diff --git a/scripts/af_or_calcs.R b/scripts/af_or_calcs.R
index ddbf432..da5e784 100755
--- a/scripts/af_or_calcs.R
+++ b/scripts/af_or_calcs.R
@@ -388,7 +388,6 @@ pvals_logistic = sapply(gene_snps_unique,function(m){
   mut = grepl(m,raw_data$all_muts_gene)
   #print(table(dst, mut))
   model<-glm(dst ~ mut , family = binomial)
-  #or_logistic = exp(summary(model)$coefficients[2,1])
   pval_logistic = summary(model)$coefficients[2,4]
 })
 
diff --git a/scripts/af_or_calcs_scratch.R b/scripts/af_or_calcs_scratch.R
index 170532e..be88f7d 100644
--- a/scripts/af_or_calcs_scratch.R
+++ b/scripts/af_or_calcs_scratch.R
@@ -163,7 +163,7 @@ cat(paste0('Total no. of distinct comp snps to perform OR calcs: ', length(gene_
 # Define OR function
 #x = as.numeric(mut)
 #y = dst
-my_chisq_or = function(x,y){
+logistic_chisq_or = function(x,y){
   tab = as.matrix(table(x,y))
   a = tab[2,2]
   if (a==0){ a<-0.5}
@@ -214,7 +214,7 @@ chisq.test(table(mut,dst))
 fisher.test(table(mut, dst))
 fisher.test(table(mut, dst))$p.value
 fisher.test(table(mut, dst))$estimate
-my_chisq_or(mut,dst)
+logistic_chisq_or(mut,dst)
 
 # logistic or
 summary(model<-glm(dst ~ mut, family = binomial))
@@ -230,7 +230,7 @@ pval_logistic2 = summary(model2)$coefficients[2,4]; print(pval_logistic2)
 
 ors = sapply(gene_snps_unique,function(m){
   mut = grepl(m,raw_data$all_muts_gene)
-  my_chisq_or(mut,dst)
+  logistic_chisq_or(mut,dst)
 })
 
 ors
@@ -249,15 +249,69 @@ afs = sapply(gene_snps_unique,function(m){
 
 afs
 
-# logistic
-logistic_ors = sapply(gene_snps_unique,function(m){
+# logistic or
+ ors_logistic = sapply(gene_snps_unique,function(m){
   mut = grepl(m,raw_data$all_muts_gene)
   model<-glm(dst ~ mut, family = binomial)
   or_logistic = exp(summary(model)$coefficients[2,1])
   #pval_logistic = summary(model)$coefficients[2,4]
+  #logistic_se = summary(model)$coefficients[2,2]
+  #logistic_zval = summary(model)$coefficients[2,3]
+  #ci_mod = exp(confint(model))[2,]
+  #logistic_ci_lower = ci_mod[["2.5 %"]]
+  #logistic_ci_upper = ci_mod[["97.5 %"]]
 })
-logistic_ors
+ors_logistic
+head(ors_logistic); head(names(ors_logistic))
 
+## logistic p-value
+pvals_logistic = sapply(gene_snps_unique,function(m){
+  mut = grepl(m,raw_data$all_muts_gene)
+  model<-glm(dst ~ mut , family = binomial)
+  pval_logistic = summary(model)$coefficients[2,4]
+})
+
+head(pvals_logistic); head(names(pvals_logistic))
+
+## logistic se
+se_logistic = sapply(gene_snps_unique,function(m){
+  mut = grepl(m,raw_data$all_muts_gene)
+  model<-glm(dst ~ mut , family = binomial)
+  logistic_se = summary(model)$coefficients[2,2]
+})
+
+head(se_logistic); head(names(se_logistic))
+
+
+## logistic z-value
+zval_logistic = sapply(gene_snps_unique,function(m){
+  mut = grepl(m,raw_data$all_muts_gene)
+  model<-glm(dst ~ mut , family = binomial)
+  logistic_zval = summary(model)$coefficients[2,3]
+})
+
+head(zval_logistic); head(names(zval_logistic))
+
+## logistic ci - lower bound
+ci_lb_logistic = sapply(gene_snps_unique,function(m){
+  mut = grepl(m,raw_data$all_muts_gene)
+  model<-glm(dst ~ mut , family = binomial)
+  ci_mod = exp(confint(model))[2,]
+  logistic_ci_lower = ci_mod[["2.5 %"]]
+})
+
+head(ci_lb_logistic); head(names(ci_lb_logistic))
+
+## logistic ci - upper bound
+ci_ub_logistic = sapply(gene_snps_unique,function(m){
+  mut = grepl(m,raw_data$all_muts_gene)
+  model<-glm(dst ~ mut , family = binomial)
+  ci_mod = exp(confint(model))[2,]
+  logistic_ci_upper = ci_mod[["97.5 %"]]
+
+})
+
+head(ci_ub_logistic); head(names(ci_ub_logistic))
 
