51 IL-1B 3 128.022 IL-1B 3
## 52 IL-1B 3 107.717 IL-1B 3
## 53 IL-1B 3 112.878 IL-1B 3
## 54 IL-1B 3 118.687 IL-1B 3
## 55 IL-1B 3 95.612 IL-1B 3
## 56 IL-1B 3 82.522 IL-1B 3
## 57 IL-1B 3 162.659 IL-1B 3
## 58 IL-1B 3 50.300 IL-1B 3
## 59 IL-1B 3 106.246 IL-1B 3
## 60 IL-1B 3 71.968 IL-1B 3
## Data.E1FMotion
## 1 60.78
## 2 38.41
## 3 58.80
## 4 59.67
## 5 113.37
## 6 58.44
## 7 71.04
## 8 109.86
## 9 99.33
## 10 90.01
## 11 35.74
## 12 59.58
## 13 77.30
## 14 72.66
## 15 100.37
## 16 36.86
## 17 62.36
## 18 145.42
## 19 97.77
## 20 124.27
## 21 74.98
## 22 75.88
## 23 104.39
## 24 69.37
## 25 157.71
## 26 50.81
## 27 50.71
## 28 83.37
## 29 110.58
## 30 56.69
## 31 45.56
## 32 31.14
## 33 39.09
## 34 21.81
## 35 29.06
## 36 41.66
## 37 42.79
## 38 25.61
## 39 58.74
## 40 19.79
## 41 66.07
## 42 59.39
## 43 50.81
## 44 37.85
## 45 59.26
## 46 33.27
## 47 65.68
## 48 68.13
## 49 114.45
## 50 58.23
## 51 95.10
## 52 219.73
## 53 104.33
## 54 69.21
## 55 102.89
## 56 95.46
## 57 120.63
## 58 77.10
## 59 148.29
## 60 71.97
tapply(exp1FW$Data.E1FWheel, exp1FW$Group.E1FWheel, sd)
## IL-1B Saline
## 42.51 21.64
tapply(exp1FW$Data.E1FWheel, exp1FW$Day.E1FWheel, sd)
## 1 2 3
## 26.08 22.78 31.96
exp1FW$Group.E1FWheel <- as.factor(exp1FW$Group.E1FWheel)
exp1FW$Day.E1FWheel <- as.factor(exp1FW$Day.E1FWheel)
exp1FW$Data.E1FWheel <- as.numeric(exp1FW$Data.E1FWheel)
aov.exp1FW = aov(Data.E1FWheel ~ Group.E1FWheel * Day.E1FWheel, data = exp1FW)
summary(aov.exp1FW)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.E1FWheel 1 2011 2011 4.56 0.037 *
## Day.E1FWheel 2 25800 12900 29.26 2.5e-09 ***
## Group.E1FWheel:Day.E1FWheel 2 16372 8186 18.57 7.3e-07 ***
## Residuals 54 23809 441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison <- aov(Data.E1FWheel ~ Group.E1FWheel * Day.E1FWheel, data = exp1FW)
TukeyHSD(comparison, "Group.E1FWheel")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1FWheel ~ Group.E1FWheel * Day.E1FWheel, data = exp1FW)
##
## $Group.E1FWheel
## diff lwr upr p adj
## Saline-IL-1B 11.58 0.7096 22.45 0.0372
TukeyHSD(comparison, "Day.E1FWheel")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1FWheel ~ Group.E1FWheel * Day.E1FWheel, data = exp1FW)
##
## $Day.E1FWheel
## diff lwr upr p adj
## 2-1 15.52 -0.4843 31.52 0.0592
## 3-1 49.64 33.6422 65.65 0.0000
## 3-2 34.13 18.1239 50.13 0.0000
print(model.tables(aov.exp1FW, "means"), digits = 17)
## Tables of means
## Grand mean
##
## 58.35
##
## Group.E1FWheel
## Group.E1FWheel
## IL-1B Saline
## 52.56 64.14
##
## Day.E1FWheel
## Day.E1FWheel
## 1 2 3
## 36.63 52.15 86.28
##
## Group.E1FWheel:Day.E1FWheel
## Day.E1FWheel
## Group.E1FWheel 1 2 3
## IL-1B 16.71 37.32 103.66
## Saline 56.55 66.98 68.89
exp1FM <- read.csv("~/Desktop/experiment1stats2wayF.csv")
exp1FW
## Group.E1FWheel Day.E1FWheel Data.E1FWheel Group.E1FMotion Day.E1FMotion
## 1 Saline 1 40.887 Saline 1
## 2 Saline 1 34.673 Saline 1
## 3 Saline 1 49.657 Saline 1
## 4 Saline 1 53.185 Saline 1
## 5 Saline 1 60.284 Saline 1
## 6 Saline 1 66.177 Saline 1
## 7 Saline 1 95.697 Saline 1
## 8 Saline 1 23.537 Saline 1
## 9 Saline 1 67.823 Saline 1
## 10 Saline 1 73.622 Saline 1
## 11 Saline 2 45.855 Saline 2
## 12 Saline 2 68.579 Saline 2
## 13 Saline 2 65.586 Saline 2
## 14 Saline 2 54.962 Saline 2
## 15 Saline 2 97.371 Saline 2
## 16 Saline 2 49.348 Saline 2
## 17 Saline 2 93.660 Saline 2
## 18 Saline 2 32.341 Saline 2
## 19 Saline 2 76.628 Saline 2
## 20 Saline 2 85.472 Saline 2
## 21 Saline 3 46.476 Saline 3
## 22 Saline 3 66.465 Saline 3
## 23 Saline 3 84.222 