Odtnhj

I've some data for fitting crude and adjusted logit GAMs:

library(mgcv)
## Simulate some data...
set.seed(3);n<-400
dat <- gamSim(1,n=n)
mu <- binomial()$linkinv(dat$f/4-2)
phi <- .5
a <- mu*phi;b <- phi - a;
dat$y <- rbeta(n,a,b) 

## Fitting GAMs
crude <- gam(y~s(x0),family=binomial(link="logit"),data=dat)
adj <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=binomial(link="logit"),data=dat)

Now I would intercept the value of x0 with the odds ratio (OR) 1.00 (i.e. probability 0.50). For this purpose I use visreg with argument plot = FALSE.

## Prepare data for ggplotting 
library(visreg)
p.crude <- visreg(crude, "x0", plot = FALSE)
p.adj <- visreg(adj, "x0", plot = FALSE)

library(dplyr)
bind_rows(
mutate(p.crude$fit, Model = "crude"),
mutate(p.adj$fit, Model = "adj")
) -> fits

Ok. I gonna compute OR from LogOR. Is the following code correct?

# Compute ORs and CI from LogOR
fits$or <- exp(fits$visregFit) 
fits$ci.low <- exp(fits$visregLwr) 
fits$ci.up <- exp(fits$visregUpr) 

Now I use approx in order to interpolate the x0 value with OR 1.00

## Interpolate x0 which give OR 1.00 (or 50% of probability)
x.crude <- round(approx(x = crude$fitted.values, y=crude$model$x0, xout = .5)$y, 1)
x.adj <- round(approx(x = adj$fitted.values, y=adj$model$x0, xout = .5)$y, 1)

Finally, I'm plotting the two models in a single graph:

## Plotting using ggplot
library(ggplot2)
ggplot(data = fits) +
 geom_vline(aes(xintercept = x.crude), size=.2, color="black")+
 geom_vline(aes(xintercept = x.adj), size=.2, color="red")+
 annotate(geom ="text", x= x.crude - 0.05, y=.5, label = x.crude, size=3.5) +
 annotate(geom ="text", x= x.adj - 0.05, y=.5, label = x.adj, size=3.5, color="red") +
 geom_ribbon(aes(x0, ymin=ci.low, ymax=ci.up, group=Model, fill=Model), alpha=.05) +
 geom_line(aes(x0, or, group=Model, color=Model)) +
 labs(x="X0", y="Odds ratio")+
 theme_bw(16) 

As you can see, only the crude model shows an intercept with OR almost equal to 1.00 (x0 = 0.9), while this never happens for the adj model.
First, how can I get an interpolation with OR that is exactly at 1?
Second...With the limitation of my statistical knowledge, it was my understanding that I should have observed an intercept with OR=1 for the adj model, as well, based on the observed values for x0 according to this model. Why is the relative curve set upwards?