1. SV vs. SR all factors
# 1.1 Agonistic Behaviors(Day2)
# 1.1.1 FT
#boxplot
p <- ggplot(df, aes(x=Stress, y=FT2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/Homogeneous of Variance/block o
levene.test(df$FT2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$FT2
## Test Statistic = 0.60036, p-value = 0.4421
summary(aov(FT2~Stress+paste(unique_pen,Litter), data=df)) ## block combined
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 31853 31853 0.703 0.42341
## paste(unique_pen, Litter) 41 10408197 253858 5.604 0.00469 **
## Residuals 9 407663 45296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lmm<-lmer(FT2~Stress+(1|unique_pen)+(1|Litter),data=df)
summary(lmm)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FT2 ~ Stress + (1 | unique_pen) + (1 | Litter)
## Data: df
##
## REML criterion at convergence: 753.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.82760 -0.36596 -0.06748 0.24232 2.35293
##
## Random effects:
## Groups Name Variance Std.Dev.
## Litter (Intercept) 59370 243.7
## unique_pen (Intercept) 103277 321.4
## Residual 73994 272.0
## Number of obs: 52, groups: Litter, 26; unique_pen, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 313.47 105.56 28.64 2.969 0.00598 **
## StressSV 50.43 89.92 17.83 0.561 0.58188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## StressSV -0.433
# 1.1.2 AB(INC+BN)
#boxplot
p <- ggplot(df, aes(x=Stress, y=AB2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/Homogeneous of Variance/lmm check
levene.test(df$AB2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$AB2
## Test Statistic = 0.66537, p-value = 0.4185
summary(aov(AB2~Stress+paste(unique_pen,Litter), data=df))
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 185 184.69 2.006 0.190
## paste(unique_pen, Litter) 41 7066 172.34 1.871 0.159
## Residuals 9 829 92.09
lmm<-lmer(AB2~Stress+(1|unique_pen)+(1|Litter),data=df) #lmm check
summary(lmm)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AB2 ~ Stress + (1 | unique_pen) + (1 | Litter)
## Data: df
##
## REML criterion at convergence: 385.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6869 -0.4490 -0.0859 0.3565 3.2969
##
## Random effects:
## Groups Name Variance Std.Dev.
## Litter (Intercept) 107.92 10.388
## unique_pen (Intercept) 0.00 0.000
## Residual 49.97 7.069
## Number of obs: 52, groups: Litter, 26; unique_pen, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.346 2.464 34.079 4.198 0.000182 ***
## StressSV 3.769 1.961 25.000 1.922 0.066012 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## StressSV -0.398
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
# 1.2 Non-Agonistic Behaviors(Day2)
# 1.2.1 NC
#boxplot
p <- ggplot(df, aes(x=Stress, y=NC2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/block o & stress o *****
levene.test(df$NC2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$NC2
## Test Statistic = 0.74617, p-value = 0.3918
summary(aov(NC2~Stress+paste(unique_pen,Litter), data=df)) #block o & stress o *****
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 9694 9694 8.001 0.0198 *
## paste(unique_pen, Litter) 41 178396 4351 3.591 0.0230 *
## Residuals 9 10905 1212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lmm<-lmer(NC2~Stress+(1|unique_pen)+(1|Litter),data=df)
summary(lmm)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NC2 ~ Stress + (1 | unique_pen) + (1 | Litter)
## Data: df
##
## REML criterion at convergence: 547.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.37766 -0.51128 -0.04896 0.25844 3.15519
