dta <- read.csv("NewP.csv",h=T)
dta <- na.omit(dta)
dta <- dta[-52,]
row.names(dta) <- 1:dim(dta)[1]
summary(Vp <- aov(Valence~Condition,data =dta)) # p <0.05 *
Df Sum Sq Mean Sq F value Pr(>F)
Condition 3 4.76 1.5865 2.733 0.0495 *
Residuals 76 44.12 0.5805
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Vp)$Condition[c(2,4,6),] #
diff lwr upr p adj
control-bite 0.03333333 -0.5995362 0.66620285 0.9990466
control-clip 0.43333333 -0.1995362 1.06620285 0.2820101
pout-control -0.55833333 -1.1912029 0.07453618 0.1030589
#SNKvp <- SNK.test(Vp,"Condition",df.residual(Vp),deviance(Vp)/df.residual(Vp),group=T)
#Schvp <- scheffe.test(Vp,"Condition",df.residual(Vp),deviance(Vp)/df.residual(Vp),group=T)
#pairwise.t.test(dta$Valence,dta$Condition,,p.adj = "bonf")
summary(Ap <- aov(Arousal~Condition,data =dta))
Df Sum Sq Mean Sq F value Pr(>F)
Condition 3 5.04 1.679 1.005 0.395
Residuals 76 126.96 1.671
#TukeyHSD(Ap)$Condition[c(2,4,6),]
summary(Tp <- aov(Typicality~Condition,data =dta))# p <0.05 *
Df Sum Sq Mean Sq F value Pr(>F)
Condition 3 7.88 2.6263 3.4 0.022 *
Residuals 76 58.70 0.7724
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Tp)$Condition[c(2,4,6),]
diff lwr upr p adj
control-bite -0.3416667 -1.0717020 0.3883686 0.6102870
control-clip 0.1916667 -0.5383686 0.9217020 0.9007474
pout-control -0.5250000 -1.2550353 0.2050353 0.2413612
dta2 <- read.csv("newN.csv",h=T)
dta2<- na.omit(dta2)
dta2 <- dta2[-52,] # For equal subjects
row.names(dta2) <- 1:dim(dta2)[1]
summary(VN <- aov(Valence~Condition,data =dta2))
Df Sum Sq Mean Sq F value Pr(>F)
Condition 3 2.642 0.8808 3.321 0.0242 *
Residuals 76 20.155 0.2652
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(VN)$Condition[c(2,4,6),] # Clip,Pout > Control
diff lwr upr p adj
control-bite -0.355 -0.782765429 0.07276543 0.13813064
control-clip -0.450 -0.877765429 -0.02223457 0.03537983
pout-control 0.430 0.002234571 0.85776543 0.04832634
summary(AN <- aov(Arousal~Condition,data =dta2))
Df Sum Sq Mean Sq F value Pr(>F)
Condition 3 1.05 0.3515 0.206 0.892
Residuals 76 129.94 1.7098
#TukeyHSD(AN)$Condition[c(2,4,6),]
summary(TN <- aov(Typicality~Condition,data =dta2))
Df Sum Sq Mean Sq F value Pr(>F)
Condition 3 9.15 3.051 2.494 0.0663 .
Residuals 76 92.98 1.223
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(TN)$Condition[c(2,4,6),]
diff lwr upr p adj
control-bite 0.0025 -0.9162719 0.92127194 0.99999987
control-clip 0.3600 -0.5587719 1.27877194 0.73288466
pout-control -0.8250 -1.7437719 0.09377194 0.09405213
VP_se <- summarySE(dta,measurevar = "Valence",groupvars = "Condition" )
VP_se$Dimension <- "Valence"
colnames(VP_se)[3] <- "Rating"
VP_se$Type <- "Positive"
AP_se <- summarySE(dta,measurevar = "Arousal",groupvars = "Condition" )
AP_se$Dimension <- "Arousal"
AP_se$Type <- "Positive"
colnames(AP_se)[3] <- "Rating"
Tp_se <- summarySE(dta,measurevar = "Typicality",groupvars = "Condition" )
Tp_se$Dimension <- "Typicality"
Tp_se$Type <- "Positive"
colnames(Tp_se)[3] <- "Rating"
PSE <- rbind(VP_se,AP_se,Tp_se)
VN_se <- summarySE(dta2,measurevar = "Valence",groupvars = "Condition" )
VN_se$Dimension <- "Valence"
colnames(VN_se)[3] <- "Rating"
VN_se$Type <- "Negative"
AN_se <- summarySE(dta2,measurevar = "Arousal",groupvars = "Condition" )
AN_se$Dimension <- "Arousal"
AN_se$Type <- "Negative"
colnames(AN_se)[3] <- "Rating"
TN_se <- summarySE(dta2,measurevar = "Typicality",groupvars = "Condition" )
TN_se$Dimension <- "Typicality"
TN_se$Type <- "Negative"
colnames(TN_se)[3] <- "Rating"
NSE <- rbind(VN_se,AN_se,TN_se)
dta_se <- rbind(PSE,NSE)
str(dta_se)
'data.frame': 24 obs. of 8 variables:
$ Condition: Factor w/ 4 levels "bite","clip",..: 1 2 3 4 1 2 3 4 1 2 ...
