aLRT (parametric bootstrap)和 standard bootstrap(nonparametric bootstrap)的区别，aLRT 是phyML计算支持率的另外一种方法，其中Chi2-based
aLRT (approximate Likelihood-Ratio Test) for branches 得到的支持率比较松散，SH-like 得到的比较相近
 
-b (or --bootstrap) int
 
int = -1 : approximate likelihood ratio test returning aLRT statistics.
 
int = -2 : approximate likelihood ratio test returning Chi2-based parametric branch supports.
 
int = -3 : minimum of Chi2-based parametric and SH-like branch supports.
 
int = -4 : SH-like branch supports alone.
 
aLRT is a statistical test to compute branch supports. It applies to every (internal) branch and is computed along PhyML run on the original data set. Thus, aLRT is much faster than standard bootstrap which requires running PhyML 100-1,000 times with resampled data sets. As with any test, the aLRT branch support is significant when it is larger than 0.90-0.99. With good quality data (enough signal and sites), the sets of branches with bootstrap proportion >0.75 and aLRT>0 aLRT (approximate Likelihood-Ratio Test) for branches
-b (or --bootstrap) int
int = -1 : approximate likelihood ratio test returning aLRT statistics.
int = -2 : approximate likelihood ratio test returning Chi2-based parametric branch supports.
int = -3 : minimum of Chi2-based parametric and SH-like branch supports.
int = -4 : SH-like branch supports alone.
aLRT is a statistical test to compute branch supports. It applies to every (internal) branch and is computed along PhyML run on the original data set. Thus, aLRT is much faster than standard bootstrap which requires running PhyML 100-1,000 times with resampled data sets. As with any test, the aLRT branch support is significant when it is larger than 0.90-0.99. With good quality data (enough signal and sites), the sets of branches with bootstrap proportion >0.75 and aLRT>0.9 (SH-like option) tend to be similar. Perform bootstrap and number of resampled data sets
-b (or --bootstrap) int
int > 0 : int is the number of bootstrap replicates.
int = 0 : neither approximate likelihood ratio test nor bootstrap values are computed.
When there is only one data set you can ask PhyML to generate resampled bootstrap data sets from this original data set. PhyML then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format. The "Print pseudo trees" option gives the pseudo trees in a *_boot_trees.txt file. option) tend to be similar. Perform bootstrap and number of resampled data sets
 
-b (or --bootstrap) int
 
int > 0 : int is the number of bootstrap replicates.
 
int = 0 : neither approximate likelihood ratio test nor bootstrap values are computed.
 
When there is only one data set you can ask PhyML to generate resampled bootstrap data sets from this original data set. PhyML then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format. The "Print pseudo trees" option gives the pseudo trees in a *_boot_trees.txt file.

reference linking:

http://www.atgc-montpellier.fr/phyml/usersguide.php?type=command

http://www.atgc-montpellier.fr/phyml/alrt/