In the following analysis are going to be considered the Spearman´s correlation and the Mann-Whitney analysis based on the characteristics of the database.

`summary(rafa)`

```
## D.H Hbacter cS100A12 SEI GPEI
## D:11 Min. :0.00 Min. :0.00 Min. :0 Min. :0
## H: 9 1st Qu.:1.00 1st Qu.:0.00 1st Qu.:0 1st Qu.:0
## Median :1.00 Median :0.00 Median :0 Median :0
## Mean :1.35 Mean :0.35 Mean :0 Mean :0
## 3rd Qu.:2.00 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:0
## Max. :3.00 Max. :1.00 Max. :0 Max. :0
## F.GN.MA IL LPL.Plm LPE LPN
## Min. :0.00 Min. :0.00 Min. :0.0 Min. :0 Min. :0.0
## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:1.0 1st Qu.:0 1st Qu.:0.0
## Median :0.00 Median :0.00 Median :1.0 Median :0 Median :0.0
## Mean :0.05 Mean :0.15 Mean :0.9 Mean :0 Mean :0.1
## 3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.:1.0 3rd Qu.:0 3rd Qu.:0.0
## Max. :1.00 Max. :1.00 Max. :2.0 Max. :0 Max. :1.0
## GLH
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
```

- SEI, GPEI, LPE, and GLH variables don´t have any values, therefore their values aren´t going to be taken into account.
- Ordinal variables are going to be analysed using Spearman Correlation. The interest variables Hbacter and cS100A12 are going to be related with all variables.

`corrplot(M, method="pie")`

`as.data.frame(datos)`

```
## Variables rho pvalues
## 1 cS100A12 0.04770849 0.8417
## 2 F.GN.MA 0.18793690 0.4275
## 3 IL 0.16569320 0.4851
## 4 LPL.Plm 0.18115950 0.4447
## 5 LPN 0.06068133 0.7994
```

`as.data.frame(datos1)`

```
## Variables rho pvalues
## 1 Hbacter 0.04770849 0.84170
## 2 F.GN.MA -0.16834510 0.47800
## 3 IL 0.27889960 0.23370
## 4 LPL.Plm 0.33833250 0.14450
## 5 LPN 0.45425680 0.04422
```

The most related variables are LPN and cS100A12 with a direct association(+). The relation between LPN and cS100A12 has an asociation strenght of 45% (Moderate). The probability to find a correlation coefficient r>=0.45 if the real r was 0 is 4% (p=0.04422)

We want to compare the mean of two independent groups, in this case would be the health variable with two possible results “Healthy” or “Disease”. Based on the ordinal and the non parametric behaviour of the variables we are going to choose the Mann-Whitney U test.

`as.data.frame(datos3)`

```
## Variables W pvalues
## 1 Hbacter 55.0 0.6901
## 2 cS100A12 58.0 0.4625
## 3 F.GN.MA 45.0 0.4214
## 4 IL 56.0 0.4617
## 5 LPL.Plm 56.5 0.5401
## 6 LPN 50.5 0.9418
```

The p-value is not significant for any of the variables, then, it means that healthy and disease patients can´t be differentiated with the values given in the Hbacter,cS100A12,F.GN.MA,IL,LPL.Plm, and LPN variables.