Coinjection of Falafels
exp3FW <- read.csv("~/Desktop/experiment 3 stats.csv")
exp3FW
## Group.first3 Day.first3 Data.first3 Group.post3 Day.post3 Data.post3
## 1 Saline 1 15.040 Saline 1 84.383
## 2 Saline 1 7.508 Saline 1 104.084
## 3 Saline 1 85.652 Saline 1 22.727
## 4 Saline 1 13.203 Saline 1 3.408
## 5 Saline 1 61.901 Saline 1 138.816
## 6 MPD 1 246.028 MPD 1 54.123
## 7 MPD 1 182.868 MPD 1 123.425
## 8 MPD 1 116.984 MPD 1 51.214
## 9 MPD 1 63.085 MPD 1 142.668
## 10 MPD 1 148.148 MPD 1 78.997
## 11 MPD 1 414.946 MPD 1 56.689
## 12 MPD 1 45.211 MPD 1 17.757
## 13 MPD+IL1B 1 322.047 MPD+IL1B 1 45.833
## 14 MPD+IL1B 1 1731.034 MPD+IL1B 1 8.621
## 15 MPD+IL1B 1 18.630 MPD+IL1B 1 34.996
## 16 MPD+IL1B 1 299.669 MPD+IL1B 1 92.308
## 17 MPD+IL1B 1 380.873 MPD+IL1B 1 83.696
## 18 MPD+IL1B 1 238.905 MPD+IL1B 1 150.074
## 19 MPD+IL1B 1 200.292 MPD+IL1B 1 0.000
## 20 Saline 2 52.009 Saline 2 134.687
## 21 Saline 2 56.223 Saline 2 56.773
## 22 Saline 2 86.384 Saline 2 181.818
## 23 Saline 2 2.986 Saline 2 163.583
## 24 Saline 2 45.623 Saline 2 193.336
## 25 MPD 2 89.144 MPD 2 49.666
## 26 MPD 2 95.361 MPD 2 41.523
## 27 MPD 2 63.836 MPD 2 58.052
## 28 MPD 2 105.766 MPD 2 123.346
## 29 MPD 2 96.334 MPD 2 88.471
## 30 MPD 2 111.603 MPD 2 2.268
## 31 MPD 2 40.783 MPD 2 31.222
## 32 MPD+IL1B 2 199.606 MPD+IL1B 2 17.361
## 33 MPD+IL1B 2 143.678 MPD+IL1B 2 91.855
## 34 MPD+IL1B 2 57.808 MPD+IL1B 2 31.934
## 35 MPD+IL1B 2 171.712 MPD+IL1B 2 161.538
## 36 MPD+IL1B 2 75.487 MPD+IL1B 2 69.182
## 37 MPD+IL1B 2 43.084 MPD+IL1B 2 180.783
## 38 MPD+IL1B 2 204.435 MPD+IL1B 2 31.333
## 39 Saline 3 48.839 Saline 3 158.468
## 40 Saline 3 55.556 Saline 3 62.998
## 41 Saline 3 125.183 Saline 3 81.169
## 42 Saline 3 85.193 Saline 3 0.000
## 43 Saline 3 109.513 Saline 3 118.799
## 44 MPD 3 37.401 MPD 3 30.564
## 45 MPD 3 66.346 MPD 3 89.347
## 46 MPD 3 51.977 MPD 3 87.636
## 47 MPD 3 157.993 MPD 3 111.699
## 48 MPD 3 105.686 MPD 3 49.106
## 49 MPD 3 146.018 MPD 3 13.605
## 50 MPD 3 51.918 MPD 3 77.686
## 51 MPD+IL1B 3 198.819 MPD+IL1B 3 4.167
## 52 MPD+IL1B 3 404.598 MPD+IL1B 3 28.537
## 53 MPD+IL1B 3 70.411 MPD+IL1B 3 86.614
## 54 MPD+IL1B 3 96.026 MPD+IL1B 3 82.906
## 55 MPD+IL1B 3 95.144 MPD+IL1B 3 47.654
## 56 MPD+IL1B 3 100.407 MPD+IL1B 3 9.658
## 57 MPD+IL1B 3 311.647 MPD+IL1B 3 135.020
## Group.first3.M Day.first3.M Data.first3.M Group.post3.M Day.post3.M
## 1 Saline 1 62.660 Saline 1
## 2 Saline 1 125.481 Saline 1
## 3 Saline 1 130.595 Saline 1
## 4 Saline 1 45.045 Saline 1
## 5 Saline 1 92.643 Saline 1
## 6 MPD 1 209.755 MPD 1
## 7 MPD 1 102.878 MPD 1
## 8 MPD 1 0.000 MPD 1
## 9 MPD 1 116.384 MPD 1
## 10 MPD 1 128.448 MPD 1
## 11 MPD 1 192.020 MPD 1
## 12 MPD 1 35.917 MPD 1
## 13 MPD+IL1B 1 259.983 MPD+IL1B 1
## 14 MPD+IL1B 1 163.293 MPD+IL1B 1
## 15 MPD+IL1B 1 89.915 MPD+IL1B 1
## 16 MPD+IL1B 1 146.285 MPD+IL1B 1
## 17 MPD+IL1B 1 57.944 MPD+IL1B 1
## 18 MPD+IL1B 1 62.202 MPD+IL1B 1
## 19 MPD+IL1B 1 71.925 MPD+IL1B 1
## 20 Saline 2 76.087 Saline 2
## 21 Saline 2 59.692 Saline 2
## 22 Saline 2 109.552 Saline 2
## 23 Saline 2 86.336 Saline 2
## 24 Saline 2 82.652 Saline 2
## 25 MPD 2 95.952 MPD 2
## 26 MPD 2 123.741 MPD 2
## 27 MPD 2 4.386 MPD 2
## 28 MPD 2 79.379 MPD 2
## 29 MPD 2 73.276 MPD 2
## 30 MPD 2 155.860 MPD 2
## 31 MPD 2 73.724 MPD 2
## 32 MPD+IL1B 2 65.972 MPD+IL1B 2
## 33 MPD+IL1B 2 63.428 MPD+IL1B 2
## 34 MPD+IL1B 2 62.745 MPD+IL1B 2
## 35 MPD+IL1B 2 169.505 MPD+IL1B 2
