### Statistical test details

Tests used for outlier detection often rely on tabulated critical values. Decisions 
to reject or not reject a potential outlier can be dependent on the source of the 
critical values.

Where ever possible, we tried to omit using fixed tables and rather implemented 
critical value calculation *ab initio*. However, to be transparent, we detail the 
mathematical approach for each test in the following.

***Note!*** Several test functions were implemented based on the functions in 
the R package `outliers` by Lukasz Komsta. Modifications were made to allow testing
up to n=100 laboratories (was restricted to n=30 in package `outliers`).

#### Cochran

The critical value is calculated internally using formula
<details>
  <summary>`eCerto:::qcochran`</summary>

```{r, echo=FALSE, eval=TRUE}
eCerto:::qcochran
```
</details> 

where parameters *p*, *n* and *k* define the desired alpha level, the number of replicates 
per group and the number of groups respectively. `stats::qf()` is the quantile 
function of the f-distribution as implemented in the R `stats` package. Likewise, 
`stats::pf()` is used to compute the Cochran P-value via function
<details>
  <summary>`eCerto:::pcochran`</summary>

```{r, echo=FALSE, eval=TRUE}
eCerto:::pcochran
```

</details> 

<br> For comparison with critical values tabulated elsewhere you can generate a respective
table in R.
<details>
  <summary>Show code</summary>

```{r, echo=TRUE, eval=FALSE}
ps <- c(0.01, 0.05)
ns <- c(3:9, seq(10,20,5))
ks <- c(2:14, seq(15,30,5))
out <- lapply(ps, function(p) {
  sapply(ns, function(n) {
    sapply(ks, function(k) {
      eCerto:::qcochran(p=p, n=n, k=k)
    })
  })
})
names(out) <- paste0("alpha=", ps)
lapply(out, function(x) {
  colnames(x) <- paste0("n=", ns)
  rownames(x) <- paste0("k=", ks)
  round(x, 4)
})
```

</details> 

<details>
  <summary>Show Cochran critical values table</summary>

```{r, echo=FALSE, eval=TRUE}
ps <- c(0.01, 0.05)
ns <- c(3:9, seq(10,20,5))
ks <- c(2:14, seq(15,30,5))
out <- lapply(ps, function(p) {
  sapply(ns, function(n) {
    sapply(ks, function(k) {
      eCerto:::qcochran(p=p, n=n, k=k)
    })
  })
})
names(out) <- paste0("alpha=", ps)
lapply(out, function(x) {
  colnames(x) <- paste0("n=", ns)
  rownames(x) <- paste0("k=", ks)
  round(x, 4)
})
```

</details> 

<br> The test is originally based on:  
"Snedecor, G.W., Cochran, W.G. (1980). Statistical Methods (seventh edition). Iowa State University Press, Ames, Iowa".

#### Dixon

The Dixon test statistic is calculated depending on the number of labs (n) using
the function 
<details>
  <summary>`eCerto:::dixon.test`</summary>

```{r, echo=FALSE, eval=TRUE}
eCerto:::dixon.test
```

</details> 

<br> In ***eCerto*** the one-sided version of the Dixon test is applied consecutively
at the lower and upper end. Critical values are stored in tabulated form internally 
(`eCerto::cvals_Dixon`) and have been combined in a single table from 
[this source](http://www.statistics4u.com/fundstat_eng/cc_outlier_tests_dixon.html).
<details>
  <summary>Show Dixon critical values table</summary>

```{r, echo=FALSE, eval=TRUE}
eCerto::cvals_Dixon[,1:8]
```

</details> 

<br> Values for non-tabulated combinations of *p* and *n* are derived by interpolation
using function
<details>
  <summary>`eCerto:::qtable`</summary>
  
```{r, echo=FALSE, eval=TRUE}
eCerto:::qtable
```

</details> 

<br> The test is originally based on:  
"Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21, 4, 488-506"  
"Dean, R.B.; Dixon, W.J. (1951). Simplified statistics for small numbers of observations. Anal.Chem. 23, 636-638"  
"Dixon, W.J. (1953). Processing data for outliers. J. Biometrics. 9, 74-89"

#### Grubbs

The Grubbs test statistic is calculated either for a single or a double outlier at
both ends of the distribution consecutively using
<details>
  <summary>`eCerto:::grubbs.test`</summary>
  
```{r, echo=FALSE, eval=TRUE}
eCerto:::grubbs.test
```

</details> 

<br> Critical values for the single Grubbs test are calculated internally using
<details>
  <summary>`eCerto:::qgrubbs`</summary>
  
```{r, echo=FALSE, eval=TRUE}
eCerto:::qgrubbs
```

</details> 

<br> For comparison with critical values tabulated elsewhere you can generate a respective
table in R.
<details>
  <summary>Show code</summary>

```{r, echo=TRUE, eval=FALSE}
ps <- c(0.01, 0.025, 0.05, 0.1)
ns <- c(3:10, seq(20,100,40))
out <- sapply(ps, function(p) {
  sapply(ns, function(n) {
    eCerto:::qgrubbs(p=p, n=n)
  })
})
colnames(out) <- paste0("a=", ps)
rownames(out) <- paste0("n=", ns)
round(out, 4)
```
</details> 

<details>
  <summary>Show Grubbs (single) critical values table</summary>

```{r, echo=FALSE, eval=TRUE}
ps <- c(0.01, 0.025, 0.05, 0.1)
ns <- c(3:10, seq(20,100,40))
out <- sapply(ps, function(p) {
  sapply(ns, function(n) {
    eCerto:::qgrubbs(p=p, n=n)
  })
})
colnames(out) <- paste0("a=", ps)
rownames(out) <- paste0("n=", ns)
round(out, 4)
```
</details> 

<br> Critical values for the double Grubbs test are stored in tabulated form internally 
(`eCerto::cvals_Grubbs2`) and P-values are derived by comparison.
<details>
  <summary>Show Grubbs (double) critical values table</summary>

```{r, echo=FALSE, eval=TRUE}
ps <- c(0.01, 0.025, 0.05, 0.1)
ns <- c(4:10, seq(20,100,40))
out <- eCerto::cvals_Grubbs2
round(out[rownames(out)%in%ns, colnames(out)%in%ps], 4)
```

</details> 

<br> ***Note!*** Critical values for double Grubbs are obtained from the `outliers` package 
for 4≤n≤30 and are estimated for 31≤n≤100 based on the approximation described in
[this paper](https://link.springer.com/article/10.1007/s10182-011-0185-y).

The test is originally based on:  
"Grubbs, F.E. (1950). Sample Criteria for testing outlying observations. Ann. Math. Stat. 21, 1, 27-58".

#### Scheffe

The Scheffe multiple comparison test is a re-implementation of the respective function
published in the `agricolae` package. It is calculated on the result of `stats::lm()`.
<details>
  <summary>`eCerto:::scheffe.test`</summary>

```{r, echo=FALSE, eval=TRUE}
eCerto:::scheffe.test
```

</details> 

The test is originally based on:  
"Steel, R.; Torri, J.; Dickey, D. (1997) Principles and Procedures of Statistics: A Biometrical Approach. pp189"
