Factorial And Graphic Display Pdf

Two-way or multi-way data often come from experiments with afactorial design. A factorial design has at least two factor variablesfor its independent variables, and multiple observation for every combinationof these factors.

The weight gain example below show factorial data. In thisexample, there are three observations for each combination of Diet and Country.

With this kind of data, we are usually interested in testingthe effect of each factor variable (main effects) and then the effect oftheir combination (interaction effect).

For two-way data, an interaction plot shows the mean ormedian value for the response variable for each combination of the independentvariables. This type of plot, especially if it includes error bars to indicatethe variability of data within each group, gives us some understanding of theeffect of the main factors and their interaction.

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When main effects or interaction effects are statisticallysignificant, post-hoc testing can be conducted to determine which groups differsignificantly from other groups. With a factorial experiment, there are a fewguidelines for determining when to do post-hoc testing. The followingguidelines are presented for two-way data for simplicity.

When neither the main effects nor the interaction effectis statistically significant, no post-hoc mean-separation testing should beconducted.

When one or more of the main effects are statisticallysignificant and the interaction effect is not, post-hoc mean-separationtesting should be conducted on significant main effects only.
(This is shown in the first weight gain example below)

When the interaction effect is statistically significant,post-hoc mean-separation testing should be conducted on the interaction effectonly. This is the case even when the main effects are also statisticallysignificant.
(This is shown in the second weight gain example below)

The final guideline above is not always followed. There aretimes when people will present the mean-separation tests for significant maineffects even when the interaction effect is significant. In general, though,if there is a significant interaction, the mean-separation tests forinteraction will better explain the results of the analysis, and themean-separation tests for the main effects will be of less interest.

Packages used in this chapter

The packages used in this chapter include:

• psych

• car

• multcompView

• lsmeans

• FSA

• ggplot2

• phia

The following commands will install these packages if theyare not already installed:


if(!require(car)){install.packages('car')}
if(!require(psych)){install.packages('psych')}
if(!require(multcompView)){install.packages('multcompView')}
if(!require(lsmeans)){install.packages('lsmeans')}
if(!require(FSA)){install.packages('FSA')}
if(!require(ggplot2)){install.packages('ggplot2')}
if(!require(phia)){install.packages('phia')}

Two-way ANOVA example with interaction effect

Imagine for this example an experiment in which people wereput on one of three diets to encourage weight gain. The amount of weightgained will be the dependent variable, and will be considered an interval/ratiovariable. For independent variables, there are three different diets and thecountry in which subjects live.

The model will be fit with the lm function, whosesyntax is similar to that of the clm function.

This kind of analysis makes certain assumptions about thedistribution of the data, but for simplicity, this example will ignore the needto determine that the data meet these assumptions.

Input =('
Diet Country Weight_change
A USA 0.120
A USA 0.125
A USA 0.112
A UK 0.052
A UK 0.055
A UK 0.044
B USA 0.096
B USA 0.100
B USA 0.089
B UK 0.025
B UK 0.029
B UK 0.019
C USA 0.149
C USA 0.150
C USA 0.142
C UK 0.077
C UK 0.080
C UK 0.066
')
Data = read.table(textConnection(Input),header=TRUE)

### Order levels of the factor; otherwise R will alphabetize them

Data$Country = factor(Data$Country,
levels=unique(Data$Country))
### Check the data frame
library(psych)
headTail(Data)
str(Data)

summary(Data)
### Remove unnecessary objects
rm(Input)

Simple interaction plot

And

The interaction.plot function creates a simpleinteraction plot for two-way data. The options shown indicate which variableswill used for the x-axis, trace variable, and response variable. The fun=meanoption indicates that the mean for each group will be plotted. For the meaningof other options, see ?interaction.plot.

This style of interaction plot does not show the variabilityof each group mean, so it is difficult to use this style of plot to determineif there are significant differences among groups.

The plot shows that mean weight gain for each diet was lowerfor the UK compared with USA. And that this difference was relatively constantfor each diet, as is evidenced by the lines on the plot being parallel. Thissuggests that there is no large or significant interaction effect. That is,the difference among diets is consistent across countries. And vice-versa, thedifference in countries is consistent across diets.

A couple of other styles of interaction plot are shown atthe end of this chapter.

interaction.plot(x.factor = Data$Country,
trace.factor = Data$Diet,
response = Data$Weight_change,
fun = mean,
type='b',
col=c('black','red','green'), ### Colors for levels of trace var.
pch=c(19, 17, 15), ###Symbols for levels of trace var.
fixed=TRUE, ###Order by factor order in data
leg.bty = 'o')


Specify the linear model and conduct an analysis ofvariance

A linear model is specified with the lm function. Weight_changeis the dependent variable. Country and Diet are the independentvariables, and including Country:Diet in the formula adds theinteraction term for Country and Diet to the model.