## 36 MPD+IL1B 2 103.738 MPD+IL1B 2
## 37 MPD+IL1B 2 80.466 MPD+IL1B 2
## 38 MPD+IL1B 2 152.941 MPD+IL1B 2
## 39 Saline 3 65.217 Saline 3
## 40 Saline 3 86.650 Saline 3
## 41 Saline 3 82.164 Saline 3
## 42 Saline 3 105.105 Saline 3
## 43 Saline 3 106.721 Saline 3
## 44 MPD 3 69.653 MPD 3
## 45 MPD 3 77.698 MPD 3
## 46 MPD 3 43.860 MPD 3
## 47 MPD 3 112.147 MPD 3
## 48 MPD 3 109.483 MPD 3
## 49 MPD 3 169.576 MPD 3
## 50 MPD 3 65.217 MPD 3
## 51 MPD+IL1B 3 49.479 MPD+IL1B 3
## 52 MPD+IL1B 3 89.069 MPD+IL1B 3
## 53 MPD+IL1B 3 78.287 MPD+IL1B 3
## 54 MPD+IL1B 3 166.409 MPD+IL1B 3
## 55 MPD+IL1B 3 98.131 MPD+IL1B 3
## 56 MPD+IL1B 3 115.934 MPD+IL1B 3
## 57 MPD+IL1B 3 127.005 MPD+IL1B 3
## Data.post3.M
## 1 28.360
## 2 155.159
## 3 165.973
## 4 42.184
## 5 95.524
## 6 103.078
## 7 105.919
## 8 3.030
## 9 118.729
## 10 66.746
## 11 74.519
## 12 31.697
## 13 64.685
## 14 50.667
## 15 43.140
## 16 92.910
## 17 53.409
## 18 64.572
## 19 6.722
## 20 32.059
## 21 95.811
## 22 66.077
## 23 89.330
## 24 157.205
## 25 86.675
## 26 84.112
## 27 3.030
## 28 88.629
## 29 98.927
## 30 40.865
## 31 43.967
## 32 51.136
## 33 128.000
## 34 33.593
## 35 140.587
## 36 63.636
## 37 127.836
## 38 114.597
## 39 130.086
## 40 105.508
## 41 105.099
## 42 16.129
## 43 98.799
## 44 57.918
## 45 105.919
## 46 112.121
## 47 93.311
## 48 100.715
## 49 161.058
## 50 59.305
## 51 125.437
## 52 78.667
## 53 93.352
## 54 75.795
## 55 36.364
## 56 85.079
## 57 128.201
tapply(exp3FW$Data.first3, exp3FW$Group.first3, sd)
## MPD MPD+IL1B Saline
## 86.39 356.28 37.01
tapply(exp3FW$Data.first3, exp3FW$Day.first3, sd)
## 1 2 3
## 383.13 54.82 94.21
exp3FW$Group.first3 <- as.factor(exp3FW$Group.first3)
exp3FW$Day.first3 <- as.factor(exp3FW$Day.first3)
exp3FW$Data.first3 <- as.numeric(exp3FW$Data.first3)
aov.exp3FW = aov(Data.first3 ~ Group.first3 * Day.first3, data = exp3FW)
summary(aov.exp3FW)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.first3 2 387994 193997 4.17 0.021 *
## Day.first3 2 239022 119511 2.57 0.087 .
## Group.first3:Day.first3 4 234793 58698 1.26 0.298
## Residuals 48 2233259 46526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison <- aov(Data.first3 ~ Group.first3 * Day.first3, data = exp3FW)
TukeyHSD(comparison, "Group.first3")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.first3 ~ Group.first3 * Day.first3, data = exp3FW)
##
## $Group.first3
## diff lwr upr p adj
## MPD+IL1B-MPD 139.38 -21.61 300.36 0.1018
## Saline-MPD -59.35 -235.70 117.01 0.6963
## Saline-MPD+IL1B -198.72 -375.08 -22.37 0.0239
TukeyHSD(comparison, "Day.first3")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.first3 ~ Group.first3 * Day.first3, data = exp3FW)
##
## $Day.first3
## diff lwr upr p adj
## 2-1 -150.01 -319.3 19.24 0.0918
## 3-1 -119.65 -288.9 49.60 0.2120
## 3-2 30.36 -138.9 199.61 0.9017
print(model.tables(aov.exp3FW, "means"), digits = 19)
## Tables of means
## Grand mean
##
## 151.8
##
## Group.first3
## MPD MPD+IL1B Saline
## 116.0683950652381355 255.4435433766666961 56.7207963377333968
## rep 21.0000000000000000 21.0000000000000000 15.0000000000000000
##
## Day.first3
## 1 2 3
## 241.6854964504210841 91.67694542410531255 122.0355924594737047
## rep 19.0000000000000000 19.00000000000000000 19.0000000000000000
##
## Group.first3:Day.first3
## Day.first3
## Group.first3 1 2 3
## MPD 173.90 86.12 88.19
## rep 7.00 7.00 7.00
## MPD+IL1B 455.92 127.97 182.44
## rep 7.00 7.00 7.00
## Saline 36.66 48.64 84.86
## rep 5.00 5.00 5.00
exp3PW <- read.csv("~/Desktop/experiment 3 stats.csv")
