Attempts to show how the relationship between y and x is being modeled in a partition or random forest model
1 2 
TREE 
A partition or random forest model (though it works with many regression models as well) 
interest 
The name of the predictor variable for which the plot of y vs. x is to be made. 
on 
A dataframe giving the values of the other predictor variables for which the relationship is to be visualized. Typically this is the dataframe on which the partition model was built. 
smooth 
If 
marginal 
If 
nplots 
The number of rows of 
seed 
the seed for the random number seed if reproducibility is required 
pos 
the location of the legend 
... 
additional arguments past to 
The function shows a scatterplot of y vs. x in the on
dataframe, then shows how TREE
is modeling the relationship between y and x with predicted values of y for each row in the data and also a curve illustrating the relationship. It is useful for seeing what the relationship between y and x as modeled by TREE
"looks like", both as a whole and for particular combinations of other variables. If marginal
is FALSE
, then differences in the curves indicate the presence of some interaction between x and another variable.
Adam Petrie
Introduction to Regression and Modeling
1 2 3 4 5 6 7 8 9  data(SALARY)
FOREST < randomForest(Salary~.,data=SALARY)
visualize_relationship(FOREST,interest="Experience",on=SALARY)
visualize_relationship(FOREST,interest="Months",on=SALARY,xlim=c(1,15),ylim=c(2500,4500))
data(WINE)
TREE < rpart(Quality~.,data=WINE)
visualize_relationship(TREE,interest="alcohol",on=WINE,smooth=FALSE)
visualize_relationship(TREE,interest="alcohol",on=WINE,marginal=FALSE,nplots=7,smooth=FALSE)

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