I have conducted a variance partitioning in R with the vegan
package to test how weather variables partition in the variance of the green-up day.
library(readr)
library(vegan)
data = read_csv('C:/path/data.csv)
out = varpart(data$greenup, ~ data$CAPE, ~ data$Temp, ~ data$precip, ~ data$WS)
out
Partition table:
Df R.square Adj.R.square Testable
[aeghklno] = X1 1 0.00873 0.00514 TRUE
[befiklmo] = X2 1 0.00357 -0.00004 TRUE
[cfgjlmno] = X3 1 0.03279 0.02929 TRUE
[dhijkmno] = X4 1 0.00029 -0.00333 TRUE
[abefghiklmno] = X1+X2 2 0.01935 0.01221 TRUE
[acefghjklmno] = X1+X3 2 0.03534 0.02832 TRUE
[adeghijklmno] = X1+X4 2 0.00878 0.00157 TRUE
[bcefgijklmno] = X2+X3 2 0.03652 0.02951 TRUE
[bdefhijklmno] = X2+X4 2 0.00621 -0.00102 TRUE
[cdfghijklmno] = X3+X4 2 0.03341 0.02638 TRUE
[abcefghijklmno] = X1+X2+X3 3 0.04303 0.03255 TRUE
[abdefghijklmno] = X1+X2+X4 3 0.02096 0.01024 TRUE
[acdefghijklmno] = X1+X3+X4 3 0.03547 0.02491 TRUE
[bcdefghijklmno] = X2+X3+X4 3 0.04023 0.02972 TRUE
[abcdefghijklmno] = All 4 0.04579 0.03181 TRUE
I would like to find if the variance partitioning of each variable is significant. I understand this is not feasible for the shared and residual parts.
Can I just look up critical values for Pearson R with 275 degrees of freedom at a 0.05 significance level? Or should I compute the p-value for each variable? If the latter, how?