exp3PW
## Group.first3 Day.first3 Data.first3 Group.post3 Day.post3 Data.post3
## 1 Saline 1 15.040 Saline 1 84.383
## 2 Saline 1 7.508 Saline 1 104.084
## 3 Saline 1 85.652 Saline 1 22.727
## 4 Saline 1 13.203 Saline 1 3.408
## 5 Saline 1 61.901 Saline 1 138.816
## 6 MPD 1 246.028 MPD 1 54.123
## 7 MPD 1 182.868 MPD 1 123.425
## 8 MPD 1 116.984 MPD 1 51.214
## 9 MPD 1 63.085 MPD 1 142.668
## 10 MPD 1 148.148 MPD 1 78.997
## 11 MPD 1 414.946 MPD 1 56.689
## 12 MPD 1 45.211 MPD 1 17.757
## 13 MPD+IL1B 1 322.047 MPD+IL1B 1 45.833
## 14 MPD+IL1B 1 1731.034 MPD+IL1B 1 8.621
## 15 MPD+IL1B 1 18.630 MPD+IL1B 1 34.996
## 16 MPD+IL1B 1 299.669 MPD+IL1B 1 92.308
## 17 MPD+IL1B 1 380.873 MPD+IL1B 1 83.696
## 18 MPD+IL1B 1 238.905 MPD+IL1B 1 150.074
## 19 MPD+IL1B 1 200.292 MPD+IL1B 1 0.000
## 20 Saline 2 52.009 Saline 2 134.687
## 21 Saline 2 56.223 Saline 2 56.773
## 22 Saline 2 86.384 Saline 2 181.818
## 23 Saline 2 2.986 Saline 2 163.583
## 24 Saline 2 45.623 Saline 2 193.336
## 25 MPD 2 89.144 MPD 2 49.666
## 26 MPD 2 95.361 MPD 2 41.523
## 27 MPD 2 63.836 MPD 2 58.052
## 28 MPD 2 105.766 MPD 2 123.346
## 29 MPD 2 96.334 MPD 2 88.471
## 30 MPD 2 111.603 MPD 2 2.268
## 31 MPD 2 40.783 MPD 2 31.222
## 32 MPD+IL1B 2 199.606 MPD+IL1B 2 17.361
## 33 MPD+IL1B 2 143.678 MPD+IL1B 2 91.855
## 34 MPD+IL1B 2 57.808 MPD+IL1B 2 31.934
## 35 MPD+IL1B 2 171.712 MPD+IL1B 2 161.538
## 36 MPD+IL1B 2 75.487 MPD+IL1B 2 69.182
## 37 MPD+IL1B 2 43.084 MPD+IL1B 2 180.783
## 38 MPD+IL1B 2 204.435 MPD+IL1B 2 31.333
## 39 Saline 3 48.839 Saline 3 158.468
## 40 Saline 3 55.556 Saline 3 62.998
## 41 Saline 3 125.183 Saline 3 81.169
## 42 Saline 3 85.193 Saline 3 0.000
## 43 Saline 3 109.513 Saline 3 118.799
## 44 MPD 3 37.401 MPD 3 30.564
## 45 MPD 3 66.346 MPD 3 89.347
## 46 MPD 3 51.977 MPD 3 87.636
## 47 MPD 3 157.993 MPD 3 111.699
## 48 MPD 3 105.686 MPD 3 49.106
## 49 MPD 3 146.018 MPD 3 13.605
## 50 MPD 3 51.918 MPD 3 77.686
## 51 MPD+IL1B 3 198.819 MPD+IL1B 3 4.167
## 52 MPD+IL1B 3 404.598 MPD+IL1B 3 28.537
## 53 MPD+IL1B 3 70.411 MPD+IL1B 3 86.614
## 54 MPD+IL1B 3 96.026 MPD+IL1B 3 82.906
## 55 MPD+IL1B 3 95.144 MPD+IL1B 3 47.654
## 56 MPD+IL1B 3 100.407 MPD+IL1B 3 9.658
## 57 MPD+IL1B 3 311.647 MPD+IL1B 3 135.020
## Group.first3.M Day.first3.M Data.first3.M Group.post3.M Day.post3.M
## 1 Saline 1 62.660 Saline 1
## 2 Saline 1 125.481 Saline 1
## 3 Saline 1 130.595 Saline 1
## 4 Saline 1 45.045 Saline 1
## 5 Saline 1 92.643 Saline 1
## 6 MPD 1 209.755 MPD 1
## 7 MPD 1 102.878 MPD 1
## 8 MPD 1 0.000 MPD 1
## 9 MPD 1 116.384 MPD 1
## 10 MPD 1 128.448 MPD 1
## 11 MPD 1 192.020 MPD 1
## 12 MPD 1 35.917 MPD 1
## 13 MPD+IL1B 1 259.983 MPD+IL1B 1
## 14 MPD+IL1B 1 163.293 MPD+IL1B 1
## 15 MPD+IL1B 1 89.915 MPD+IL1B 1
## 16 MPD+IL1B 1 146.285 MPD+IL1B 1
## 17 MPD+IL1B 1 57.944 MPD+IL1B 1
## 18 MPD+IL1B 1 62.202 MPD+IL1B 1
## 19 MPD+IL1B 1 71.925 MPD+IL1B 1
## 20 Saline 2 76.087 Saline 2
## 21 Saline 2 59.692 Saline 2
## 22 Saline 2 109.552 Saline 2
## 23 Saline 2 86.336 Saline 2
## 24 Saline 2 82.652 Saline 2
## 25 MPD 2 95.952 MPD 2
## 26 MPD 2 123.741 MPD 2
## 27 MPD 2 4.386 MPD 2
## 28 MPD 2 79.379 MPD 2
## 29 MPD 2 73.276 MPD 2
## 30 MPD 2 155.860 MPD 2
## 31 MPD 2 73.724 MPD 2
## 32 MPD+IL1B 2 65.972 MPD+IL1B 2
## 33 MPD+IL1B 2 63.428 MPD+IL1B 2
## 34 MPD+IL1B 2 62.745 MPD+IL1B 2
## 35 MPD+IL1B 2 