Calculates pathogen diversity indices.

This function calculates five diversity indices for the user.

Simple diversity index, which will show the proportion of unique pathotypes to total samples. As the values gets closer to 1, there is greater diversity in pathoypes within the population. Simple diversity is calculated as: $$ D = \frac{Np}{Ns} $$ where \(Np\) is the number of pathotypes and \(Ns\) is the number of samples.

Gleason diversity index, an alternate version of Simple diversity index, is less sensitive to sample size than the Simple index. $$ D = \frac{ (Np - 1) }{ log(Ns)}$$ Where \(Np\) is the number of pathotypes and \(Ns\) is the number of samples.

Shannon diversity index is typically between 1.5 and 3.5, as richness and evenness of the population increase, so does the Shannon index value. $$ D = -\sum_{i = 1}^{R} p_i \log p_i $$ Where \(p_i\) is the proportional abundance of species \(i\).

Simpson diversity index values range from 0 to 1, 1 represents high diversity and 0 represents no diversity. Where diversity is calculated as: $$ D = \sum_{i = 1}^{R} p_i^2 $$

Evenness ranges from 0 to 1, as the Evenness value approaches 1, there is a more even distribution of each pathoype's frequency within the population. Where Evenness is calculated as: $$ D = \frac{H'}{log(Np) }$$ where \(H'\) is the Shannon diversity index and \(Np\) is the number of pathotypes.

`calculate_diversities(x, cutoff, control, sample, gene, perc_susc)`

- x
a

`data.frame`

containing the data.- cutoff
value for percent susceptible cutoff. Numeric.

- control
value used to denote the susceptible control in the

`gene`

column. Character.- sample
column providing the unique identification for each sample being tested. Character.

- gene
column providing the gene(s) being tested. Character.

- perc_susc
column providing the percent susceptible reactions. Character.

hagis.diversities object containing

Number of Samples

Number of Pathotypes

Simple Diversity Index

Gleason Diversity Index

Shannon Diversity Index

Simpson Diversity Index

Evenness Diversity Index

```
# Using the built-in data set, P_sojae_survey
data(P_sojae_survey)
P_sojae_survey
#> Isolate Line Rps Total HR (1) Lesion (2)
#> 1: 1 Williams susceptible 10 0 0
#> 2: 1 Harlon Rps 1a 10 4 0
#> 3: 1 Harosoy 13xx Rps 1b 8 0 0
#> 4: 1 L75-3735 Rps 1c 10 10 0
#> 5: 1 PI 103091 Rps 1d 9 2 0
#> ---
#> 290: 21 PRX 145-48 Rps 3c 8 3 1
#> 291: 21 L85-2352 Rps 4 10 3 1
#> 292: 21 L85-3059 Rps 5 10 0 4
#> 293: 21 Harosoy 62XX Rps 6 8 2 1
#> 294: 21 Harosoy Rps 7 10 0 0
#> Lesion to cotyledon (3) Dead (4) total.susc total.resis perc.susc
#> 1: 0 10 10 0 100
#> 2: 0 6 6 4 60
#> 3: 0 8 8 0 100
#> 4: 0 0 0 10 0
#> 5: 1 6 7 2 78
#> ---
#> 290: 0 4 5 3 63
#> 291: 4 2 7 3 70
#> 292: 0 6 10 0 100
#> 293: 0 5 6 2 75
#> 294: 0 10 10 0 100
#> perc.resis
#> 1: 0
#> 2: 40
#> 3: 0
#> 4: 100
#> 5: 22
#> ---
#> 290: 38
#> 291: 30
#> 292: 0
#> 293: 25
#> 294: 0
# calculate susceptibilities with a 60 % cutoff value
diversities <- calculate_diversities(x = P_sojae_survey,
cutoff = 60,
control = "susceptible",
sample = "Isolate",
gene = "Rps",
perc_susc = "perc.susc")
diversities
#>
#> hagis Diversities
#>
#> Number of Samples 21
#> Number of Pathotypes 19
#>
#> Indices
#> Simple 0.9047619
#> Gleason 5.912257
#> Shannon 2.912494
#> Simpson 0.9433107
#> Evenness 0.9891509
#>
```