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r:twoway_anova

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r:twoway_anova [2017/11/06 09:31] – [e.g., 6] hkimscilr:twoway_anova [2018/10/19 08:39] (current) – [e.g. 5] hkimscil
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 +data sample: {{:r:diet.csv}}
 +
 ====== Twoway ANOVA ====== ====== Twoway ANOVA ======
 recap of oneway ANOVA recap of oneway ANOVA
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 45 Carrier 3    Office 5 15.04 45 Carrier 3    Office 5 15.04
  
-> library(ggplot2) +> library("ggpubr") 
-> ggplot(delivery.df, aes(Time, Destination, colour = Service)) + geom_point() +> ggline(delivery.df, x = "Destination", y = "Time", color = "Service", add = c("mean_se", "dotplot"))
-> +
 </code> </code>
  
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 [{{:r:interaction effect eg04.png|Interaction effect example 4}}] [{{:r:interaction effect eg04.png|Interaction effect example 4}}]
  
-===== 5 =====+===== e.g., 5 =====
 download {{:r:dataset_anova_twoWay_comparisons.csv|dataset_anova_twoWay_interactions.csv}}  download {{:r:dataset_anova_twoWay_comparisons.csv|dataset_anova_twoWay_interactions.csv}} 
  
-<code>data <- read.csv("http://commres.net/wiki/_media/r/dataset_anova_twoway_comparisons.csv")+<code>stressdata <- read.csv("http://commres.net/wiki/_media/r/dataset_anova_twoway_comparisons.csv")
 > #display the data > #display the data
-> data+> stressdata
    Treatment   Age StressReduction    Treatment   Age StressReduction
 1     mental young              10 1     mental young              10
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 27   medical   old               0</code> 27   medical   old               0</code>
  
-<code>> aov(d$StressReduction~d$Treatment*d$Age, d) +<code> 
-Call: +> stressdata <- read.csv("http://commres.net/wiki/_media/r/dataset_anova_twoway_comparisons.csv") 
-   aov(formula = d$StressReduction ~ d$Treatment * d$Age, data = d) +>  
- +> a.mod <- aov(StressReduction~Treatment*Age, data=stressdata)
-Terms: +
-                d$Treatment d$Age d$Treatment:d$Age Residuals +
-Sum of Squares           18   162                 0        18 +
-Deg. of Freedom           2     2                 4        18 +
- +
-Residual standard error: 1 +
-Estimated effects may be unbalanced +
-> a.mod <- aov(d$StressReduction~d$Treatment*d$Age, d)+
 > summary(a.mod) > summary(a.mod)
-                  Df Sum Sq Mean Sq F value  Pr(>F)     +              Df Sum Sq Mean Sq F value  Pr(>F)     
-d$Treatment        2     18       9       9 0.00195 **  +Treatment      2     18       9       9 0.00195 **  
-d$Age              2    162      81      81   1e-09 *** +Age            2    162      81      81   1e-09 *** 
-d$Treatment:d$Age  4      0       0       0 1.00000     +Treatment:Age  4      0       0       0 1.00000     
-Residuals         18     18       1                    +Residuals     18     18       1                    
 --- ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-></code> +>  
 +</code> 
  
-<code>> pairwise.t.test(d$StressReduction, d$Treatment, p.adj = "none")+<code>> TukeyHSD(a.mod, which="Treatment") 
 +  Tukey multiple comparisons of means 
 +    95% family-wise confidence level
  
- Pairwise comparisons using t tests with pooled SD +Fit: aov(formula = StressReduction ~ Treatment * Age, data = stressdata)
  
-data:  d$StressReduction and d$Treatment +$Treatment 
 +                 diff        lwr       upr     p adj 
 +mental-medical      2  0.7968988 3.2031012 0.0013531 
 +physical-medical    1 -0.2031012 2.2031012 0.1135025 
 +physical-mental    -1 -2.2031012 0.2031012 0.1135025
  
-         medical mental +>  
-mental   0.13    -      +</code>  
-physical 0.45    0.45  +<code> 
 +> TukeyHSD(a.mod, which="Age") 
 +  Tukey multiple comparisons of means 
 +    95% family-wise confidence level
  
-P value adjustment method: none</code>  +Fit: aov(formula = StressReduction ~ Treatment * Age, data = stressdata)
-<code>> pairwise.t.test(d$StressReduction, d$Age, p.adj = "none")+
  
