Example

• The data set chickwts is available in R, and on the course webpage.
• 71 newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement.
• Their weights in grams after six weeks are given along with feed types, i.e. we have a sample with corresponding measurements of 2 variables:
  • weight: a numeric variable giving the chick weight.
  • feed: a factor giving the feed type.
• Always start with some graphics:

library(mosaic)
gf_boxplot(weight ~ feed, data = chickwts)

The ANOVA Model

• We measure the response \(y\) which in this case is weight.
• We want to study the effect of the factor \(x\) on \(y\). In this case \(x=\)feed and divides the sample in \(g=6\) groups.
• The mean responses within the groups are denoted \(\mu_1,\mu_2,\ldots,\mu_g\).
• We will assume that
  • \(y=\mu_x+\epsilon\), when \(y\) is a response in group \(x\)
  • \(\epsilon\) are a sample from a population with mean zero and standard deviation \(\sigma\).
  • The standard deviation for the population in each group is the same and equals \(\sigma\)
  • The response variable, \(y\), is normal distributed within each group.
• The ANOVA test is a test of equal means for the different groups.

Estimates

• Least squares estimates for population means \(\widehat\mu_x\) is given by the average of the response measurements in group \(x\).
• For a given measured response \(y\) we let \(\widehat y\) denote the model’s prediction of \(y\), i.e. \[\widehat y = \widehat\mu_x\] if \(y\) is a response for an observation in group \(x\).
• We use mean to find the mean, for each group:

mean(weight ~ feed, data = chickwts)

##    casein horsebean   linseed  meatmeal   soybean sunflower 
##  323.5833  160.2000  218.7500  276.9091  246.4286  328.9167

• We can e.g. see that \(\widehat y=323.6\), when feed=casein but \(\widehat y=160.2\), when feed=horsebean.
• Is it a significant difference ?

Contrast coding

• In many cases there is a group corresponding to “no treatment” and we are interested in the effect of different treatments.
• In this example we only have different feeds, which are sorted in lexicographical order by R, so casein is the reference.
• We can specify the model via:
  • Intercept corresponding to the mean response for the reference (casein).
  • For each of the other groups we have a contrast, which measures the difference between the mean value for that group and the reference group.
• For a given contrast we can calculate standard error, t-score and p-value, and thereby investigate whether there is a difference between this group and the reference group.
• In Agresti this is referred to as using dummy variables.

Example

model <- lm(weight ~ feed, data = chickwts)
summary(model)

## 
## Call:
## lm(formula = weight ~ feed, data = chickwts)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -123.909  -34.413    1.571   38.170  103.091 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    323.583     15.834  20.436  < 2e-16 ***
## feedhorsebean -163.383     23.485  -6.957 2.07e-09 ***
## feedlinseed   -104.833     22.393  -4.682 1.49e-05 ***
## feedmeatmeal   -46.674     22.896  -2.039 0.045567 *  
## feedsoybean    -77.155     21.578  -3.576 0.000665 ***
## feedsunflower    5.333     22.393   0.238 0.812495    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 54.85 on 65 degrees of freedom
## Multiple R-squared:  0.5417, Adjusted R-squared:  0.5064 
## F-statistic: 15.36 on 5 and 65 DF,  p-value: 5.936e-10

• We get information about contrasts and their significance:
• Intercept corresponding to casein has weight different from zero (\(p < 2\times 10^{-16}\)) (of course, chickens grow a lot over 6 weeks)
• Weight difference between casein and horsebean is extremely significant (p=\(2\times 10^{-9}\)).
• There is no significant weight difference between casein and sunflower (p=\(81\)%).

Graphical representation of models

• We have two alternative explanations of the data.
• Simple model with one parameter (mean): “The feed type doesn’t matter. The weight is just random around a common mean value”.
• Complex model with six parameters (means): “The feed type is important. For each feed type we get a different mean value and the weights are random around these values.”

