Sal vs. IL dommer bout analysis
exp1DB <- read.csv("~/Desktop/experiment 1 bout analysis D.csv")
exp1DB
## Group.Db Day.Db Data.Db Group.Dbl Day.Dbl Data.Dbl
## 1 Saline 1 85.106 Saline 1 90.31
## 2 Saline 1 65.934 Saline 1 104.64
## 3 Saline 1 67.708 Saline 1 44.43
## 4 Saline 1 105.932 Saline 1 100.65
## 5 Saline 1 72.289 Saline 1 96.93
## 6 Saline 1 49.180 Saline 1 117.11
## 7 Saline 1 103.846 Saline 1 80.46
## 8 Saline 1 119.048 Saline 1 75.83
## 9 Saline 2 117.021 Saline 2 58.31
## 10 Saline 2 54.945 Saline 2 91.32
## 11 Saline 2 83.333 Saline 2 39.49
## 12 Saline 2 114.407 Saline 2 114.38
## 13 Saline 2 54.217 Saline 2 137.32
## 14 Saline 2 106.557 Saline 2 100.68
## 15 Saline 2 84.615 Saline 2 79.24
## 16 Saline 2 87.302 Saline 2 91.99
## 17 Saline 3 117.021 Saline 3 62.61
## 18 Saline 3 27.473 Saline 3 62.78
## 19 Saline 3 83.333 Saline 3 33.15
## 20 Saline 3 118.644 Saline 3 106.79
## 21 Saline 3 54.217 Saline 3 92.08
## 22 Saline 3 106.557 Saline 3 113.40
## 23 Saline 3 100.000 Saline 3 89.28
## 24 Saline 3 83.333 Saline 3 98.99
## 25 IL-1B 1 0.000 IL-1B 1 120.00
## 26 IL-1B 1 6.024 IL-1B 1 87.63
## 27 IL-1B 1 20.833 IL-1B 1 45.45
## 28 IL-1B 1 44.643 IL-1B 1 68.97
## 29 IL-1B 1 32.787 IL-1B 1 100.00
## 30 IL-1B 1 44.118 IL-1B 1 177.08
## 31 IL-1B 1 46.429 IL-1B 1 97.09
## 32 IL-1B 1 73.643 IL-1B 1 85.59
## 33 IL-1B 2 50.000 IL-1B 2 20.34
## 34 IL-1B 2 12.048 IL-1B 2 24.54
## 35 IL-1B 2 62.500 IL-1B 2 33.42
## 36 IL-1B 2 66.964 IL-1B 2 60.07
## 37 IL-1B 2 65.574 IL-1B 2 58.70
## 38 IL-1B 2 58.824 IL-1B 2 42.62
## 39 IL-1B 2 60.714 IL-1B 2 115.64
## 40 IL-1B 2 7.752 IL-1B 2 129.22
## 41 IL-1B 3 100.000 IL-1B 3 66.65
## 42 IL-1B 3 66.265 IL-1B 3 26.13
## 43 IL-1B 3 83.333 IL-1B 3 49.50
## 44 IL-1B 3 125.000 IL-1B 3 83.00
## 45 IL-1B 3 131.148 IL-1B 3 63.14
## 46 IL-1B 3 95.588 IL-1B 3 76.67
## 47 IL-1B 3 103.571 IL-1B 3 89.26
## 48 IL-1B 3 73.643 IL-1B 3 107.91
tapply(exp1DB$Data.Db, exp1DB$Group.Db, sd)
## IL-1B Saline
## 35.92 25.84
tapply(exp1DB$Data.Db, exp1DB$Day.Db, sd)
## 1 2 3
## 34.76 31.14 27.40
exp1DB$Group.Db <- as.factor(exp1DB$Group.Db)
exp1DB$Day.Db <- as.factor(exp1DB$Day.Db)
exp1DB$Data.Db <- as.numeric(exp1DB$Data.Db)
aov.exp1DB = aov(Data.Db ~ Group.Db * Day.Db, data = exp1DB)
summary(aov.exp1DB)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.Db 1 8285 8285 12.84 0.00088 ***
## Day.Db 2 9397 4699 7.28 0.00193 **
## Group.Db:Day.Db 2 8548 4274 6.62 0.00316 **
## Residuals 42 27100 645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison <- aov(Data.Db ~ Group.Db * Day.Db, data = exp1DB)
TukeyHSD(comparison, "Group.Db")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.Db ~ Group.Db * Day.Db, data = exp1DB)
##
## $Group.Db
## diff lwr upr p adj
## Saline-IL-1B 26.28 11.48 41.07 9e-04
TukeyHSD(comparison, "Day.Db")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.Db ~ Group.Db * Day.Db, data = exp1DB)
##
## $Day.Db
## diff lwr upr p adj
## 2-1 9.328 -12.490 31.15 0.5570
## 3-1 33.225 11.407 55.04 0.0018
## 3-2 23.897 2.079 45.72 0.0290
print(model.tables(aov.exp1DB, "means"), digits = 17)
## Tables of means
## Grand mean
##
## 72.78
##
## Group.Db
## Group.Db
## IL-1B Saline
## 59.64 85.92
##
## Day.Db
## Day.Db
## 1 2 3
## 58.60 67.92 91.82
##
## Group.Db:Day.Db
## Day.Db
## Group.Db 1 2 3
## IL-1B 33.56 48.05 97.32
## Saline 83.63 87.80 86.32
exp1DBl <- read.csv("~/Desktop/experiment 1 bout analysis D.csv")