##
## Random effects:
## Groups Name Variance Std.Dev.
## Litter (Intercept) 238 15.43
## unique_pen (Intercept) 2100 45.82
## Residual 1559 39.48
## Number of obs: 52, groups: Litter, 26; unique_pen, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 87.18 13.73 29.45 6.348 5.74e-07 ***
## StressSV 16.32 12.76 21.33 1.278 0.215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## StressSV -0.471
# 1.3 Daily Behaviors(Day2)
# 1.3.1 SLP -zero inflation
#boxplot
p <- ggplot(df, aes(x=Stress, y=SLP2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/unequal-variance/too many zeros - anova block, factor o
levene.test(df$SLP2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$SLP2
## Test Statistic = 5.3523, p-value = 0.02485
summary(aov(SLP2~Stress+paste(unique_pen,Litter), data=df)) ##unequal
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 246 245.6 15.16 0.003656 **
## paste(unique_pen, Litter) 41 8711 212.5 13.12 0.000159 ***
## Residuals 9 146 16.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
oneway.test(SLP2~Stress, data=df)
##
## One-way analysis of means (not assuming equal variances)
##
## data: SLP2 and Stress
## F = 1.3862, num df = 1.000, denom df = 25.094, p-value = 0.2501
# 1.3.2 SP
#boxplot
p <- ggplot(df, aes(x=Stress, y=SP2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/zero-inflated
levene.test(df$SP2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$SP2
## Test Statistic = 1.5379, p-value = 0.2207
summary(aov(SP2~Stress+paste(unique_pen,Litter), data=df)) ## Stress, block o
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 7416 7416 9.804 0.0121 *
## paste(unique_pen, Litter) 41 622167 15175 20.061 2.63e-05 ***
## Residuals 9 6808 756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.3.3 FE
#boxplot
p <- ggplot(df, aes(x=Stress, y=FE2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$FE2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$FE2
## Test Statistic = 2.9629, p-value = 0.09138
summary(aov(FE2~Stress+paste(unique_pen,Litter), data=df)) ## x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 119904 119904 0.204 0.662
## paste(unique_pen, Litter) 41 64353598 1569600 2.674 0.059 .
## Residuals 9 5281914 586879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.3.4 LD
#boxplot
p <- ggplot(df, aes(x=Stress, y=LD2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$LD2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$LD2
## Test Statistic = 0.0099597, p-value = 0.9209
summary(aov(LD2~Stress+paste(unique_pen,Litter), data=df)) # block o, Stress o
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 5235577 5235577 8.337 0.01796 *
## paste(unique_pen, Litter) 41 156732131 3822735 6.087 0.00344 **
## Residuals 9 5651786 627976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.3.5 EX
#boxplot
p <- ggplot(df, aes(x=Stress, y=EX2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$EX2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$EX2
## Test Statistic = 2.4928, p-value = 0.1207
summary(aov(EX2~Stress+paste(unique_pen,Litter), data=df)) #x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 1151176 1151176 0.826 0.387
## paste(unique_pen, Litter) 41 62053007 1513488 1.086 0.483
## Residuals 9 12538618 1393180
# 1.3.6 DR
#boxplot
p <- ggplot(df, aes(x=Stress, y=DR2)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/ block,Stress x
levene.test(df$DR2,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$DR2
## Test Statistic = 0.32203, p-value = 0.5729
summary(aov(DR2~Stress+paste(unique_pen,Litter), data=df)) #x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 32.3 32.33 0.256 0.625
## paste(unique_pen, Litter) 41 2668.5 65.09 0.515 0.928
## Residuals 9 1137.8 126.42
##################################################
# 1.4 Agonistic Behaviors(Day5)
# 1.4.1 FT
#boxplot
p <- ggplot(df, aes(x=Stress, y=FT5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$FT5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$FT5
## Test Statistic = 0.15955, p-value = 0.6913
summary(aov(FT5~Stress+paste(unique_pen,Litter), data=df)) ## stress,block o
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 3233 3233 7.262 0.0246 *
## paste(unique_pen, Litter) 41 77660 1894 4.255 0.0128 *
## Residuals 9 4006 445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.4.2 AB
#boxplot
p <- ggplot(df, aes(x=Stress, y=AB5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$FT5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$FT5
## Test Statistic = 0.15955, p-value = 0.6913
summary(aov(AB5~Stress+paste(unique_pen,Litter), data=df)) ## x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 277 276.92 3.087 0.1128
## paste(unique_pen, Litter) 41 9171 223.67 2.493 0.0727 .
## Residuals 9 807 89.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.5 Non-Agonistic Behaviors(Day5)
# 1.5.1 NC
#boxplot
p <- ggplot(df, aes(x=Stress, y=NC5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$NC5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$NC5
## Test Statistic = 2.2443, p-value = 0.1404
summary(aov(NC5~Stress+paste(unique_pen,Litter), data=df)) #x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 438 438.5 0.542 0.4803
## paste(unique_pen, Litter) 41 73996 1804.8 2.231 0.0999 .
## Residuals 9 7280 808.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.6 Daily Behaviors(Day2)
# 1.6.1 SLP -zero inflation
#boxplot
p <- ggplot(df, aes(x=Stress, y=SLP5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/unequal-variance/too many zeros
levene.test(df$SLP5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$SLP5
## Test Statistic = 10.203, p-value = 0.002428
summary(aov(SLP5~Stress+paste(unique_pen,Litter), data=df)) ##unequal
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 96.9 96.94 4.340 0.0669 .