169.505 MPD+IL1B 2
## 36 MPD+IL1B 2 103.738 MPD+IL1B 2
## 37 MPD+IL1B 2 80.466 MPD+IL1B 2
## 38 MPD+IL1B 2 152.941 MPD+IL1B 2
## 39 Saline 3 65.217 Saline 3
## 40 Saline 3 86.650 Saline 3
## 41 Saline 3 82.164 Saline 3
## 42 Saline 3 105.105 Saline 3
## 43 Saline 3 106.721 Saline 3
## 44 MPD 3 69.653 MPD 3
## 45 MPD 3 77.698 MPD 3
## 46 MPD 3 43.860 MPD 3
## 47 MPD 3 112.147 MPD 3
## 48 MPD 3 109.483 MPD 3
## 49 MPD 3 169.576 MPD 3
## 50 MPD 3 65.217 MPD 3
## 51 MPD+IL1B 3 49.479 MPD+IL1B 3
## 52 MPD+IL1B 3 89.069 MPD+IL1B 3
## 53 MPD+IL1B 3 78.287 MPD+IL1B 3
## 54 MPD+IL1B 3 166.409 MPD+IL1B 3
## 55 MPD+IL1B 3 98.131 MPD+IL1B 3
## 56 MPD+IL1B 3 115.934 MPD+IL1B 3
## 57 MPD+IL1B 3 127.005 MPD+IL1B 3
## Data.post3.M
## 1 28.360
## 2 155.159
## 3 165.973
## 4 42.184
## 5 95.524
## 6 103.078
## 7 105.919
## 8 3.030
## 9 118.729
## 10 66.746
## 11 74.519
## 12 31.697
## 13 64.685
## 14 50.667
## 15 43.140
## 16 92.910
## 17 53.409
## 18 64.572
## 19 6.722
## 20 32.059
## 21 95.811
## 22 66.077
## 23 89.330
## 24 157.205
## 25 86.675
## 26 84.112
## 27 3.030
## 28 88.629
## 29 98.927
## 30 40.865
## 31 43.967
## 32 51.136
## 33 128.000
## 34 33.593
## 35 140.587
## 36 63.636
## 37 127.836
## 38 114.597
## 39 130.086
## 40 105.508
## 41 105.099
## 42 16.129
## 43 98.799
## 44 57.918
## 45 105.919
## 46 112.121
## 47 93.311
## 48 100.715
## 49 161.058
## 50 59.305
## 51 125.437
## 52 78.667
## 53 93.352
## 54 75.795
## 55 36.364
## 56 85.079
## 57 128.201
tapply(exp3PW$Data.post3, exp3PW$Group.post3, sd)
## MPD MPD+IL1B Saline
## 38.49 54.32 62.73
tapply(exp3PW$Data.post3, exp3PW$Day.post3, sd)
## 1 2 3
## 48.31 62.70 45.73
exp3PW$Group.post3 <- as.factor(exp3PW$Group.post3)
exp3PW$Day.post3 <- as.factor(exp3PW$Day.post3)
exp3PW$Data.post3 <- as.numeric(exp3PW$Data.post3)
aov.exp3PW = aov(Data.post3 ~ Group.post3 * Day.post3, data = exp3PW)
summary(aov.exp3PW)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.post3 2 13016 6508 2.53 0.09 .
## Day.post3 2 6317 3158 1.23 0.30
## Group.post3:Day.post3 4 14106 3526 1.37 0.26
## Residuals 48 123303 2569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison <- aov(Data.post3 ~ Group.post3 * Day.post3, data = exp3PW)
TukeyHSD(comparison, "Group.post3")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.post3 ~ Group.post3 * Day.post3, data = exp3PW)
##
## $Group.post3
## diff lwr upr p adj
## MPD+IL1B-MPD 0.7146 -37.114 38.54 0.9989
## Saline-MPD 34.6669 -6.772 76.11 0.1176
## Saline-MPD+IL1B 33.9523 -7.486 75.39 0.1277
TukeyHSD(comparison, "Day.post3")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.post3 ~ Group.post3 * Day.post3, data = exp3PW)
##
## $Day.post3
## diff lwr upr p adj
## 2-1 21.8374 -17.93 61.61 0.3868
## 3-1 -0.9572 -40.73 38.81 0.9981
## 3-2 -22.7946 -62.56 16.97 0.3560
print(model.tables(aov.exp3PW, "means"), digits = 19)
## Tables of means
## Grand mean
##
## 75.06
##
## Group.post3
## MPD MPD+IL1B Saline
## 65.66967016361907383 66.38423789566670052 100.3365769734000565
## rep 21.00000000000000000 21.00000000000000000 15.0000000000000000
##
## Day.post3
## 1 2 3
## 68.0957148324210948 89.93314360557899079 67.13854808021054055
## rep 19.0000000000000000 19.00000000000000000 19.00000000000000000
##
## Group.post3:Day.post3
## Day.post3
## Group.post3 1 2 3
## MPD 74.98 56.36 65.66
## rep 7.00 7.00 7.00
## MPD+IL1B 59.36 83.43 56.37
## rep 7.00 7.00 7.00
## Saline 70.68 146.04 84.29
## rep 5.00 5.00 5.00
exp3FM <- read.csv("~/Desktop/experiment 3 stats.csv")
