I would like to make classification trees to predict the presence/absence of 1 bird species based on several variables. I know that rpart handles univariate partitioning and mvpart handles multivariate partitioning, but I'd like to use mvpart for my one-variable tree because of its more flexible output. Does anyone know of a reason that I should not do this? Will the splits be different in rpart vs mvpart with the same exact input?
Splitting rules in mvpart vs rpart
1.1k Views Asked by user2168122 At
1
There are 1 best solutions below
Related Questions in R
- in R, recovering strings that have been converted to factors with factor()
- How to reinstall pandoc after removing .cabal?
- How do I code a Mixed effects model for abalone growth in Aquaculture nutrition with nested individuals
- How to save t.test result in R to a txt file?
- how to call function from library in formula with R type provider
- geom_bar define border color with different fill colors
- Different outcome using model.matrix for a function in R
- Creating a combination data.table in R
- Force specific interactions in Package 'earth' in R
- Output from recursive function R
- Extract series of observations from dataframe for complete sets of data
- Retrieve path of supplementary data file of developed package
- r package development - own function not visible for opencpu
- Label a dataset according to bins of a histogram
- multiply each columns of a matrix by a vector
Related Questions in RPART
- Using ordinal variables in rpart and caret without converting to dummy categorical variables
- How can I get a plot in rpart to use observed values rather than weights
- How to return multiple objects of a library in R?
- Saving decision tree's output into a text file
- How do I make my tree plot in R to be less messy, so I can see the outcomes of the visualization? (image provided)
- Data manipulation makes lapply not work
- I get an error [invalid type (list) for variable '(weights)'] while using the rpart function for decision trees in R
- cart with rpart not displaying entire plot
- building classification tree having categorical variables using rpart
- In the as.party function how can I clarify which are the indices for the different nodes?
- Getting error "variable lengths differ (found for 'columns_features')" in R
- Prediction with RPART in R
- Looping predictions using different trees in rpart
- RPART explain the predict output for type matrix
- How to create a learning curve (bias/variance) from the output of caret::train
Related Questions in CART-ANALYSIS
- Add indval values on a multivariate regression tree
- Search for corresponding node in a regression tree using rpart
- Running regression tree on large dataset in R
- How to build a classification tree with only binary splits in each feature variable (preferably in R)?
- Splits and Root node of binary decision tree(CART)
- Choosing the values of cp, minsplit and maxdepth in CART
- rpart doesn't build a full tree – problems with cp?
- Strange Behavior for the predict() function
- Add conditioning variables to a random forest model in R
- Using weights for repeated cases in R (and specifically gam for binary response)
- Is it possible to use the evtree package in R for panel data / over multiple years?
- Is there an equivalence of "anova" (for lm) to an rpart object?
- How do I interpret rpart splits on factor variables when building classification trees in R?
- Follow example on R decision example
- rpart models collapse to zero splits in caret
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
It cannot be guaranteed that the splits will be the same;
mvpart()is minimising the within groups sums of squares whereasrpartfor a classification tree will be minimising the Gini coefficient (by default IIRC).You may end up with the same model/splits but as the two functions are using two different measures of node impurity this may just be a fluke.
FYI,
mvpartis fitting a regression model but you want a classification model.Finally, consider using the party package and its function
ctree; it has much nicer outputs thanrpartby default but is, again, doing something slightly different in terms of model fitting.As an aside, also look into the plotmo package which includes enhanced plots for a number of tree-like models including, IIRC,
rpartones.