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https://help.kajabi.com/hc/en-us/articles/360045291453-Detaching-Stripe-and-PayPal-From-Kajabi

 

 

 

Common wealth

https://www.globalatlantic.com/commonwealth/policyholders

 

Odds ratio in SAS PROC LOGISTICS are just

exp(coefficient)

This is obvious for binary variables.

Even for continuous variables, it's just

exp(coefficient)

..which means that I think it is mot counterintuitive to standardize continuous variables.

So one easy way to get odds ratios is to run PROC LOGISTICS with continuous variables standardized and use exp(X) in Excel.

ods trace on;
proc logistic data=final DESCENDING;
model YA01_3_bin =
treat
male
age_log
CH_compass
/*R_compass*/
white
black
/*other_race*/;
ods output ParameterEstimates=kaz1;
run;

data kaz1b;
set kaz1;
if Variable="Intercept" or Variable="treat";
keep Variable Estimate;
run;
proc transpose data=kaz1b out=kaz1bt;
id Variable;
run;

data kaz1bt2;
set kaz1bt;
C_LOGIT=intercept;
T_LOGIT=intercept+treat;
C_EXP=exp(C_LOGIT)/(1+exp(C_LOGIT));
C_ODDS=C_EXP/(1-C_EXP);
C_STEP1=log(C_ODDS);

T_EXP=exp(T_LOGIT)/(1+exp(T_LOGIT));
T_ODDS=T_EXP/(1-T_EXP);
T_STEP1=log(T_ODDS);

STEP2=T_STEP1-C_STEP1;
wwc_effect_size=STEP2/1.65;
odds_ratio=T_ODDS/C_ODDS;
run;

https://ies.ed.gov/ncee/wwc/Docs/referenceresources/WWC_Procedures_Handbook_V4_1_Draft.pdf

See Page 16.

two_values<-view1b[3:4]
num<-view1b[2]

omega<-(1-3/(4*num-9))

C_LOGIT<-two_values[1]
C_EXP<-exp(C_LOGIT)/(1+exp(C_LOGIT))
C_ODDS<-C_EXP/(1-C_EXP)
C_STEP1<-log(C_ODDS)
T_LOGIT<-two_values[1]+two_values[2]
T_EXP<-exp(T_LOGIT)/(1+exp(T_LOGIT))
T_ODDS<-T_EXP/(1-T_EXP)
T_STEP1<-log(T_ODDS)
STEP2<-T_STEP1-C_STEP1
wwc_effect_size<-STEP2/1.65

wwc_effect_size_omega<-(omega*STEP2)/1.65

odds_ratio<-T_ODDS/C_ODDS

 

if datepart(OffenseDate) > s_date;

 

 

data crim;
set abc.crim_history;
format new_date MMDDYY10.;
format s_date MMDDYY10.;
new_date=datepart(OffenseDate);
s_date=mdy(1,1,2019);
instruction="Drop ";
if new_date > s_date then instruction="Keep";
*if datepart(OffenseDate) > s_date;
run;

#clear console
rm(list = ls())

setwd("C:/sas/(01) D/newdata")

crim<- read_excel("HistoryCombined.xlsx",sheet="History ")

#Creating the new dataset after WV review -- I added dual credit info per grade level
write.foreign(crim, "C:/sas/(01) D/newdata/data.txt",
"C:/sas/(01) D/newdata/data.sas", package="SAS")

 

Packages I use are:

library(broom)
library(compute.es)
library(dplyr)
library(forcats)
library(FSA)
library(gapminder)
library(ggplot2)
library(gmodels)
library(haven)
library(here)
library(leaflet)
library(magrittr)
library(markdown)
library(MatchIt)
library(plyr)
library(psych)
library(purrr)
library(readr)
library(readxl)
library(sas7bdat)
library(sf)
library(sqldf)
library(stringr)
library(summarytools)
library(tidyverse)
library(tmap)
library(tmaptools)
library(descr)
library(MatchIt)
library(sjmisc)
library(writexl)
library(skimr)
library(lme4)
library(lmerTest)
library(car)
library(foreign)

https://note.com/canvajapan/n/n91bb5e32e683

 

#https://stats.oarc.ucla.edu/r/dae/logit-regression/
mylogit1 <- glm(enroll_FR_spring ~ treat+ male +minority +disadv +binary_dualcredit+SAT_TOTAL + GPA_12_GRADE +FALL_2020_INSTNAME, data = sample, family = "binomial")
summary(mylogit1)

coef_table<-(coef(mylogit1))
two_values<-coef_table[1:2]
C_LOGIT<-two_values[1]
C_EXP<-exp(C_LOGIT)/(1+exp(C_LOGIT))
C_ODDS<-C_EXP/(1-C_EXP)
C_STEP1<-log(C_ODDS)
T_LOGIT<-two_values[1]+two_values[2]
T_EXP<-exp(T_LOGIT)/(1+exp(T_LOGIT))
T_ODDS<-T_EXP/(1-T_EXP)
T_STEP1<-log(T_ODDS)
STEP2<-T_STEP1-C_STEP1
wwc_effect_size<-STEP2/1.65
odds_ratio<-T_ODDS/C_ODDS