exp3FM
## Group.first3 Day.first3 Data.first3 Group.post3 Day.post3 Data.post3
## 1 Saline 1 15.040 Saline 1 84.383
## 2 Saline 1 7.508 Saline 1 104.084
## 3 Saline 1 85.652 Saline 1 22.727
## 4 Saline 1 13.203 Saline 1 3.408
## 5 Saline 1 61.901 Saline 1 138.816
## 6 MPD 1 246.028 MPD 1 54.123
## 7 MPD 1 182.868 MPD 1 123.425
## 8 MPD 1 116.984 MPD 1 51.214
## 9 MPD 1 63.085 MPD 1 142.668
## 10 MPD 1 148.148 MPD 1 78.997
## 11 MPD 1 414.946 MPD 1 56.689
## 12 MPD 1 45.211 MPD 1 17.757
## 13 MPD+IL1B 1 322.047 MPD+IL1B 1 45.833
## 14 MPD+IL1B 1 1731.034 MPD+IL1B 1 8.621
## 15 MPD+IL1B 1 18.630 MPD+IL1B 1 34.996
## 16 MPD+IL1B 1 299.669 MPD+IL1B 1 92.308
## 17 MPD+IL1B 1 380.873 MPD+IL1B 1 83.696
## 18 MPD+IL1B 1 238.905 MPD+IL1B 1 150.074
## 19 MPD+IL1B 1 200.292 MPD+IL1B 1 0.000
## 20 Saline 2 52.009 Saline 2 134.687
## 21 Saline 2 56.223 Saline 2 56.773
## 22 Saline 2 86.384 Saline 2 181.818
## 23 Saline 2 2.986 Saline 2 163.583
## 24 Saline 2 45.623 Saline 2 193.336
## 25 MPD 2 89.144 MPD 2 49.666
## 26 MPD 2 95.361 MPD 2 41.523
## 27 MPD 2 63.836 MPD 2 58.052
## 28 MPD 2 105.766 MPD 2 123.346
## 29 MPD 2 96.334 MPD 2 88.471
## 30 MPD 2 111.603 MPD 2 2.268
## 31 MPD 2 40.783 MPD 2 31.222
## 32 MPD+IL1B 2 199.606 MPD+IL1B 2 17.361
## 33 MPD+IL1B 2 143.678 MPD+IL1B 2 91.855
## 34 MPD+IL1B 2 57.808 MPD+IL1B 2 31.934
## 35 MPD+IL1B 2 171.712 MPD+IL1B 2 161.538
## 36 MPD+IL1B 2 75.487 MPD+IL1B 2 69.182
## 37 MPD+IL1B 2 43.084 MPD+IL1B 2 180.783
## 38 MPD+IL1B 2 204.435 MPD+IL1B 2 31.333
## 39 Saline 3 48.839 Saline 3 158.468
## 40 Saline 3 55.556 Saline 3 62.998
## 41 Saline 3 125.183 Saline 3 81.169
## 42 Saline 3 85.193 Saline 3 0.000
## 43 Saline 3 109.513 Saline 3 118.799
## 44 MPD 3 37.401 MPD 3 30.564
## 45 MPD 3 66.346 MPD 3 89.347
## 46 MPD 3 51.977 MPD 3 87.636
## 47 MPD 3 157.993 MPD 3 111.699
## 48 MPD 3 105.686 MPD 3 49.106
## 49 MPD 3 146.018 MPD 3 13.605
## 50 MPD 3 51.918 MPD 3 77.686
## 51 MPD+IL1B 3 198.819 MPD+IL1B 3 4.167
## 52 MPD+IL1B 3 404.598 MPD+IL1B 3 28.537
## 53 MPD+IL1B 3 70.411 MPD+IL1B 3 86.614
## 54 MPD+IL1B 3 96.026 MPD+IL1B 3 82.906
## 55 MPD+IL1B 3 95.144 MPD+IL1B 3 47.654
## 56 MPD+IL1B 3 100.407 MPD+IL1B 3 9.658
## 57 MPD+IL1B 3 311.647 MPD+IL1B 3 135.020
## Group.first3.M Day.first3.M Data.first3.M Group.post3.M Day.post3.M
## 1 Saline 1 62.660 Saline 1
## 2 Saline 1 125.481 Saline 1
## 3 Saline 1 130.595 Saline 1
## 4 Saline 1 45.045 Saline 1
## 5 Saline 1 92.643 Saline 1
## 6 MPD 1 209.755 MPD 1
## 7 MPD 1 102.878 MPD 1
## 8 MPD 1 0.000 MPD 1
## 9 MPD 1 116.384 MPD 1
## 10 MPD 1 128.448 MPD 1
## 11 MPD 1 192.020 MPD 1
## 12 MPD 1 35.917 MPD 1
## 13 MPD+IL1B 1 259.983 MPD+IL1B 1
## 14 MPD+IL1B 1 163.293 MPD+IL1B 1
## 15 MPD+IL1B 1 89.915 MPD+IL1B 1
## 16 MPD+IL1B 1 146.285 MPD+IL1B 1
## 17 MPD+IL1B 1 57.944 MPD+IL1B 1
## 18 MPD+IL1B 1 62.202 MPD+IL1B 1
## 19 MPD+IL1B 1 71.925 MPD+IL1B 1
## 20 Saline 2 76.087 Saline 2
## 21 Saline 2 59.692 Saline 2
## 22 Saline 2 109.552 Saline 2
## 23 Saline 2 86.336 Saline 2
## 24 Saline 2 82.652 Saline 2
## 25 MPD 2 95.952 MPD 2
## 26 MPD 2 123.741 MPD 2
## 27 MPD 2 4.386 MPD 2
## 28 MPD 2 79.379 MPD 2
## 29 MPD 2 73.276 MPD 2
## 30 MPD 2 155.860 MPD 2
## 31 MPD 2 73.724 MPD 2
## 32 MPD+IL1B 2 65.972 MPD+IL1B 2
## 33 MPD+IL1B 2 63.428 MPD+IL1B 2
## 34 MPD+IL1B 2 62.745 MPD+IL1B 2
## 35 MPD+IL1B 2 169.505 MPD+IL1B 2
## 36 MPD+IL1B 2 