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Adding param CI in logit reg

Martin Horvat 6 月之前
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e4878dbb39
共有 3 个文件被更改,包括 258 次插入195 次删除
  1. 252 193
      python/logistic/logit_reg_fit_gen.ipynb
  2. 6 2
      python/logistic/logit_utils_gen.py
  3. 二进制
      python/logistic/results/logit_fit.pdf

文件差异内容过多而无法显示
+ 252 - 193
python/logistic/logit_reg_fit_gen.ipynb


+ 6 - 2
python/logistic/logit_utils_gen.py

@@ -12,7 +12,7 @@
         
         beta(pars) = [beta(pars)_j]_{j=0}^degree
     
-    where pars are regression parameters, len(pars) = degree + 1.
+    where pars are regression parameters and len(pars) = degree + 1.
     
     Coefficients beta(pars) can be constrained to be monotonic 
     function of x by using monotonic cubic transformation:
@@ -29,10 +29,14 @@
     
         s(y) = 2*y - 1
     
-    with x in R and y in {0,1}. For degree = 1 this is standard logistic regression
+    with x in R and y in {0,1}. For degree = 1 this is standard logistic regression 
     
         beta(pars) = (pars[0], pars[1]). 
     
+    and generally without monotonicity condition
+
+        beta(pars) = [pars_i]_{i=0}^degree
+
     For degree = 3 and mono = True the coefficients beta(pars) are constrained to be 
     monotonic by using monotonic cubic transformation.
 

二进制
python/logistic/results/logit_fit.pdf


部分文件因为文件数量过多而无法显示