Hypotheses and test statistic

• Is the complex model significantly better (i.e. is there any effect of the explanatory grouping variable)? We can write the corresponding hypotheses in two different ways \[H_0: \mu_1 = \mu_2 = \dots=\mu_g \quad \mbox{against} \quad H_a: \mbox{ At least 2 of the population means are different}\]
• Alternatively \[H_0: \mbox{ All contrasts are equal to zero. } \quad H_a: \mbox{ At least one contrast is non-zero}.\]
• We will (indirectly) use \(R^2\) to do the test. If it is large, the complex model has good predictive power compared to the simple model. To judge significance we use \[F_{obs} = \frac{(n-g)R^2}{(g-1)(1-R^2)} = \frac{(TSS-SSE)/(g-1)}{SSE/(n-g)}.\]
• Large values of \(R^2\) implies large values of \(F_{obs}\), which points to the alternative hypothesis.
• I.e. when we have calculated the observed value \(F_{obs}\), then we have to find the probability that a new experiment would result in a larger value.
• TSS: error sum of squares if common mean. SSE: error sum of squares if different means.
• TSS-SSE: how much does error sum of squares increase if means are restricted to be equal.
• One can show that TSS-SSE is variation of group means around common mean - variance between groups

Interpretation of \(F\) statistic - Variance between/within groups

• It can be shown that the numerator of \(F_{obs}\) is a measure of the variance between the groups, i.e. how much “boxes” vary around the total average (the red line).

• Likewise it can be shown the denominator of \(F_{obs}\) is a measure for the variance within groups, i.e. how “tall” the boxes in the boxplot are.

• The bigger deviations between the red line and the box means relative to the variation within boxes, the less we trust \(H_0\). This is measured by the F-test statistic, which can be stated as \[F_{obs} = \frac{\mbox{variance between groups}}{\mbox{variance within groups}}\]

Example

model <- lm(weight ~ feed, data = chickwts)
summary(model)

## 
## Call:
## lm(formula = weight ~ feed, data = chickwts)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -123.909  -34.413    1.571   38.170  103.091 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    323.583     15.834  20.436  < 2e-16 ***
## feedhorsebean -163.383     23.485  -6.957 2.07e-09 ***
## feedlinseed   -104.833     22.393  -4.682 1.49e-05 ***
## feedmeatmeal   -46.674     22.896  -2.039 0.045567 *  
## feedsoybean    -77.155     21.578  -3.576 0.000665 ***
## feedsunflower    5.333     22.393   0.238 0.812495    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 54.85 on 65 degrees of freedom
## Multiple R-squared:  0.5417, Adjusted R-squared:  0.5064 
## F-statistic: 15.36 on 5 and 65 DF,  p-value: 5.936e-10

• The last line gives us the value of \(F_{obs} = 15.36\) and the corresponding \(p\)-value (\(5.9 \times 10^{-10}\)). Clearly there is a significant difference between the types of feed.

Additive effects

• The data set ToothGrowth is available in R and on the webpage. For more info about this data, use ?ToothGrowth.

• The data describes the tooth length in guinea pigs where some receive vitamin C treatment and others are given orange juice in different dosage.

• A total of \(60\) observations on 3 variables.

  • [,1] len The tooth length
  • [,2] supp The type of the supplement (OJ or VC)
  • [,3] dose The dosage (LO, ME, HI)

• We will study the response len with the predictors supp and dose.

• At first we look at the model with additive effects

  • len=\(\mu\) + "effect of supp"+ "effect of dose" + error

• This is also called the main effects model since it does not contain interaction terms.

• The parameter \(\mu\) corresponds to the Intercept and is the mean tooth length in the reference group (supp OJ, dose LO).

• The effect of supp is the difference in mean when changing from OJ to VC.

• The effect of dose is the difference in mean when changing from LO to eitherME or HI.

