search for: 36.28

Hello R-help Below is the output from the predict() function. How can I assign $y to a variable. >predict(function,df2) $x        V1 1   36.28 2   34.73 3   33.74 4   69.87 5   58.88 6   89.44 7   43.97 8   41.94 9   33.34 10  38.47 11  35.16 12  42.94 13  46.76 14  53.24 15  52.43 16  50.40 17  34.42 18  33.22 19  33.24 20  39.60 21  39.32 22  44.71 23  54.03 24  47.48 25  35.42 26  34.78

[cc'ing back to r-help: this is good etiquette so that the responses will be seen by others/ archived for future reference.] On 10-08-25 04:35 PM, Marino Taussig De Bodonia, Agnese wrote: > Yes, I meant "MuMIn" > > the global formula I introduced was: > > rc4.mod<-lm(central$hunting~ central$year + central$gender + central$hunter + central$k.score +

Instrumentation based Profile Guided Optimization (PGO) is a compiler technique that leverages important program runtime information, such as precise edge counts and frequent value information, to make frequently executed code run faster. It's proven to be one of the most effective ways to improve program performance. An important design point of PGO is to decide where to place the

Accidentally sent to uiuc server. On Fri, Aug 7, 2015 at 10:49 PM, Sean Silva <chisophugis at gmail.com> wrote: > Can you compare your results with another approach: simply do not > instrument the top 1% hottest functions (by function entry count)? If this > simple approach provides most of the benefits (my measurements on one > codebase I tested show that it would eliminate

On Sat, Aug 8, 2015 at 6:31 AM, Xinliang David Li <davidxl at google.com> wrote: > On Fri, Aug 7, 2015 at 10:56 PM, Sean Silva <chisophugis at gmail.com> wrote: > > Accidentally sent to uiuc server. > > > > > > On Fri, Aug 7, 2015 at 10:49 PM, Sean Silva <chisophugis at gmail.com> > wrote: > >> > >> Can you compare your results