David Zelený

en:monte_carlo_r

# Differences

This shows you the differences between two versions of the page.

 en:monte_carlo_r [2019/01/26 20:00]David Zelený en:monte_carlo_r [2019/02/10 16:04] (current)David Zelený [R functions] Both sides previous revision Previous revision 2019/02/10 16:04 David Zelený [R functions] 2019/01/26 20:00 David Zelený 2017/10/11 20:36 external edit2017/06/13 07:42 David Zelený [R functions] 2017/02/17 14:49 David Zelený 2017/02/17 14:49 David Zelený created 2019/02/10 16:04 David Zelený [R functions] 2019/01/26 20:00 David Zelený 2017/10/11 20:36 external edit2017/06/13 07:42 David Zelený [R functions] 2017/02/17 14:49 David Zelený 2017/02/17 14:49 David Zelený created Line 7: Line 7: [[{|width: 7em; background-color:​ white; color: navy}monte_carlo_exercise|Exercise {{::​lock-icon.png?​nolink|}}]] [[{|width: 7em; background-color:​ white; color: navy}monte_carlo_exercise|Exercise {{::​lock-icon.png?​nolink|}}]] - ==== R functions ==== + - * **''​anova.cca''​** (library ''​vegan''​) - tests the significance of the variation in species composition explained by explanatory variables in constrained ordination (RDA, CCA), using Monte Carlo permutation test. It can test the significance of + - * the global model (default setting), i.e. all variables included in the analysis; + - * only the first constrained axis (adding argument ''​first = TRUE''​);​ + - * individual axes (''​by = "​axis"''​),​ sequentially from the first to the last (this is done by using samples scores on the //n//-th axis as explanatory variables, while using scores of the axis 1, 2, ... //n// as covariables);​ + - * individual terms (explanatory variables) added sequentially in the order in which they appear in the formula or data frame (''​by = "​terms"'';​ note that this variation depends on the order of variables in which they enter the model); + - * variation explained by individual explanatory variables after removing variation of all other variables in the model (''​by = "​margin"'';​ here the variation does not depend on the order of variables in the model). +