The ANOVA table indicates that the main effects aresignificant, but that the interaction effect is not.

model = lm(Weight_change ~ Country + Diet + Country:Diet,
data = Data)
library(car)
Anova(model,
type = 'II')


Anova Table (Type II tests)
Sum Sq Df F value Pr(>F)
Country 0.022472 1 657.7171 7.523e-12 ***
Diet 0.007804 2 114.2049 1.547e-08 ***
Country:Diet 0.000012 2 0.1756 0.8411
Residuals 0.000410 12

Post-hoc testing with lsmeans

Because the main effects were significant, we will want toperform post-hoc mean separation tests for each main effect factor variable.

For this, we will use the lsmeans package. The linearmodel under consideration is called model, created the lmfunction above. The formula in the lsmeans function indicates that pairwisecomparisons should be conducted for the variable Country in the firstcall, and for the variable Diet in the second call.


library(lsmeans)
lsmeans(model,
pairwise ~ Country,
adjust='tukey') ###Tukey-adjusted comparisons


$contrasts
contrast estimate SE df t.ratio p.value
USA - UK 0.07066667 0.002755466 12 25.646 <.0001

library(lsmeans)
lsmeans(model,
pairwise ~ Diet,
adjust='tukey') ###Tukey-adjusted comparisons


$contrasts

contrast estimate SE df t.ratio p.value
A - B 0.025 0.003374743 12 7.408 <.0001
A - C -0.026 0.003374743 12 -7.704 <.0001
B - C -0.051 0.003374743 12 -15.112 <.0001

Extended example with additional country

For the following example, the hypothetical data have beenamended to include a third country, New Zealand.


Input =('
Diet Country Weight_change
A USA 0.120
A USA 0.125
A USA 0.112
A UK 0.052
A UK 0.055
A UK 0.044
A NZ 0.080
A NZ 0.090
A NZ 0.075
B USA 0.096
B USA 0.100
B USA 0.089
B UK 0.025
B UK 0.029
B UK 0.019
B NZ 0.055
B NZ 0.065
B NZ 0.050
C USA 0.149
C USA 0.150
C USA 0.142
C UK 0.077
C UK 0.080
C UK 0.066
C NZ 0.055
C NZ 0.065
C NZ 0.050
C NZ 0.054
')
Data = read.table(textConnection(Input),header=TRUE)

### Order levels of the factor; otherwise R will alphabetize them

Data$Country = factor(Data$Country,
levels=unique(Data$Country))
### Check the data frame
library(psych)
headTail(Data)
str(Data)

summary(Data)
### Remove unnecessary objects
rm(Input)

Simple interaction plot

The plot suggests that the effect of diet is not consistentacross all three countries. While Diet C showed the greatest mean weight gainfor USA and UK, for NZ it has a lower mean than Diet A. This suggests theremay be a meaningful or significant interaction effect, but we will need to do astatistical test to confirm this hypothesis.


interaction.plot(x.factor = Data$Country,
trace.factor = Data$Diet,
response = Data$Weight_change,
fun = mean,
type='b',
col=c('black','red','green'), ### Colors for levels of trace var.
pch=c(19, 17, 15), ###Symbols for levels of trace var.
fixed=TRUE, ###Order by factor order in data
leg.bty = 'o')


Specify the linear model and conduct an analysis ofvariance

The ANOVA table indicates that the interaction effect issignificant, as are both main effects.


model = lm(Weight_change ~ Country + Diet + Country:Diet,
data = Data)
library(car)
Anova(model,
type = 'II')


Anova Table (Type II tests)
Sum Sq Df F value Pr(>F)
Country 0.0256761 2 318.715 2.426e-15 ***
Diet 0.0051534 2 63.969 3.634e-09 ***
Country:Diet 0.0040162 4 24.926 2.477e-07 ***
Residuals 0.0007653 19

Post-hoc testing with lsmeans

Because the interaction effect was significant, we wouldlike to compare all group means from the interaction. Even though the maineffects were significant, the typical advice is to not conduct pairwisecomparisons for main effects when their interaction is significant. This isbecause focusing on the groups in the interaction better describe the resultsof the analysis.

We will use the lsmeans package, and ask for acompact letter display with the cld function. First we create anobject, named marginal, with the results of the call to lsmeans. Notice here that the formula indicates that pairwise comparisons shouldbe conducted for the interaction of Country and Diet, indicatedwith Country:Diet.

Be sure to read the Least Square Means for Multiple Comparisonschapter for correct interpretation of least square means. For lm modelobjects, the values for lsmean, SE, LCL, and UCLvalues are meaningful.


library(lsmeans)
marginal = lsmeans(model,
pairwise ~ Country:Diet,
adjust='tukey') ### Tukey-adjusted comparisons
marginal$contrasts
cld(marginal,
alpha=0.05,
Letters=letters, ### Use lower-caseletters for .group
adjust='tukey') ###Tukey-adjusted comparisons