exp1DBl
## Group.Db Day.Db Data.Db Group.Dbl Day.Dbl Data.Dbl
## 1 Saline 1 85.106 Saline 1 90.31
## 2 Saline 1 65.934 Saline 1 104.64
## 3 Saline 1 67.708 Saline 1 44.43
## 4 Saline 1 105.932 Saline 1 100.65
## 5 Saline 1 72.289 Saline 1 96.93
## 6 Saline 1 49.180 Saline 1 117.11
## 7 Saline 1 103.846 Saline 1 80.46
## 8 Saline 1 119.048 Saline 1 75.83
## 9 Saline 2 117.021 Saline 2 58.31
## 10 Saline 2 54.945 Saline 2 91.32
## 11 Saline 2 83.333 Saline 2 39.49
## 12 Saline 2 114.407 Saline 2 114.38
## 13 Saline 2 54.217 Saline 2 137.32
## 14 Saline 2 106.557 Saline 2 100.68
## 15 Saline 2 84.615 Saline 2 79.24
## 16 Saline 2 87.302 Saline 2 91.99
## 17 Saline 3 117.021 Saline 3 62.61
## 18 Saline 3 27.473 Saline 3 62.78
## 19 Saline 3 83.333 Saline 3 33.15
## 20 Saline 3 118.644 Saline 3 106.79
## 21 Saline 3 54.217 Saline 3 92.08
## 22 Saline 3 106.557 Saline 3 113.40
## 23 Saline 3 100.000 Saline 3 89.28
## 24 Saline 3 83.333 Saline 3 98.99
## 25 IL-1B 1 0.000 IL-1B 1 120.00
## 26 IL-1B 1 6.024 IL-1B 1 87.63
## 27 IL-1B 1 20.833 IL-1B 1 45.45
## 28 IL-1B 1 44.643 IL-1B 1 68.97
## 29 IL-1B 1 32.787 IL-1B 1 100.00
## 30 IL-1B 1 44.118 IL-1B 1 177.08
## 31 IL-1B 1 46.429 IL-1B 1 97.09
## 32 IL-1B 1 73.643 IL-1B 1 85.59
## 33 IL-1B 2 50.000 IL-1B 2 20.34
## 34 IL-1B 2 12.048 IL-1B 2 24.54
## 35 IL-1B 2 62.500 IL-1B 2 33.42
## 36 IL-1B 2 66.964 IL-1B 2 60.07
## 37 IL-1B 2 65.574 IL-1B 2 58.70
## 38 IL-1B 2 58.824 IL-1B 2 42.62
## 39 IL-1B 2 60.714 IL-1B 2 115.64
## 40 IL-1B 2 7.752 IL-1B 2 129.22
## 41 IL-1B 3 100.000 IL-1B 3 66.65
## 42 IL-1B 3 66.265 IL-1B 3 26.13
## 43 IL-1B 3 83.333 IL-1B 3 49.50
## 44 IL-1B 3 125.000 IL-1B 3 83.00
## 45 IL-1B 3 131.148 IL-1B 3 63.14
## 46 IL-1B 3 95.588 IL-1B 3 76.67
## 47 IL-1B 3 103.571 IL-1B 3 89.26
## 48 IL-1B 3 73.643 IL-1B 3 107.91
tapply(exp1DBl$Data.Dbl, exp1DBl$Group.Dbl, sd)
## IL-1B Saline
## 37.71 25.95
tapply(exp1DBl$Data.Dbl, exp1DBl$Day.Dbl, sd)
## 1 2 3
## 30.98 37.95 26.06
exp1DBl$Group.Dbl <- as.factor(exp1DBl$Group.Dbl)
exp1DBl$Day.Dbl <- as.factor(exp1DBl$Day.Dbl)
exp1DBl$Data.Dbl <- as.numeric(exp1DBl$Data.Dbl)
aov.exp1DBl = aov(Data.Dbl ~ Group.Dbl * Day.Dbl, data = exp1DBl)
summary(aov.exp1DBl)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.Dbl 1 1339 1339 1.34 0.25
## Day.Dbl 2 3352 1676 1.68 0.20
## Group.Dbl:Day.Dbl 2 2820 1410 1.41 0.26
## Residuals 42 42018 1000
comparison <- aov(Data.Dbl ~ Group.Dbl * Day.Dbl, data = exp1DBl)
TukeyHSD(comparison, "Group.Dbl")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.Dbl ~ Group.Dbl * Day.Dbl, data = exp1DBl)
##
## $Group.Dbl
## diff lwr upr p adj
## Saline-IL-1B 10.56 -7.862 28.99 0.2538
TukeyHSD(comparison, "Day.Dbl")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.Dbl ~ Group.Dbl * Day.Dbl, data = exp1DBl)
##
## $Day.Dbl
## diff lwr upr p adj
## 2-1 -18.431 -45.60 8.738 0.2372
## 3-1 -16.926 -44.09 10.243 0.2950
## 3-2 1.505 -25.66 28.674 0.9901
print(model.tables(aov.exp1DBl, "means"), digits = 17)
## Tables of means
## Grand mean
##
## 81.47
##
## Group.Dbl
## Group.Dbl
## IL-1B Saline
## 76.19 86.76
##
## Day.Dbl
## Day.Dbl
## 1 2 3
## 93.26 74.83 76.33
##
## Group.Dbl:Day.Dbl
## Day.Dbl
## Group.Dbl 1 2 3
## IL-1B 97.73 60.57 70.28
## Saline 88.79 89.09 82.39