I have the following data from an experiment
> spears
treatment length
1 Control 94.7
2 Control 96.1
3 Control 86.5
4 Control 98.5
5 Control 94.9
6 IAA 89.9
7 IAA 94.0
8 IAA 99.1
9 IAA 92.8
10 IAA 99.4
11 ABA 96.8
12 ABA 87.8
13 ABA 89.1
14 ABA 91.1
15 ABA 89.4
16 GA3 99.1
17 GA3 95.3
18 GA3 94.6
19 GA3 93.1
20 GA3 95.7
21 CPPU 104.4
22 CPPU 98.9
23 CPPU 98.9
24 CPPU 106.5
25 CPPU 104.8
An I want to compare all the treatments against the "Control" treatment using the following code
mod0 <-aov( length ~ treatment, data = spears)
summary(mod0)
library(multcomp)
spears_dun <- glht(mod0,linfct = mcp(treatment = "Dunnett"), alternative = "greater")
summary(spears_dun)
However it is taking the first treatment in alphabetical order (ABA) as control instead of the "Control" treatment.
The results are as follow
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: aov(formula = length ~ treatment, data = spears)
Linear Hypotheses:
Estimate Std. Error t value Pr(>t)
Control - ABA <= 0 3.300 2.325 1.419 0.2240
CPPU - ABA <= 0 11.860 2.325 5.101 <0.001 ***
GA3 - ABA <= 0 4.720 2.325 2.030 0.0833 .
IAA - ABA <= 0 4.200 2.325 1.806 0.1230
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
How can I make the comparison against "Control"?
Thanks.
You can force the treatment contrast level when you make it a factor into any order you like the first will be the base.
spears$treatment <- factor(spears$treatment,
levels = c("Control", "ABA", "CPPU", "GA3", "IAA"))
contrasts(spears$treatment)
#> ABA CPPU GA3 IAA
#> Control 0 0 0 0
#> ABA 1 0 0 0
#> CPPU 0 1 0 0
#> GA3 0 0 1 0
#> IAA 0 0 0 1
mod0 <-aov( length ~ treatment, data = spears)
library(multcomp)
spears_dun <- glht(mod0,linfct = mcp(treatment = "Dunnett"), alternative = "greater")
summary(spears_dun)
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Multiple Comparisons of Means: Dunnett Contrasts
#>
#>
#> Fit: aov(formula = length ~ treatment, data = spears)
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>t)
#> ABA - Control <= 0 -3.300 2.325 -1.419 0.99232
#> CPPU - Control <= 0 8.560 2.325 3.682 0.00272 **
#> GA3 - Control <= 0 1.420 2.325 0.611 0.55407
#> IAA - Control <= 0 0.900 2.325 0.387 0.65279
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> (Adjusted p values reported -- single-step method)