Note that ANOVA and post-hoc tests can be performed in Prism too.

## One-way ANOVA

See the Prism help page for some general considerations.

#### Reading in data

- Use read.* or create appropriate dataframe

# Input data from a tab-delimited text file of the format # weight group # 56 a # 29 b # ... strains = read.delim("brain_weights.txt",header=TRUE) # Input data for 4 different groups by creating a dataframe by hand a = c(56,60,44,53) b = c(29,38,18,35) c = c(11,25,7,18) d = c(26,44,20,32) strains.frame = data.frame(a, b, c, d) strains = stack(strains.frame) colnames(strains) = c("weight", "group")

#### Creating an ANOVA table

- Use the command
*anova*or*aov*with summary. The first argument is the dependent variable, followed by ~, and then by independent variable(s). - So if we want to set up a model where weight is a function of the group (e.g., the weight potentially depends on the group)

# Syntax 1 anova( lm(weight ~ group, data=strains) ) # Syntax 2 summary( aov(weight ~ group, data=strains) )

## Post-test: Comparing all pairs of means

#### Tukey

- "Tukey's method is more conservative but may miss real differences too often" - Intuitive Biostatistics (p.259)

TukeyHSD( aov(weight ~ group, data=strains) )

#### Dunnett

- Useful if you want to compare a reference group to all other groups (instead of doing an all vs. all comparison)
- The first group ("a" in this example) is used as the reference group. If this is not the case, use the relevel() command to set the reference, like
*strains$group = relevel(strains$group, "b")*

library(multcomp) summary(glht(aov(weight ~ group, data=strains), linfct=mcp(group="Dunnett")))

## Repeated-measures ANOVA

Repeated-measures ANOVA is typically needed if multiple measurements are made on the same sample (such as assaying a mouse's weight during a time course).

## Two-way ANOVA

Two-way ANOVA should be used for experiments where two different factors are being studied (such as comparing different treatments of different genotypes of mice).

#### Reading in data, plotting, and summarizing

- Use read.* or create appropriate dataframe

# Input data from a tab-delimited text file of the format # weight treatment genotype # 56 a ko # 29 b wt # 60 a wt # ... strains = read.delim("brain_weights.txt",header=TRUE) # Plot the data by group boxplot(weight ~ paste(genotype, treatment), data=strains) stripchart(weight ~ paste(genotype, treatment), data=strains, vert=T, method="jitter", jitter = 0.4, pch=19, cex=2, add=T) # Summarize the data by group tapply(strains$weight, paste(strains$genotype, strains$treatment), mean)

#### Creating an ANOVA Table

- Use the command
*anova*or*aov*with summary. The first argument is the dependent variable, followed by ~, and then by independent variable(s). - So if we want to set up a model where weight is a function of the group and/or the genotype, with a potential interaction (e.g., the difference between groups depends on the genotype), the typical analysis would look like

# Syntax 1 anova( lm(weight ~ group * genotype, data=strains) ) anova( lm(weight ~ genotype * group, data=strains) ) # Syntax 2 summary( aov(weight ~ group * genotype, data=strains) ) summary( aov(weight ~ genotype * group, data=strains) )

Note that the p-value for each factor depends on the order of the factors in the above formulas.

## Post-test: Comparing all pairs of means

As before, with 1-way ANOVA,

TukeyHSD( aov (weight ~ group * genotype, data=strains) )

If the experimental design is unbalanced (e.g., some groups are more replicated than others), we need a more complex model.

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