Partial dependence plot for glm in r -- why linear? I'd like to understand why my partial dependence plots for a logistic regression model simply show up as straight lines -- even when I'd expect basically a threshold effect from a covariate. I know partial dependence plots are typical of machine learning, but the (excellent) description by the authors of the pdp] package suggest glms are fair game. So why does the relationship between outcome and effort (below) appear to be linear?
Here's a dummy dataset. Note that I forced higher values of effort for outcomes corresponding to 1 (a "win"). Also note that sometimes the algorithm won't converge -- if that's the case, just generate new data.
library(pdp)
library(randomForest)

# Sample game data
outcome <- as.vector(cbind(rep(0,25), rep(1,25)))
effort <- as.vector(cbind(rnorm(25, 25, 5), rnorm(25, 50, 10)))
skill <- rnorm(50, 50, 20)
game <- cbind(outcome, effort, skill) %>% as.data.frame()

# Simple glm
mod <- glm(outcome ~ effort + skill, data = game, family = binomial(link = "logit"))
summary(mod)
partial(mod, pred.var = c("effort"), plot = TRUE)

Call:
glm(formula = outcome ~ effort + skill, family = binomial(link = "logit"), 
    data = game)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.26979  -0.13985  -0.00751   0.01736   2.34734  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)   
(Intercept) -15.12758    5.73393  -2.638  0.00833 **
effort        0.50174    0.19218   2.611  0.00903 **
skill        -0.05414    0.05142  -1.053  0.29231   

Clearly, effort is going to be a strong predictor -- with way more wins (1s) associated with higher effort (given my data assignments). However, the partial dependence plot looks like this:  
partial(mod, pred.var = c("effort"), plot = TRUE)


If I use a random forest instead, that threshold effect shows up. (Yes, I know it throws a warning about using <5 unique response values in regression. It also shows up if you force outcome to be a factor.) 
rf <- randomForest(outcome ~ effort + skill, data = game)
partial(rf, pred.var = c("effort"), plot = TRUE)


My primary question here is not about which model is a better fit, but why the partial dependence is apparently linear with the logistic regression? Why doesn't that 30-40 range pop out as a threshold in the glm plot? Is that truly representing the relationship between game and effort in the model?
Thanks for any insights!
 A: A partial dependence plot for a logistic-type model is constructed by setting all but one feature to fixed, static values, varying the remaining feature throughout a range, and plotting:
$$ t \mapsto \log \left( \frac{p}{1-p} \right) $$
Where $p$ is the (probability) prediction for your model when the varied feature is set to the value $t$.  Note that, in particular, the $y$-axis of a partial dependency plot is measured on the log-odds scale, not the probability scale.
For a standard logistic regression, the functional form of your model is:
$$ \log \left( \frac{p}{1-p} \right) = \beta_0 + \beta_1 x_1 + \cdots + \beta_k x_k $$
So the form of the partial dependence plot is:
$$ t \mapsto \beta_j t + \text{constant} $$
where $j$ is the index of the feature you are constructing the partial dependence plot of.  This is why you get a line, the slope of that line is the parameter estimate $\hat \beta_j$ in the regression.
In a random forest the functional form of your model is:
$$ p = \text{average} \left( T_0(x), T_1(x), \ldots, T_{\text{n_trees}}(x) \right) $$
where the $T(x)$'s are the probability predictions from your individual classification trees.  So the partial dependence plot is the unwieldy:
$$  t \mapsto \log \left( \frac{p}{1-p} \right) = 
\frac{\text{average} \left( T_0(t), T_1(t), \ldots, T_{\text{n_trees}}(t) \right)}{1 - \text{average} \left( T_0(t), T_1(t), \ldots, T_{\text{n_trees}}(t) \right)} $$
This can be a very complicated, non-linear function of any individual feature, resulting in a vast multitude of possible shapes for the partial dependence plots.  The fact that you are seeing a soft threshold shape is due to the particulars of the problem you are solving, not something structural about partial dependence plots.
A: The pdp package allows converting back to probability scale by setting "prob = TRUE". You may also add the rugs to display the distribution of predictors.
partial(mod, pred.var = c("effort"), plot = TRUE, prob = TRUE, rug = TRUE)