## paste(unique_pen, Litter) 41 1513.0 36.90 1.652 0.2149
## Residuals 9 201.1 22.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
oneway.test(SLP5~Stress, data=df) # x
##
## One-way analysis of means (not assuming equal variances)
##
## data: SLP5 and Stress
## F = 2.8279, num df = 1.000, denom df = 28.682, p-value = 0.1035
# 1.6.2 SP - zero inflated
#boxplot
p <- ggplot(df, aes(x=Stress, y=SP5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance
levene.test(df$SP5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$SP5
## Test Statistic = 2.1816, p-value = 0.1459
summary(aov(SP5~Stress+paste(unique_pen,Litter), data=df)) #Stress, block
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 746 746.3 20.48 0.00144 **
## paste(unique_pen, Litter) 41 54744 1335.2 36.63 1.94e-06 ***
## Residuals 9 328 36.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.6.3 FE
#boxplot
p <- ggplot(df, aes(x=Stress, y=FE5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$FE5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$FE5
## Test Statistic = 3.7686, p-value = 0.05787
summary(aov(FE5~Stress+paste(unique_pen,Litter), data=df)) #x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 497646 497646 0.327 0.582
## paste(unique_pen, Litter) 41 45760767 1116116 0.733 0.764
## Residuals 9 13707893 1523099
# 1.6.4 LD
#boxplot
p <- ggplot(df, aes(x=Stress, y=LD5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/block o
levene.test(df$LD5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$LD5
## Test Statistic = 0.62986, p-value = 0.4312
summary(aov(LD5~Stress+paste(unique_pen,Litter), data=df)) # x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 772261 772261 0.328 0.5811
## paste(unique_pen, Litter) 41 219313264 5349104 2.269 0.0953 .
## Residuals 9 21217917 2357546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 1.6.5 EX
#boxplot
p <- ggplot(df, aes(x=Stress, y=EX5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/block o
levene.test(df$EX5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$EX5
## Test Statistic = 0.83665, p-value = 0.3647
summary(aov(EX5~Stress+paste(unique_pen,Litter), data=df)) #x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 18545 18545 0.044 0.838
## paste(unique_pen, Litter) 41 23769241 579738 1.387 0.313
## Residuals 9 3761363 417929
# 1.6.6 DR
#boxplot
p <- ggplot(df, aes(x=Stress, y=DR5)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=2)
p
#blocked anova test/equal-variance/
levene.test(df$DR5,df$Stress, location="mean")
##
## Classical Levene's test based on the absolute deviations from the mean
## ( none not applied because the location is not set to median )
##
## data: df$DR5
## Test Statistic = 0.039832, p-value = 0.8426
summary(aov(DR5~Stress+paste(unique_pen,Litter), data=df)) #x
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 0.1 0.08 0.003 0.9591
## paste(unique_pen, Litter) 41 2655.8 64.78 2.337 0.0877 .
## Residuals 9 249.5 27.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
2. SV vs. SR all factors with ages
# 2.1
# 2.1.1 FT *** interaction x, stress x, block o, age o
summary(aov(FT~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 27690 27690 1.092 0.32338
## paste(unique_pen, Litter) 41 5450083 132929 5.240 0.00603 **
## Residuals 9 228312 25368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 2024145 2024145 19.392 5.61e-05 ***
## Stress:Age 1 7395 7395 0.071 0.791
## Residuals 50 5219130 104383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$FT,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of FT", trace.label = "Stress", legend = F)
legend("topright",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=FT, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("FT distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
# 2.1.2 INC *** interaction x, stress x, block o, age x
summary(aov(AB~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 5 4.65 0.062 0.8085