 # logistic adj # Doesn't seem to make a difference
 logistic_ors2 = sapply(gene_snps_unique,function(m){
@@ -276,16 +330,35 @@ or_logistic2; pval_logistic2
 
 head(logistic_ors)
 #====================================
-
 # logistic
 summary(model<-glm(dst ~ mut
                    , family = binomial
                    #, control = glm.control(maxit = 1)
                    #, options(warn = 1)
                    ))
-or_logistic_maxit = exp(summary(model)$coefficients[2,1]); print(or_logistic_maxit)
+or_logistic = exp(summary(model)$coefficients[2,1]); print(or_logistic)
 pval_logistic_maxit = summary(model)$coefficients[2,4]; print(pval_logistic_maxit)
 
+# extract SE of the logistic model for a given snp
+logistic_se = summary(model)$coefficients[2,2]
+print(paste0('SE:', logistic_se))
+  
+  
+# extract Z of the logistic model for a given snp
+logistic_zval = summary(model)$coefficients[2,3]
+print(paste0('Z-value:', logistic_zval))
+  
+  
+# extract confint interval of snp (2 steps, since the output is a named number)
+ci_mod = exp(confint(model))[2,]
+print(paste0('CI:', ci_mod))
+#logistic_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
+  
+logistic_ci_lower = ci_mod[["2.5 %"]]
+logistic_ci_upper = ci_mod[["97.5 %"]]
+  
+print(paste0('CI_lower:', logistic_ci_lower))
+print(paste0('CI_upper:', logistic_ci_upper))
 
 #####################
 # iterate: subset
@@ -296,10 +369,6 @@ snps_test = c("pnca_p.trp68gly", "pnca_p.leu4ser", "pnca_p.leu159arg","pnca_p.hi
 data = snps_test[1:2]
 
 data
-
-
-
-
 ################# start loop
 for (i in data){
   
@@ -328,9 +397,14 @@ for (i in data){
                      #, control = glm.control(maxit = 1)
                      #, options(warn = 1)
                      ))
-  or_logistic_maxit = exp(summary(model)$coefficients[2,1]); print(or_logistic_maxit)
+                     #, warning = perfectSeparation))
+  or_logistic = exp(summary(model)$coefficients[2,1]); print(or_logistic)
   pval_logistic_maxit = summary(model)$coefficients[2,4]; print(pval_logistic_maxit)
-  
+  logistic_se = summary(model)$coefficients[2,2]
+  logistic_zval = summary(model)$coefficients[2,3]
+  ci_mod = exp(confint(model))[2,]
+  logistic_ci_lower = ci_mod[["2.5 %"]]
+  logistic_ci_upper = ci_mod[["97.5 %"]]
   #=====================
   # fishers test
   #=====================
@@ -353,11 +427,9 @@ for (i in data){
   # all output 
   writeLines(c(paste0("mutation:", i)
                , paste0("=========================")
-               , paste0("OR_logistic_maxit:", or_logistic_maxit,"--->", "P-val_logistic_maxit:", pval_logistic_maxit )
+               , paste0("or_logistic:", or_logistic,"--->", "P-val_logistic_maxit:", pval_logistic_maxit )
                , paste0("OR_fisher:", or_fisher, "--->","P-val_fisher:", pval_fisher )
                , paste0("Chi_sq_estimate:", est_chisq, "--->","P-val_chisq:", pval_chisq)))
-    
-
 }
 
 
@@ -382,4 +454,4 @@ table(dst, mut)
  # https://stats.stackexchange.com/questions/259635/what-is-the-difference-using-a-fishers-exact-test-vs-a-logistic-regression-for
   exact2x2(table(dst, mut),tsmethod="central")
   
-  
\ No newline at end of file
+  
diff --git a/scripts/combine_afs_ors.py b/scripts/combine_afs_ors.py
index 253260a..e32595e 100755
--- a/scripts/combine_afs_ors.py
+++ b/scripts/combine_afs_ors.py
@@ -308,33 +308,25 @@ print('Finished writing file:', outfile
       , '\nNo. of cols:', len(combined_or_df.columns)
       , '\n=========================================================')
 
-
-
-
-
-
-
-#%%
+#%% practice
 df = pd.DataFrame()
 column_names = ['x','y','z','mean']
 for col in column_names: 
     df[col] = np.random.randint(0,100, size=10000)
 df.head()
 
-
 # drop duplicate col with dup values not necessarily colnames
 df['xdup'] = df['x']
 df
 
-df.T.drop_duplicates().T
+df = df.T.drop_duplicates().T
 
-import math
+#import math
 math.exp(0)
 
 df['expX'] = np.exp(df['x']) # math doesn't understand series dtype
 df
-
-
+#%%