glm(Decision ~ Thoughts, family = binomial, data = data)

exp(coef(results))

library('MASS')
data("menarche")
m<-glm(cbind(Menarche, Total-Menarche) ~ Age, family=binomial, data=menarche)
summary(m)

Call:
glm(formula = cbind(Menarche, Total - Menarche) ~ Age, family = binomial,
    data = menarche)

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-2.0363  -0.9953  -0.4900   0.7780   1.3675

Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept) -21.22639    0.77068  -27.54   <2e-16 ***
Age           1.63197    0.05895   27.68   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 3693.884  on 24  degrees of freedom
Residual deviance:   26.703  on 23  degrees of freedom
AIC: 114.76

Number of Fisher Scoring iterations: 4

#predict gives the predicted value in terms of logits
plot.dat <- data.frame(prob = menarche$Menarche/menarche$Total,
                       age = menarche$Age,
                       fit = predict(m, menarche))
#convert those logit values to probabilities
plot.dat$fit_prob <- exp(plot.dat$fit)/(1+exp(plot.dat$fit))

library(ggplot2)
ggplot(plot.dat, aes(x=age, y=prob)) +
  geom_point() +
  geom_line(aes(x=age, y=fit_prob))

exp(coef(m))

 (Intercept)          Age
6.046358e-10 5.113931e+00

exp(intercept + coef*THOUGHT_Value)/(1+(exp(intercept+coef*THOUGHT_Value))

library(oddsratio)
fit_glm <- glm(admit ~ gre + gpa + rank, data = data_glm, family = "binomial")

# Calculate OR for specific increment step of continuous variable
or_glm(data = data_glm, model = fit_glm,
       incr = list(gre = 380, gpa = 5))

  predictor oddsratio CI.low (2.5 %) CI.high (97.5 %)          increment
1       gre     2.364          1.054            5.396                380
2       gpa    55.712          2.229         1511.282                  5
3     rank2     0.509          0.272            0.945 Indicator variable
4     rank3     0.262          0.132            0.512 Indicator variable
5     rank4     0.212          0.091            0.471 Indicator variable