103.738 MPD+IL1B 2
## 37 MPD+IL1B 2 80.466 MPD+IL1B 2
## 38 MPD+IL1B 2 152.941 MPD+IL1B 2
## 39 Saline 3 65.217 Saline 3
## 40 Saline 3 86.650 Saline 3
## 41 Saline 3 82.164 Saline 3
## 42 Saline 3 105.105 Saline 3
## 43 Saline 3 106.721 Saline 3
## 44 MPD 3 69.653 MPD 3
## 45 MPD 3 77.698 MPD 3
## 46 MPD 3 43.860 MPD 3
## 47 MPD 3 112.147 MPD 3
## 48 MPD 3 109.483 MPD 3
## 49 MPD 3 169.576 MPD 3
## 50 MPD 3 65.217 MPD 3
## 51 MPD+IL1B 3 49.479 MPD+IL1B 3
## 52 MPD+IL1B 3 89.069 MPD+IL1B 3
## 53 MPD+IL1B 3 78.287 MPD+IL1B 3
## 54 MPD+IL1B 3 166.409 MPD+IL1B 3
## 55 MPD+IL1B 3 98.131 MPD+IL1B 3
## 56 MPD+IL1B 3 115.934 MPD+IL1B 3
## 57 MPD+IL1B 3 127.005 MPD+IL1B 3
## Data.post3.M
## 1 28.360
## 2 155.159
## 3 165.973
## 4 42.184
## 5 95.524
## 6 103.078
## 7 105.919
## 8 3.030
## 9 118.729
## 10 66.746
## 11 74.519
## 12 31.697
## 13 64.685
## 14 50.667
## 15 43.140
## 16 92.910
## 17 53.409
## 18 64.572
## 19 6.722
## 20 32.059
## 21 95.811
## 22 66.077
## 23 89.330
## 24 157.205
## 25 86.675
## 26 84.112
## 27 3.030
## 28 88.629
## 29 98.927
## 30 40.865
## 31 43.967
## 32 51.136
## 33 128.000
## 34 33.593
## 35 140.587
## 36 63.636
## 37 127.836
## 38 114.597
## 39 130.086
## 40 105.508
## 41 105.099
## 42 16.129
## 43 98.799
## 44 57.918
## 45 105.919
## 46 112.121
## 47 93.311
## 48 100.715
## 49 161.058
## 50 59.305
## 51 125.437
## 52 78.667
## 53 93.352
## 54 75.795
## 55 36.364
## 56 85.079
## 57 128.201
tapply(exp3FM$Data.first3.M, exp3FM$Group.first3.M, sd)
## MPD MPD+IL1B Saline
## 55.25 52.36 24.44
tapply(exp3FM$Data.first3.M, exp3FM$Day.first3.M, sd)
## 1 2 3
## 64.82 39.19 34.01
exp3FM$Group.first3.M <- as.factor(exp3FM$Group.first3.M)
exp3FM$Day.first3.M <- as.factor(exp3FM$Day.first3.M)
exp3FM$Data.first3.M <- as.numeric(exp3FM$Data.first3.M)
aov.exp3FM = aov(Data.first3.M ~ Group.first3.M * Day.first3.M, data = exp3FM)
summary(aov.exp3FM)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.first3.M 2 3786 1893 0.76 0.47
## Day.first3.M 2 3955 1978 0.79 0.46
## Group.first3.M:Day.first3.M 4 662 166 0.07 0.99
## Residuals 48 119639 2492
comparison <- aov(Data.first3.M ~ Group.first3.M * Day.first3.M, data = exp3FM)
TukeyHSD(comparison, "Group.first3.M")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.first3.M ~ Group.first3.M * Day.first3.M, data = exp3FM)
##
## $Group.first3.M
## diff lwr upr p adj
## MPD+IL1B-MPD 11.205 -26.06 48.47 0.7486
## Saline-MPD -9.339 -50.16 31.48 0.8453
## Saline-MPD+IL1B -20.544 -61.36 20.27 0.4490
TukeyHSD(comparison, "Day.first3.M")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.first3.M ~ Group.first3.M * Day.first3.M, data = exp3FM)
##
## $Day.first3.M
## diff lwr upr p adj
## 2-1 -19.681 -58.86 19.49 0.4502
## 3-1 -14.504 -53.68 24.67 0.6458
## 3-2 5.178 -34.00 44.35 0.9453
print(model.tables(aov.exp3FM, "means"), digits = 19)
## Tables of means
## Grand mean
##
## 98.78
##
## Group.first3.M
## MPD MPD+IL1B Saline
## 97.11199366628571283 108.3169129242857309 87.77341124866666178
## rep 21.00000000000000000 21.0000000000000000 15.00000000000000000
##
## Day.first3.M
## 1 2 3
## 110.1774593378947458 90.49640570852632493 95.67393532894736552
## rep 19.0000000000000000 19.00000000000000000 19.00000000000000000
##
## Group.first3.M:Day.first3.M
## Day.first3.M
## Group.first3.M 1 2 3
## MPD 112.20 86.62 92.52
## rep 7.00 7.00 7.00
## MPD+IL1B 121.65 99.83 103.47
## rep 7.00 7.00 7.00
## Saline 91.28 82.86 89.17
## rep 5.00 5.00 5.00
exp3PM <- read.csv("~/Desktop/experiment 3 stats.csv")
