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I have a N-way frequency table generated from a regression model fit. This is a reproducible example of such table:

data("CO2")
lm.fit = lm(uptake ~  Type + Treatment, data = CO2)
lm.fit$coefficients

test = count(CO2, c('Type','Treatment'))
test$res = predict(lm.fit, newdata = test)
test$freq = NULL

I am trying to visualize test as a decision tree with nodes as Type and Treatment and res as leaves. I would interpret it as the path the regression model takes, leading to the final value for a particular segment.

I am not able to generate a tree with test. I am also open to other novel ways of visualizing these results. My original problem has many categorical variables, so I am looking for a customizable visualization, something from party::ctree or rattle::fancyRpartPlot.

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You could try

library(data.tree)
test$pathString <- with(test, paste("lm", Type, Treatment, round(res, 2), sep="/"))
(tree <- as.Node(test))
#             levelName
# 1  lm                
# 2   ¦--Quebec        
# 3   ¦   ¦--nonchilled
# 4   ¦   ¦   °--36.97 
# 5   ¦   °--chilled   
# 6   ¦       °--30.11 
# 7   °--Mississippi   
# 8       ¦--nonchilled
# 9       ¦   °--24.31 
# 10      °--chilled   
# 11          °--17.45 
plot(tree)

The plot uses the DiagrammeR package.

enter image description here

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