## paste(unique_pen, Litter) 41 11013 268.61 3.595 0.0229 *
## Residuals 9 673 74.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 42 41.9 0.338 0.5633
## Stress:Age 1 457 457.0 3.693 0.0604 .
## Residuals 50 6187 123.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$INC,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of INC", trace.label = "Stress", legend = F)
legend("topleft",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=INC, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("INC distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
# 2.2
# 2.2.1 NC *interaction x, stress o, block o, age o
summary(aov(NC~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 7128 7128 12.193 0.00681 **
## paste(unique_pen, Litter) 41 168461 4109 7.028 0.00198 **
## Residuals 9 5261 585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 12916 12916 6.668 0.0128 *
## Stress:Age 1 3005 3005 1.551 0.2188
## Residuals 50 96855 1937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$NC,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of NC", trace.label = "Stress", legend = F)
legend("topright",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=NC, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("NC distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
#2.3
#2.3.1 SLP *interaction x, stress x, block o, age x - zero-inflated
summary(aov(SLP~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 17 16.96 0.786 0.39828
## paste(unique_pen, Litter) 41 5037 122.86 5.696 0.00442 **
## Residuals 9 194 21.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 5 4.7 0.044 0.835
## Stress:Age 1 326 325.5 3.048 0.087 .
## Residuals 50 5340 106.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$SLP,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of SLP", trace.label = "Stress", legend = F)
legend("topright",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=SLP, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("SLP distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
#2.3.2 SP *interaction x, stress o, block o, age x - zero-inflated
summary(aov(SP~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 6434 6434 11.44 0.0081 **
## paste(unique_pen, Litter) 41 428068 10441 18.56 3.67e-05 ***
## Residuals 9 5062 562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 17889 17889 3.565 0.0648 .
## Stress:Age 1 1729 1729 0.344 0.5599
## Residuals 50 250917 5018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$SP,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of SP", trace.label = "Stress", legend = F)
legend("topright",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=SP, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("SP distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
#2.3.3 FE *interaction x, stress x, block x, age o
summary(aov(FE~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 553049 553049 0.374 0.556
## paste(unique_pen, Litter) 41 58410600 1424649 0.963 0.573
## Residuals 9 13315160 1479462
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 10261521 10261521 8.942 0.00432 **
## Stress:Age 1 64501 64501 0.056 0.81356
## Residuals 50 57378412 1147568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$FE,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of FE", trace.label = "Stress", legend = F)
legend("topleft",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=FE, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("FE distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
#2.3.4 LD *interaction x, stress x, block o, age o
summary(aov(LD~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 5014698 5014698 3.441 0.0966 .
## paste(unique_pen, Litter) 41 231991238 5658323 3.882 0.0176 *
## Residuals 9 13116668 1457408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 51756891 51756891 16.399 0.000178 ***
## Stress:Age 1 993140 993140 0.315 0.577335
## Residuals 50 157807192 3156144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$LD,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of LD", trace.label = "Stress", legend = F)
legend("topleft",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=LD, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("LD distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
#2.3.5 EX *interaction x, stress x, block x, age o
summary(aov(EX~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 730971 730971 0.866 0.376
## paste(unique_pen, Litter) 41 60268276 1469958 1.741 0.190
## Residuals 9 7598516 844280
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 9955378 9955378 14.53 0.000379 ***
## Stress:Age 1 438750 438750 0.64 0.427349
## Residuals 50 34255437 685109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$EX,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of EX", trace.label = "Stress", legend = F)
legend("topright",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=EX, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("EX distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
#2.3.6 DR *interaction x, stress x, block x, age o
summary(aov(DR~Stress*Age+paste(unique_pen,Litter)+Error(ID/Age),data=df2))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Stress 1 18 17.78 0.250 0.629
## paste(unique_pen, Litter) 41 3539 86.33 1.212 0.403
## Residuals 9 641 71.23
##
## Error: ID:Age
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 1211.8 1211.8 23.938 1.08e-05 ***
## Stress:Age 1 14.6 14.6 0.289 0.593
## Residuals 50 2531.1 50.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
interaction.plot(df2$Age,df2$Stress,df2$DR,
fun=mean,type="b",pch=c(2,4),col=c(3,4),xlab="Time",ylab="Mean of DR", trace.label = "Stress", legend = F)
legend("topright",legend=c("SR","SV"),pch=c(2,4),col=c(3,4), bg="gray90")
ggplot(df2, aes(x=Stress, y=DR, color=as.factor(Age)))+
geom_boxplot()+
ggtitle("DR distribution by Stress and Age")+
theme(plot.title=element_text(face="bold", hjust=0.5))+
scale_color_discrete(name="Age")