$ N : num 20 20 20 20 20 20 20 20 20 20 ...
$ Rating : num 8.03 7.63 8.07 7.51 6.4 ...
$ sd : num 0.657 0.876 0.657 0.832 1.366 ...
$ se : num 0.147 0.196 0.147 0.186 0.306 ...
$ ci : num 0.307 0.41 0.307 0.389 0.639 ...
$ Dimension: chr "Valence" "Valence" "Valence" "Valence" ...
$ Type : chr "Positive" "Positive" "Positive" "Positive" ...
dta_se$Condition <- factor(dta_se$Condition,
levels = c("control","bite","pout","clip"),
labels = c("Control","Bite","Pout","Clip"))
dta_se$Type <- factor(dta_se$Type,
levels = c("Positive","Negative"))
dta_se$Dimension <- factor(dta_se$Dimension,
levels = c("Valence","Arousal","Typicality"))
ann_text <- data.frame(Condition = c("Pout","Clip"), Rating = c(4,4),lab = "test",
Type = factor("Negative",levels = c("Positive","Negative")),
Dimension = factor("Valence",levels = c("Valence","Arousal","Typicality")))
ggplot(data = dta_se,aes(x = Condition,y = Rating, col = Condition,shape = Condition))+
facet_grid(Type~Dimension)+
geom_point(size=2)+
geom_errorbar(aes(x=Condition,ymin = Rating-se,ymax = Rating +se),
width=0.1,show.legend = FALSE)+
theme_bw()+
scale_colour_manual(values = c("black","grey25","grey45","grey65"))+
scale_shape_manual(values = c(15,16,17,18))+
geom_text(data = ann_text,label = "*",size = 7,show.legend = FALSE)+
labs(list(y= "Rating Scores"))
dta$Type <- "Positive"
dta2$Type <- "Negative"
dtaall <- rbind(dta,dta2)
summary(v1 <- aov(Valence~Type*Condition,data =dtaall))
Df Sum Sq Mean Sq F value Pr(>F)
Type 1 1011.7 1011.7 2392.703 < 2e-16 ***
Condition 3 1.1 0.4 0.861 0.46293
Type:Condition 3 6.3 2.1 4.974 0.00255 **
Residuals 152 64.3 0.4
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(v1,"Type")
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Valence ~ Type * Condition, data = dtaall)
$Type
diff lwr upr p adj
Positive-Negative 5.029167 4.826038 5.232295 0
#with(dtaall,interaction.plot(x.factor = "Condition",trace.factor = "Type",response="Valence",ylim = c(1,9)))
summary(aov(Arousal~Type*Condition,data =dtaall))
Df Sum Sq Mean Sq F value Pr(>F)
Type 1 4.89 4.894 2.896 0.0909 .
Condition 3 1.61 0.537 0.318 0.8126
Type:Condition 3 4.48 1.493 0.884 0.4511
Residuals 152 256.90 1.690
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(aov(Typicality~Type*Condition,data =dtaall))
Df Sum Sq Mean Sq F value Pr(>F)
Type 1 94.53 94.53 94.73 < 2e-16 ***
Condition 3 16.32 5.44 5.45 0.00138 **
Type:Condition 3 0.72 0.24 0.24 0.86857
Residuals 152 151.68 1.00
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1