search for: horserac

...116.46 0.14 0.00 0.00 5.14 5.00 118.50 118.97 5.00 119.77 0.00 0.00 0.14 4.57 4.14 120.74 121.03 4.00 116.81 0.14 0.29 0.00 4.86 3.57 117.63 117.40 5.00 117.66 0.14 0.14 0.14 4.57 4.71 119.19 120.57 7.00 #load Data crs<- read.csv("file:///C:/temp/Horse//horseracing.csv<file:///C:\temp\Horse\horseracing.csv>", na.strings=c(",", "NA", "", "?"), encoding="UTF-8") # # define x and y x= x<-crs[,9] #predictor variables y= y<-crs[1:8,] #response variable library(lars) cv.lars(x, y, K=10,...

Posted about this earlier. Didn't receive any response But, some further research leads me to believe that MAYBE a GLMM or a GEE function will do what I need. Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the coefficients are combined across all the training races to come up with a final set of coefficients. My understanding is that they maximize log likelihood across the entire set of training races. Yet this...

Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the coefficients are combined across all the training races to come up with a final set of coefficients. My understanding is that they maximize log likelihood across the entire set of training races. Yet this...