Saline 3
## 24 Saline 3 51.044 Saline 3
## 25 Saline 3 92.015 Saline 3
## 26 Saline 3 60.039 Saline 3
## 27 Saline 3 70.089 Saline 3
## 28 Saline 3 38.171 Saline 3
## 29 Saline 3 113.237 Saline 3
## 30 Saline 3 67.147 Saline 3
## 31 IL-1B 1 43.018 IL-1B 1
## 32 IL-1B 1 3.747 IL-1B 1
## 33 IL-1B 1 18.807 IL-1B 1
## 34 IL-1B 1 8.585 IL-1B 1
## 35 IL-1B 1 11.869 IL-1B 1
## 36 IL-1B 1 15.967 IL-1B 1
## 37 IL-1B 1 20.367 IL-1B 1
## 38 IL-1B 1 7.981 IL-1B 1
## 39 IL-1B 1 16.951 IL-1B 1
## 40 IL-1B 1 19.785 IL-1B 1
## 41 IL-1B 2 43.357 IL-1B 2
## 42 IL-1B 2 12.851 IL-1B 2
## 43 IL-1B 2 27.114 IL-1B 2
## 44 IL-1B 2 36.562 IL-1B 2
## 45 IL-1B 2 38.793 IL-1B 2
## 46 IL-1B 2 41.972 IL-1B 2
## 47 IL-1B 2 33.353 IL-1B 2
## 48 IL-1B 2 45.532 IL-1B 2
## 49 IL-1B 2 35.421 IL-1B 2
## 50 IL-1B 2 58.227 IL-1B 2
## 51 IL-1B 3 128.022 IL-1B 3
## 52 IL-1B 3 107.717 IL-1B 3
## 53 IL-1B 3 112.878 IL-1B 3
## 54 IL-1B 3 118.687 IL-1B 3
## 55 IL-1B 3 95.612 IL-1B 3
## 56 IL-1B 3 82.522 IL-1B 3
## 57 IL-1B 3 162.659 IL-1B 3
## 58 IL-1B 3 50.300 IL-1B 3
## 59 IL-1B 3 106.246 IL-1B 3
## 60 IL-1B 3 71.968 IL-1B 3
## Data.E1FMotion
## 1 60.78
## 2 38.41
## 3 58.80
## 4 59.67
## 5 113.37
## 6 58.44
## 7 71.04
## 8 109.86
## 9 99.33
## 10 90.01
## 11 35.74
## 12 59.58
## 13 77.30
## 14 72.66
## 15 100.37
## 16 36.86
## 17 62.36
## 18 145.42
## 19 97.77
## 20 124.27
## 21 74.98
## 22 75.88
## 23 104.39
## 24 69.37
## 25 157.71
## 26 50.81
## 27 50.71
## 28 83.37
## 29 110.58
## 30 56.69
## 31 45.56
## 32 31.14
## 33 39.09
## 34 21.81
## 35 29.06
## 36 41.66
## 37 42.79
## 38 25.61
## 39 58.74
## 40 19.79
## 41 66.07
## 42 59.39
## 43 50.81
## 44 37.85
## 45 59.26
## 46 33.27
## 47 65.68
## 48 68.13
## 49 114.45
## 50 58.23
## 51 95.10
## 52 219.73
## 53 104.33
## 54 69.21
## 55 102.89
## 56 95.46
## 57 120.63
## 58 77.10
## 59 148.29
## 60 71.97
tapply(exp1FW$Data.E1FMotion, exp1FW$Group.E1FMotion, sd)
## IL-1B Saline
## 42.77 30.82
tapply(exp1FW$Data.E1FMotion, exp1FW$Day.E1FMotion, sd)
## 1 2 3
## 28.42 30.71 40.94
exp1FW$Group.E1FMotion <- as.factor(exp1FW$Group.E1FMotion)
exp1FW$Day.E1FMotion <- as.factor(exp1FW$Day.E1FMotion)
exp1FW$Data.E1FMotion <- as.numeric(exp1FW$Data.E1FMotion)
aov.exp1FW = aov(Data.E1FMotion ~ Group.E1FMotion * Day.E1FMotion, data = exp1FW)
summary(aov.exp1FW)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.E1FMotion 1 1853 1853 1.95 0.16824
## Day.E1FMotion 2 17329 8665 9.12 0.00039 ***
## Group.E1FMotion:Day.E1FMotion 2 11963 5981 6.30 0.00348 **
## Residuals 54 51298 950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison <- aov(Data.E1FMotion ~ Group.E1FMotion * Day.E1FMotion, data = exp1FW)
TukeyHSD(comparison, "Group.E1FMotion")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1FMotion ~ Group.E1FMotion * Day.E1FMotion, data = exp1FW)
##
## $Group.E1FMotion
## diff lwr upr p adj
## Saline-IL-1B 11.11 -4.84 27.07 0.1682
TukeyHSD(comparison, "Day.E1FMotion")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.E1FMotion ~ Group.E1FMotion * Day.E1FMotion, data = exp1FW)
##
## $Day.E1FMotion
## diff lwr upr p adj
## 2-1 15.53 -7.964 39.01 0.2574
## 3-1 41.21 17.724 64.70 0.0003
## 3-2 25.69 2.198 49.18 0.0290
print(model.tables(aov.exp1FW, "means"), digits = 17)
## Tables of means
## Grand mean
##
## 74.66
##
## Group.E1FMotion
## Group.E1FMotion
## IL-1B Saline
## 69.10 80.22
##
## Day.E1FMotion
## Day.E1FMotion
## 1 2 3
## 55.75 71.27 96.96
##
## Group.E1FMotion:Day.E1FMotion
## Day.E1FMotion
## Group.E1FMotion 1 2 3
## IL-1B 35.52 61.31 110.47
## Saline 75.97 81.23 83.45