exp3PM
## Group.first3 Day.first3 Data.first3 Group.post3 Day.post3 Data.post3
## 1 Saline 1 15.040 Saline 1 84.383
## 2 Saline 1 7.508 Saline 1 104.084
## 3 Saline 1 85.652 Saline 1 22.727
## 4 Saline 1 13.203 Saline 1 3.408
## 5 Saline 1 61.901 Saline 1 138.816
## 6 MPD 1 246.028 MPD 1 54.123
## 7 MPD 1 182.868 MPD 1 123.425
## 8 MPD 1 116.984 MPD 1 51.214
## 9 MPD 1 63.085 MPD 1 142.668
## 10 MPD 1 148.148 MPD 1 78.997
## 11 MPD 1 414.946 MPD 1 56.689
## 12 MPD 1 45.211 MPD 1 17.757
## 13 MPD+IL1B 1 322.047 MPD+IL1B 1 45.833
## 14 MPD+IL1B 1 1731.034 MPD+IL1B 1 8.621
## 15 MPD+IL1B 1 18.630 MPD+IL1B 1 34.996
## 16 MPD+IL1B 1 299.669 MPD+IL1B 1 92.308
## 17 MPD+IL1B 1 380.873 MPD+IL1B 1 83.696
## 18 MPD+IL1B 1 238.905 MPD+IL1B 1 150.074
## 19 MPD+IL1B 1 200.292 MPD+IL1B 1 0.000
## 20 Saline 2 52.009 Saline 2 134.687
## 21 Saline 2 56.223 Saline 2 56.773
## 22 Saline 2 86.384 Saline 2 181.818
## 23 Saline 2 2.986 Saline 2 163.583
## 24 Saline 2 45.623 Saline 2 193.336
## 25 MPD 2 89.144 MPD 2 49.666
## 26 MPD 2 95.361 MPD 2 41.523
## 27 MPD 2 63.836 MPD 2 58.052
## 28 MPD 2 105.766 MPD 2 123.346
## 29 MPD 2 96.334 MPD 2 88.471
## 30 MPD 2 111.603 MPD 2 2.268
## 31 MPD 2 40.783 MPD 2 31.222
## 32 MPD+IL1B 2 199.606 MPD+IL1B 2 17.361
## 33 MPD+IL1B 2 143.678 MPD+IL1B 2 91.855
## 34 MPD+IL1B 2 57.808 MPD+IL1B 2 31.934
## 35 MPD+IL1B 2 171.712 MPD+IL1B 2 161.538
## 36 MPD+IL1B 2 75.487 MPD+IL1B 2 69.182
## 37 MPD+IL1B 2 43.084 MPD+IL1B 2 180.783
## 38 MPD+IL1B 2 204.435 MPD+IL1B 2 31.333
## 39 Saline 3 48.839 Saline 3 158.468
## 40 Saline 3 55.556 Saline 3 62.998
## 41 Saline 3 125.183 Saline 3 81.169
## 42 Saline 3 85.193 Saline 3 0.000
## 43 Saline 3 109.513 Saline 3 118.799
## 44 MPD 3 37.401 MPD 3 30.564
## 45 MPD 3 66.346 MPD 3 89.347
## 46 MPD 3 51.977 MPD 3 87.636
## 47 MPD 3 157.993 MPD 3 111.699
## 48 MPD 3 105.686 MPD 3 49.106
## 49 MPD 3 146.018 MPD 3 13.605
## 50 MPD 3 51.918 MPD 3 77.686
## 51 MPD+IL1B 3 198.819 MPD+IL1B 3 4.167
## 52 MPD+IL1B 3 404.598 MPD+IL1B 3 28.537
## 53 MPD+IL1B 3 70.411 MPD+IL1B 3 86.614
## 54 MPD+IL1B 3 96.026 MPD+IL1B 3 82.906
## 55 MPD+IL1B 3 95.144 MPD+IL1B 3 47.654
## 56 MPD+IL1B 3 100.407 MPD+IL1B 3 9.658
## 57 MPD+IL1B 3 311.647 MPD+IL1B 3 135.020
## Group.first3.M Day.first3.M Data.first3.M Group.post3.M Day.post3.M
## 1 Saline 1 62.660 Saline 1
## 2 Saline 1 125.481 Saline 1
## 3 Saline 1 130.595 Saline 1
## 4 Saline 1 45.045 Saline 1
## 5 Saline 1 92.643 Saline 1
## 6 MPD 1 209.755 MPD 1
## 7 MPD 1 102.878 MPD 1
## 8 MPD 1 0.000 MPD 1
## 9 MPD 1 116.384 MPD 1
## 10 MPD 1 128.448 MPD 1
## 11 MPD 1 192.020 MPD 1
## 12 MPD 1 35.917 MPD 1
## 13 MPD+IL1B 1 259.983 MPD+IL1B 1
## 14 MPD+IL1B 1 163.293 MPD+IL1B 1
## 15 MPD+IL1B 1 89.915 MPD+IL1B 1
## 16 MPD+IL1B 1 146.285 MPD+IL1B 1
## 17 MPD+IL1B 1 57.944 MPD+IL1B 1
## 18 MPD+IL1B 1 62.202 MPD+IL1B 1
## 19 MPD+IL1B 1 71.925 MPD+IL1B 1
## 20 Saline 2 76.087 Saline 2
## 21 Saline 2 59.692 Saline 2
## 22 Saline 2 109.552 Saline 2
## 23 Saline 2 86.336 Saline 2
## 24 Saline 2 82.652 Saline 2
## 25 MPD 2 95.952 MPD 2
## 26 MPD 2 123.741 MPD 2
## 27 MPD 2 4.386 MPD 2
## 28 MPD 2 79.379 MPD 2
## 29 MPD 2 73.276 MPD 2
## 30 MPD 2 155.860 MPD 2
## 31 MPD 2 73.724 MPD 2
## 32 MPD+IL1B 2 65.972 MPD+IL1B 2
## 33 MPD+IL1B 2 63.428 MPD+IL1B 2
## 34 MPD+IL1B 2 62.745 MPD+IL1B 2
## 35 MPD+IL1B 2 169.505 MPD+IL1B 