title: best way to create response curves for a GLM species distribution model in R?
body: <p>I'm running a binomial GLM to predict the probability of a species occurrence, where I am training on one dataset and testing the model on another dataset: </p>
  
  <pre><code>TrainingData&lt;-read.csv("TrainingData.csv")[,-1]
  TrainingData[,1]&lt;-as.factor(TrainingData[,1])
  TrainingData[,4]&lt;-as.factor(TrainingData[,4])
  
  TestData&lt;-read.csv("TestData.csv")[,-1]
  TestData[,1]&lt;-as.factor(TestData[,1])
  TestData[,4]&lt;-as.factor(TestData[,4])
  
  mod&lt;-glm(presence~var1+var2+var3, family=binomial, data=TrainingData)
  
  probs=predict(mod, TestData, type="response")
  </code></pre>
  
  <p>What is the best way (or function) to create response curves to plot the relationship between the probability of presence and each predictor variable?</p>
  
  <p>Thanks! </p>