I am confused by the meaning of Adjusted R2 on page 263 in the Phoenix Winnolin user guide. I have two questions about adjusted R2.
here is the sentence.
In the Rsq_adjusted field, type 0.97 to flag any profile with an Rsq_adjusted value greater than or equal to this value.
Profiles that break the rule are flagged in the output and can be quickly filtered out of the results. The process will be illustrated later in this example.
Based on the sentences above, all the profiles whose Adjusted R2 are bigger than 0.97 will be excluded for calculation PK parameters.
1. is my understanding right or not?
Another, the meaning of adjusted R2 online is below.
R2 shows how well terms (data points) fit a curve or line. Adjusted R2 also indicates how well terms fit a curve or line, but adjusts for the number of terms in a model. If you add more and more useless variables to a model, adjusted r-squared will decrease. If you add more useful variables, adjusted r-squared will increase. Adjusted R2 will always be less than or equal to R2.
According to the meaning of adjusted R2 here, the higher R2, the more number of variables is useful for modeling.
2. Am I right on this concept?
Thank you vey much in advance!