Entering edit mode

Thanks for reading my post. I understand how to run non-parametric and parametric tests on three or more groups. But what I haven't figured out was a simple way of doing this multiple times from different categories I am interested in. In my dataset, I have gene copies for various antibiotic classes and I want to see if there are significant differences between samples in each antibiotic class. My data is currently in long format and looks like this:

```
Sample Class Value
A Macrolide 0.22
A Macrolide 0.45
A Macrolide 0.63
B Macrolide 0.25
B Macrolide 0.28
B Macrolide 0.47
C Macrolide 0.22
C Macrolide 0.26
C Macrolide 0.29
A Ceph 0.32
A Ceph 0.42
A Ceph 0.62
B Ceph 0.42
B Ceph 0.20
B Ceph 0.91
C Ceph 0.82
C MCeph 0.92
```

So essentially I want to do a one-way ANOVA for Macrolides and then another for Ceph etc etc. Can someone help me?

Thanks

Entering edit mode

Couldn't you just subset your dataframe for Class. i.e.

```
> library(lmPerm)
> data.df = read.csv("data.txt")
> aov.macro <- lmPerm::aovp(Value ~ Sample, subset(data.df,data.df$Class == "Macrolide") )
[1] "Settings: unique SS "
> summary(aov.macro)
Component 1 :
Df R Sum Sq R Mean Sq Pr(Exact)
Sample 2 0.047089 0.023544 1
Residuals 6 0.115400 0.019233
> ceph.macro <- lmPerm::aovp(Value ~ Sample, subset(data.df,data.df$Class == "Ceph") )
[1] "Settings: unique SS "
> summary(ceph.macro)
Component 1 :
Df R Sum Sq R Mean Sq Pr(Exact)
Sample 2 0.10293 0.051467 1
Residuals 4 0.31087 0.077717
> mceph.macro <- lmPerm::aovp(Value ~ Sample, subset(data.df,data.df$Class == "MCeph") )
Error in ctrfn(levels(x), contrasts = contrasts) :
not enough degrees of freedom to define contrasts
```

Not sure if that was intentional but you have one class labelled as MCeph. Seems like a typo, or the data is truncated? If not, you don't have enough degrees of freedom to do an anova on that class (you'd need at least one of each Sample for the function to even work).

Entering edit mode

Split data by class, then run your favourite function, see example:

```
# example data
df1 <- read.table(text = "Sample Class Value
A Macrolide 0.22
A Macrolide 0.45
A Macrolide 0.63
B Macrolide 0.25
B Macrolide 0.28
B Macrolide 0.47
C Macrolide 0.22
C Macrolide 0.26
C Macrolide 0.29
A Ceph 0.32
A Ceph 0.42
A Ceph 0.62
B Ceph 0.42
B Ceph 0.2
B Ceph 0.91
C Ceph 0.82
C MCeph 0.92
A MCeph 0.2
B MCeph 0.72
C MCeph 0.32
", header = TRUE, stringsAsFactors = FALSE)
# split and apply function, result is a named list:
lapply(split(df1, df1$Class), function(i){
anova(lm(Value ~ Sample, data = i))
})
$Ceph
Analysis of Variance Table
Response: Value
Df Sum Sq Mean Sq F value Pr(>F)
Sample 2 0.10293 0.051467 0.6622 0.5644
Residuals 4 0.31087 0.077717
$Macrolide
Analysis of Variance Table
Response: Value
Df Sum Sq Mean Sq F value Pr(>F)
Sample 2 0.047089 0.023544 1.2241 0.3582
Residuals 6 0.115400 0.019233
$MCeph
Analysis of Variance Table
Response: Value
Df Sum Sq Mean Sq F value Pr(>F)
Sample 2 0.1608 0.0804 0.4467 0.7268
Residuals 1 0.1800 0.1800
```

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a small comment, you don't need to write

`lmPerm::aovp`

since you already loaded the libraryThanks so much! Yes that was a typo.

Can I use the same subsetted dataframe for TukeyHSD?

Most likely yes. I'm not familiar with the function but it looking at some examples, it looks like it needs takes an input of an anova object (generated by

`aov`

) so the`lmPerm::aovp`

method might not work. Try it, and if it doesn't work use`aov`

like in here (with subsetted dataframes):https://www.rdocumentation.org/packages/stats/versions/3.6.0/topics/TukeyHSD