Country Diet lsmean SE df lower.CL upper.CL .group
UK B 0.02433333 0.003664274 19 0.01291388 0.03575278 a
UK A 0.05033333 0.003664274 19 0.03891388 0.06175278 b
NZ C 0.05600000 0.003173354 19 0.04611047 0.06588953 b
NZ B 0.05666667 0.003664274 19 0.04524722 0.06808612 bc
UK C 0.07433333 0.003664274 19 0.06291388 0.08575278 cd
NZ A 0.08166667 0.003664274 19 0.07024722 0.09308612 de
USA B 0.09500000 0.003664274 19 0.08358055 0.10641945 e
USA A 0.11900000 0.003664274 19 0.10758055 0.13041945 f
USA C 0.14700000 0.003664274 19 0.13558055 0.15841945 g
Confidence level used: 0.95
Conf-level adjustment: sidak method for 9 estimates
P value adjustment: tukey method for comparing a family of 9 estimates
significance level used: alpha = 0.05
### Groups sharing a letter are not significantlydifferent
### at the alpha = 0.05 level.

Interaction plot with error bars using ggplot2

The interaction plots created with the interaction.plotfunction above are handy to investigate trends in the data, but have thedisadvantage of not showing the variability in data within each group.

The package ggplot2 can be used to create attractiveinteraction plots with error bars. Here, we will use standard error of eachmean for the error bars.


### Create a data frame called Sum with means and standard deviations

library(FSA)
Sum = Summarize(Weight_change ~ Country + Diet,
data=Data,
digits=3)
### Add standard error of the mean to the Sum dataframe
Sum$se = Sum$sd / sqrt(Sum$n)
Sum$se = signif(Sum$se, digits=3)
Sum

Country Diet nnvalid mean sd min Q1 median Q3 max percZero se
1 USA A 3 3 0.119 0.007 0.112 0.116 0.120 0.122 0.125 00.00404
2 UK A 3 3 0.050 0.006 0.044 0.048 0.052 0.054 0.055 00.00346
3 NZ A 3 3 0.082 0.008 0.075 0.078 0.080 0.085 0.090 00.00462
4 USA B 3 3 0.095 0.006 0.089 0.092 0.096 0.098 0.100 00.00346
5 UK B 3 3 0.024 0.005 0.019 0.022 0.025 0.027 0.029 00.00289
6 NZ B 3 3 0.057 0.008 0.050 0.052 0.055 0.060 0.065 00.00462
7 USA C 3 3 0.147 0.004 0.142 0.146 0.149 0.150 0.150 00.00231
8 UK C 3 3 0.074 0.007 0.066 0.072 0.077 0.078 0.080 00.00404
9 NZ C 4 4 0.056 0.006 0.050 0.053 0.054 0.058 0.065 00.00300

Factorial And Graphic Display Pdf Template

### Order levels of the factor;otherwise R will alphabetize them
Sum$Country = factor(Sum$Country,
levels=unique(Sum$Country))
### Produce interaction plot
library(ggplot2)
pd = position_dodge(.2)
ggplot(Sum, aes(x = Country,
y = mean,
color = Diet)) +
geom_errorbar(aes(ymin = mean - se,
ymax = mean + se),
width=.2, size=0.7, position=pd) +
geom_point(shape=15, size=4, position=pd) +
theme_bw() +
theme(axis.title = element_text(face = 'bold') +
scale_colour_manual(values= c('black','red','green'))+
ylab('Mean weight change')
### You may see an error, “ymax not defined”
### In this case, it does not appear to affect anything

Factorial And Graphic Display Pdf Example


Interaction plot with mean separation letters manually added

It is common to add mean separation letters from post-hocanalyses to interaction plots. One option is to add letters manually in eitherimage manipulation software like Photoshop or GIMP, or in a word processor orother software that can handle graphic manipulation.

Letters can also be added to the plot by ggplot2 withthe annotate or geom_text options. See “Optional: Interactionplot of least square means with mean separation letters” in the Least SquareMeans for Multiple Comparisons chapter for examples.

It is probably more common for means to be lettered so thatthe greatest mean is indicated with a. However, lsmeansby default labels the least mean with a. The order of letterscan be reversed manually.


Plot of mean weight change for three diets in three countries. Means sharing aletter are not significantly different according to pairwise comparisons ofleast square means with Tukey adjustment for multiple comparisons.

Interaction plot with the phia package

Factorial And Graphic Display Pdf Files

The phia package can be used to create interactionplots quickly. By default the error bars indicate standard error of themeans. For additional options, see ?interactionMeans.

Note that model is the linear model specified above.


library(phia)
IM = interactionMeans(model)
IM


Country Diet adjusted mean std. error
1 USA A 0.11900000 0.003664274
2 UK A 0.05033333 0.003664274
3 NZ A 0.08166667 0.003664274
4 USA B 0.09500000 0.003664274
5 UK B 0.02433333 0.003664274
6 NZ B 0.05666667 0.003664274
7 USA C 0.14700000 0.003664274
8 UK C 0.07433333 0.003664274
9 NZ C 0.05600000 0.003173354

### Produce interaction plot
plot(IM)
### Return the graphics device to its default 1-plot-per-window state

par(mfrow=c(1,1))