Explaining the fish diversity in rivers

Methods

We used Partial Least Squares Path Modeling using the package plspm in the R environment (Sanchez, Trinchera, and Russolillo 2017, R Core Team (2018)) to make all the analysis

linear Models

Checking for unidimensionality

Since Productivity, Energy and History are reflective in nature, we need to check for unidimensionality

MVs	C.alpha	DG.rho	eig.1st	eig.2nd	Factor
2	0	0	1.403751	0.5962492	Productivity
1	1	1	1.000000	0.0000000	Energy
1	1	1	1.000000	0.0000000	History
1	1	1	1.000000	0.0000000	Diversity

Usually we would expect Cronbach’s alpha values to be over 0.7 to be used, in this case only productivuty does not have that value, so we will have to keep checking that variable, the same goes for Dillon-Goldstein’s rho, and we still find that productivity has values less than 0.7

Checking the loadings

Goodness of fit of the model

The goodness of fit of the model is 0.5195781

Log models

Fig 1:Ilustration of basin area model

## [1] 0.4226545

References

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Sanchez, Gaston, Laura Trinchera, and Giorgio Russolillo. 2017. Plspm: Tools for Partial Least Squares Path Modeling (Pls-Pm). https://CRAN.R-project.org/package=plspm.