2
## 36 MPD+IL1B 2 103.738 MPD+IL1B 2
## 37 MPD+IL1B 2 80.466 MPD+IL1B 2
## 38 MPD+IL1B 2 152.941 MPD+IL1B 2
## 39 Saline 3 65.217 Saline 3
## 40 Saline 3 86.650 Saline 3
## 41 Saline 3 82.164 Saline 3
## 42 Saline 3 105.105 Saline 3
## 43 Saline 3 106.721 Saline 3
## 44 MPD 3 69.653 MPD 3
## 45 MPD 3 77.698 MPD 3
## 46 MPD 3 43.860 MPD 3
## 47 MPD 3 112.147 MPD 3
## 48 MPD 3 109.483 MPD 3
## 49 MPD 3 169.576 MPD 3
## 50 MPD 3 65.217 MPD 3
## 51 MPD+IL1B 3 49.479 MPD+IL1B 3
## 52 MPD+IL1B 3 89.069 MPD+IL1B 3
## 53 MPD+IL1B 3 78.287 MPD+IL1B 3
## 54 MPD+IL1B 3 166.409 MPD+IL1B 3
## 55 MPD+IL1B 3 98.131 MPD+IL1B 3
## 56 MPD+IL1B 3 115.934 MPD+IL1B 3
## 57 MPD+IL1B 3 127.005 MPD+IL1B 3
## Data.post3.M
## 1 28.360
## 2 155.159
## 3 165.973
## 4 42.184
## 5 95.524
## 6 103.078
## 7 105.919
## 8 3.030
## 9 118.729
## 10 66.746
## 11 74.519
## 12 31.697
## 13 64.685
## 14 50.667
## 15 43.140
## 16 92.910
## 17 53.409
## 18 64.572
## 19 6.722
## 20 32.059
## 21 95.811
## 22 66.077
## 23 89.330
## 24 157.205
## 25 86.675
## 26 84.112
## 27 3.030
## 28 88.629
## 29 98.927
## 30 40.865
## 31 43.967
## 32 51.136
## 33 128.000
## 34 33.593
## 35 140.587
## 36 63.636
## 37 127.836
## 38 114.597
## 39 130.086
## 40 105.508
## 41 105.099
## 42 16.129
## 43 98.799
## 44 57.918
## 45 105.919
## 46 112.121
## 47 93.311
## 48 100.715
## 49 161.058
## 50 59.305
## 51 125.437
## 52 78.667
## 53 93.352
## 54 75.795
## 55 36.364
## 56 85.079
## 57 128.201
tapply(exp3PM$Data.post3.M, exp3PM$Group.post3.M, sd)
## MPD MPD+IL1B Saline
## 38.82 37.51 47.92
tapply(exp3PM$Data.post3.M, exp3PM$Day.post3.M, sd)
## 1 2 3
## 44.88 41.31 34.38
exp3PM$Group.post3.M <- as.factor(exp3PM$Group.post3.M)
exp3PM$Day.post3.M <- as.factor(exp3PM$Day.post3.M)
exp3PM$Data.post3.M <- as.numeric(exp3PM$Data.post3.M)
aov.exp3PM = aov(Data.post3.M ~ Group.post3.M * Day.post3.M, data = exp3PM)
summary(aov.exp3PM)
## Df Sum Sq Mean Sq F value Pr(>F)
## Group.post3.M 2 2076 1038 0.63 0.54
## Day.post3.M 2 4266 2133 1.30 0.28
## Group.post3.M:Day.post3.M 4 7401 1850 1.13 0.35
## Residuals 48 78763 1641
comparison <- aov(Data.post3.M ~ Group.post3.M * Day.post3.M, data = exp3PM)
TukeyHSD(comparison, "Group.post3.M")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.post3.M ~ Group.post3.M * Day.post3.M, data = exp3PM)
##
## $Group.post3.M
## diff lwr upr p adj
## MPD+IL1B-MPD 0.8625 -29.37 31.10 0.9974
## Saline-MPD 14.1120 -19.01 47.23 0.5615
## Saline-MPD+IL1B 13.2496 -19.87 46.37 0.6007
TukeyHSD(comparison, "Day.post3.M")
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Data.post3.M ~ Group.post3.M * Day.post3.M, data = exp3PM)
##
## $Day.post3.M
## diff lwr upr p adj
## 2-1 9.424 -22.36 41.21 0.7547
## 3-1 21.149 -10.64 52.93 0.2516
## 3-2 11.726 -20.06 43.51 0.6478
print(model.tables(aov.exp3PM, "means"), digits = 19)
## Tables of means
## Grand mean
##
## 82.14
##
## Group.post3.M
## MPD MPD+IL1B Saline
## 78.10819918142858853 78.9706543780000203 92.22022040066673298
## rep 21.00000000000000000 21.0000000000000000 15.00000000000000000
##
## Day.post3.M
## 1 2 3
## 71.94863981042109913 81.3722575284210734 93.09800954315791444
## rep 19.00000000000000000 19.0000000000000000 19.00000000000000000
##
## Group.post3.M:Day.post3.M
## Day.post3.M
## Group.post3.M 1 2 3
## MPD 71.96 63.74 98.62
## rep 7.00 7.00 7.00
## MPD+IL1B 53.73 94.20 88.98
## rep 7.00 7.00 7.00
## Saline 97.44 88.10 91.12
## rep 5.00 5.00 5.00