- Pairwise comparisons using t tests with pooled SD +$Age 
 +          diff       lwr       upr    p adj 
 +old-mid     -3 -4.203101 -1.796899 1.54e-05 
 +young-mid    3  1.796899  4.203101 1.54e-05 
 +young-old    6  4.796899  7.203101 0.00e+00 
 +> 
 +</code> 
  
-data:  d$StressReduction and d$Age  
- 
-      mid     old     
-old   2.5e-05 -       
-young 2.5e-05 2.3e-10 
- 
-P value adjustment method: none  
->  
-</code> 
  
 <WRAP info>위는 아래의 linear model 을 이용하여도 가능. 사실 모든 ANOVA 테스트는 linear model이기도 함(lm) <WRAP info>위는 아래의 linear model 을 이용하여도 가능. 사실 모든 ANOVA 테스트는 linear model이기도 함(lm)
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 </code> </code>
  
-<code>> pairwise.t.test(d$StressReduction, d$Treatment, p.adj = "none") 
  
- Pairwise comparisons using t tests with pooled SD +===== e.g., 6 =====
  
-data:  d$StressReduction and d$Treatment  
- 
-         medical mental 
-mental   0.13    -      
-physical 0.45    0.45   
- 
-P value adjustment method: none</code>  
-<code>> pairwise.t.test(d$StressReduction, d$Age, p.adj = "none") 
- 
- Pairwise comparisons using t tests with pooled SD  
- 
-data:  d$StressReduction and d$Age  
- 
-      mid     old     
-old   2.5e-05 -       
-young 2.5e-05 2.3e-10 
- 
-P value adjustment method: none  
->  
-</code> 
- 
- 
-===== e.g., 6 ===== 
 <code>tdata <- ToothGrowth <code>tdata <- ToothGrowth
 tdata tdata
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 table(tdata$supp, tdata$dose) table(tdata$supp, tdata$dose)
 </code> </code>
 +
 +==== Visualize the data ====
  
 <code>install.packages("ggpubr") <code>install.packages("ggpubr")
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 </code> </code>
 +
 +==== Compute ANOVA test ====
  
 <code>res.aov2 <- aov(len ~ supp + dose, data = tdata) <code>res.aov2 <- aov(len ~ supp + dose, data = tdata)
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   * the p-value for the interaction between supp*dose is 0.02 (significant), which indicates that the relationships between dose and tooth length depends on the supp method.   * the p-value for the interaction between supp*dose is 0.02 (significant), which indicates that the relationships between dose and tooth length depends on the supp method.
  
 +==== pair-wise test ====
  
 <code>> TukeyHSD(res.aov3, which = "dose") <code>> TukeyHSD(res.aov3, which = "dose")
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 d2-dh 15.495 12.727488 18.262512 0.0e+00 d2-dh 15.495 12.727488 18.262512 0.0e+00
 d2-d1  6.365  3.597488  9.132512 2.7e-06 d2-d1  6.365  3.597488  9.132512 2.7e-06
 +</code>
 +
 +<code>pairwise.t.test(tdata$len, tdata$dose, p.adjust.method = "BH")
 +</code>
 +==== Homogeneity ====
 +
 +<code>plot(res.aov3, 1)</code>
 +
 +<code>install.packages("car")
 +library(car)
 +leveneTest(len ~ supp*dose, data = tdata)
 +
 +Levene's Test for Homogeneity of Variance (center = median)
 +      Df F value Pr(>F)
 +group  5  1.7086 0.1484
 +      54               
 +</code>
 +p value is greater than .05, which indicates that there is no evidence of significant difference between variances of groups.
 +
 +==== check normality ====
 +<code>plot(res.aov3, 2)
 +</code>
 +The residuals are plotted against the quantiles of normal distribution (the straight line).
 +
 +==== unbalance design ====
 +<code>> library(car)
 +> my_anova <- aov(len ~ supp * dose, data = tdata)
 +> 
 +> Anova(my_anova, type="III"
 ++ )
 +Anova Table (Type III tests)
 +
 +Response: len
 +             Sum Sq Df F value    Pr(>F)    
 +(Intercept) 1750.33  1 132.730 3.603e-16 ***
 +supp         137.81  1  10.450  0.002092 ** 
 +dose         885.26  2  33.565 3.363e-10 ***
 +supp:dose    108.32  2   4.107  0.021860 *  
 +Residuals    712.11 54                      
 +---
 +Signif. codes:  
 +0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 </code> </code>
r/twoway_anova.1509930060.txt.gz · Last modified: 2017/11/06 09:31 by hkimscil
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