title: Obtaining p-values for RDA analysis using R package "vegan"
body: <p>I am conducting a Redundancy Analysis using R package "vegan". I would like to obtain p-values (or alternatively t-values) for the analysis to assess the significance of my predictor variables. I'm able to generate p-values using the anova function, separately for each RDA axis (axis) and each predictor (terms):</p>
  
  <pre><code>model&lt;-rda(precip ~ SOI + IOD + AAO, predictors)
  
  anova_axis&lt;-anova(model,by="axis")
  anova_terms&lt;-anova&lt;-anova(model,by="term")
  
  
  #### Outputs ####
  
  Model: rda(formula = precip ~ SOI + IOD + AAO, data = predictors)
            Df Var    F         N.Perm Pr(&gt;F)   
  RDA1      1  640315 6.1841    199    0.005 **
  RDA2      1  255421 2.4669    199    0.010 **
  RDA3      1   75211 0.7264     99    0.710   
  Residual  25 2588537  
  
  
  Model: rda(formula = precip ~ SOI + IOD + AAO, data = predictors)
            Df    Var      F     N.Perm Pr(&gt;F)   
  SOI       1  435080 4.2020     99     0.01 **
  IOD       1  419880 4.0552     99     0.01 **
  SAM_AAO   1  115987 1.1202     99     0.23   
  Residual 25 2588537                        
  </code></pre>
  
  <p>However, what I really need is p-values for each predictor, for each RDA axis. So a table of like this:</p>
  
  <pre><code>Predictor    RDA1    RDA2    RDA3
  SOI          xxx     xxx     xxx
  IOD          xxx     xxx     xxx
  AAO          xxx     xxx     xxx
  </code></pre>
  
  <p>Does anyone know how and whether this is possible to obtain? Any help would be much appreciated!</p>