Dummy coding

• Let us introduce dummy variables:
  • \(s_C=1\) if supp VC and zero otherwise.
  • \(d_M=1\) if dose is ME and zero otherwise.
  • \(d_H=1\) if dose is HI and zero otherwise.
• Then we state the model \[\mbox{length}=\mu+\beta_1 s_C+\beta_2 d_M+\beta_3 d_H + \mbox{error} .\]
• Interpretation:
  • \(\mu\) is the expected tooth length when supp is OJ and dose is LO (\(s_C=d_M=d_H=0)\)).
  • \(\beta_1\) is the effect of supplement OJ to VC (\(s_C=1\)).
  • \(\beta_2\) is the effect of increasing dosage from LO to ME (\(d_M=1\)).
  • \(\beta_3\) is the effect of increasing dosage from LO to HI (\(d_H=1\)).
• As a two-way table:

\[ \begin{array}{cccc} & LO & ME & HI \\ OJ & \mu & \mu+\beta_2 & \mu+ \beta_3\\ VC & \mu +\beta_1 & \mu+\beta_1 + \beta_2 & \mu+ \beta_1 + \beta_3\\ \end{array} \]

Main effect model in R

• The main effects model is fitted by

MainEff <- lm(len ~ supp + dose, data = ToothGrowth)
summary(MainEff)

## 
## Call:
## lm(formula = len ~ supp + dose, data = ToothGrowth)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.085 -2.751 -0.800  2.446  9.650 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.4550     0.9883  12.603  < 2e-16 ***
## suppVC       -3.7000     0.9883  -3.744 0.000429 ***
## doseME        9.1300     1.2104   7.543 4.38e-10 ***
## doseHI       15.4950     1.2104  12.802  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.828 on 56 degrees of freedom
## Multiple R-squared:  0.7623, Adjusted R-squared:  0.7496 
## F-statistic: 59.88 on 3 and 56 DF,  p-value: < 2.2e-16

• The model has 4 parameters.
• The \(F\) test at the end compares with the (null) model with only one overall mean parameter.

Testing effect of supp

• Alternative model without effect of supp:

doseEff <- lm(len ~ dose, data = ToothGrowth)

• We can compare \(R^2\) to see if doseEff (Model 1) is sufficient to explain the data compared to MainEff (Model 2). This is done by converting to \(F\)-statistic: \[ F_{obs} = \frac{(R_2^2 - R_1^2)/(df_1 - df_2)}{(1 - R_2^2)/df_2} = \frac{(SSE_1 - SSE_2)/(df_1 - df_2)}{(SSE_2)/df_2}. \]
• \(SSE_1-SSE_2\): increase in error sum of square when using Model 1 instead of Model 2
• In R the calculations are done using anova:

anova(doseEff, MainEff)

## Analysis of Variance Table
## 
## Model 1: len ~ dose
## Model 2: len ~ supp + dose
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     57 1025.78                                  
## 2     56  820.43  1    205.35 14.017 0.0004293 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

• \(p\)-value is 0.004 hence we reject that supp does not have an effect. Thus we prefer Model 2.

Testing effect of dose

• Alternative model without effect of dose:

suppEff <- lm(len ~ supp, data = ToothGrowth)
anova(suppEff, MainEff)

## Analysis of Variance Table
## 
## Model 1: len ~ supp
## Model 2: len ~ supp + dose
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1     58 3246.9                                  
## 2     56  820.4  2    2426.4 82.811 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

• \(p\)-value is \(\approx 0\) hence we reject that dose does not have an effect. Thus we prefer Model 2.

Example

• We will extend the model by introducing an interaction between supp and dose.

• Interaction plot:

with(ToothGrowth, interaction.plot(dose, supp, len, col = 2:3))

• For each of the supplement types we plot the average tooth length as a function of dosage.

• If the main effects model is correct then the difference between supplements is the same for all levels of dosage, i.e. the curves should be parallel - except for noise.

• This does not seem to be the case.

• This is how the plot should look if the main effects model (no interaction) is correct:

• Parallel lines mean that effect of supplement does not depend on dose !

Dummy coding

• The extended model can be formulated as \[ \mathtt{length} = \mu+\beta_1 s_C+\beta_2 d_M+\beta_3 d_H+ \beta_4 s_C d_M+\beta_5 s_C d_H+\mathtt{error} \]
• Interpretation:
  • \(\mu\) is the expected tooth length for supp OJ and dose LO (\(s_C=d_M=d_H=0\)).
  • \(\beta_1\) is the effect of changing from supp OJ to VC, dose is LO (\(s_C=1,d_M=d_H=0\)).
  • \(\beta_2\) is the effect of increasing dose from LO to ME, when supp is OJ (\(s_C=0,d_M=1\)).
  • \(\beta_3\) is the effect of increasing dose from LO to HI, when supp is OJ (\(s_C=0,d_H=1\)).
  • \(\beta_4\) is an additional effect of both changing from supp OJ to VC and increasing dose from LO to ME (\(s_C=1,d_M=1\))
  • \(\beta_5\) is an additional effect of both changing from supp OJ to VC and increasing dose from LO to HI (\(s_C=1,d_H=1\))
• As a two-way table:

\[ \begin{array}{cccc} & LO & ME & HI \\ OJ & \mu & \mu+\beta_2 & \mu+ \beta_3\\ VC & \mu +\beta_1 & \mu+\beta_1 + \beta_2 +\beta_4 & \mu+ \beta_1 + \beta_3 + \beta_5\\ \end{array} \]

• Further examples:
  • effect of changing from supp OJ to VC if dose is LO is \(\mu+\beta_1-\mu=\beta_1\)
  • effect of changing from supp OJ to VC if dose is ME is \(\mu+\beta_1+\beta_2+\beta_4- \mu-\beta_2=\beta_1+\beta_4\)
  • effect of changing from supp OJ to VC if dose is HI is \(\mu+\beta_1+\beta_3+\beta_5-\mu-\beta_3=\beta_1+\beta_5\)
  • if \(\beta_4=0\) and \(\beta_5=0\) the effect of changing from OJ to VC does not depend on dose

Example

• We fit the interaction model by changing plus to multiply in the model expression from before:

Interaction <- lm(len ~ supp*dose, data = ToothGrowth)

• Now we can think of an experiment with 6 groups corresponding to each combination of the predictors.

• Is added interaction significant ? - we compare main effects model and more complex interaction model using anova:

anova(MainEff, Interaction)

## Analysis of Variance Table
## 
## Model 1: len ~ supp + dose
## Model 2: len ~ supp * dose
##   Res.Df    RSS Df Sum of Sq     F  Pr(>F)  
## 1     56 820.43                             
## 2     54 712.11  2    108.32 4.107 0.02186 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

• With a p-value of 2.1860269% there is a significant interaction supp:dose, i.e. the lack of parallel curves in the interaction plot is significant.

summary(Interaction)

## 
## Call:
## lm(formula = len ~ supp * dose, data = ToothGrowth)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -8.20  -2.72  -0.27   2.65   8.27 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     13.230      1.148  11.521 3.60e-16 ***
## suppVC          -5.250      1.624  -3.233  0.00209 ** 
## doseME           9.470      1.624   5.831 3.18e-07 ***
## doseHI          12.830      1.624   7.900 1.43e-10 ***
## suppVC:doseME   -0.680      2.297  -0.296  0.76831    
## suppVC:doseHI    5.330      2.297   2.321  0.02411 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.631 on 54 degrees of freedom
## Multiple R-squared:  0.7937, Adjusted R-squared:  0.7746 
## F-statistic: 41.56 on 5 and 54 DF,  p-value: < 2.2e-16

• Note the negative effect of changing from OJ to VC when dose is low is cancelled by the positive interaction parameter \(\beta_5\)=suppVC:doseHI) meaning almost no difference between OJ and VC when dose is high (compare with interaction plot)

Hierarchical principle

• In presence of interaction effect it does not make sense to make tests for absence of main effects ! Indeed each factor has an effect that just happens to vary depending on the other factor
• Hence start by investigating whether there is an interaction effect
• If yes: no further tests !
• If no: you may test main effects if relevant for your study