Multivariate Adaptive Regression Splines

Build a regression model using the techniques in Friedman's papers "Multivariate Adaptive Regression Splines" and "Fast MARS". See the package vignette “Notes on the earth package”.

models, regression, smooth
"earth"(formula = stop("no 'formula' argument"), data = NULL, weights = NULL, wp = NULL, subset = NULL, na.action =, pmethod = c("backward", "none", "exhaustive", "forward", "seqrep", "cv"), keepxy = FALSE, trace = 0, glm = NULL, degree = 1, nprune = NULL, ncross=1, nfold=0, stratify=TRUE, varmod.method = "none", varmod.exponent = 1, varmod.conv = 1, varmod.clamp = .1, varmod.minspan = -3, Scale.y = (NCOL(y)==1), ...)
"earth"(x = stop("no 'x' argument"), y = stop("no 'y' argument"), weights = NULL, wp = NULL, subset = NULL, na.action =, pmethod = c("backward", "none", "exhaustive", "forward", "seqrep", "cv"), keepxy = FALSE, trace = 0, glm = NULL, degree = 1, nprune = NULL, ncross=1, nfold=0, stratify=TRUE, varmod.method = "none", varmod.exponent = 1, varmod.conv = 1, varmod.clamp = .1, varmod.minspan = -3, Scale.y = (NCOL(y)==1), ...)
"earth"(x = stop("no 'x' argument"), y = stop("no 'y' argument"), weights = NULL, wp = NULL, subset = NULL, na.action =, pmethod = c("backward", "none", "exhaustive", "forward", "seqrep", "cv"), keepxy = FALSE, trace = 0, glm = NULL, degree = 1, penalty = if(degree > 1) 3 else 2, nk = min(200, max(20, 2 * ncol(x))) + 1, thresh = 0.001, minspan = 0, endspan = 0, newvar.penalty = 0, fast.k = 20, fast.beta = 1, linpreds = FALSE, allowed = NULL, nprune = NULL, Object = NULL, Scale.y = (NCOL(y)==1), Adjust.endspan = 2, Force.weights = FALSE, Use.beta.cache = TRUE, Force.xtx.prune = FALSE, Get.leverages = NROW(x) < 1e5, Exhaustive.tol = 1e-10, ...)
Model formula.
Data frame for formula.
Matrix or dataframe containing the independent variables.
Vector containing the response variable, or, in the case of multiple responses, a matrix or dataframe whose columns are the values for each response.
Index vector specifying which cases to use, i.e., which rows in x to use. Default is NULL, meaning all.
Case weights. Default is NULL, meaning no case weights. If specified, weights must have length equal to nrow(x) before applying subset. Zero weights are converted to a very small nonzero value.
Response weights. Default is NULL, meaning no response weights. If specified, wp must have an element for each column of y (after factors in y, if any, have been expanded). Zero weights are converted to a very small nonzero value.
NA action. Default is, and only is supported.
Default is FALSE. Set to TRUE to retain the following in the returned value: x and y (or data), subset, and weights. The function and friends will use these if present instead of searching for them in the environment at the time is invoked. When the nfold argument is used with keepxy=TRUE, earth keeps more data and calls multiple times to generate and (see the cv. arguments in the “Value” section below). It therefore makes cross-validation significantly slower.
Trace earth's execution. Default is 0. Values: 0 no tracing .3 variance model (the varmod.method arg) .5 cross validation (the nfold arg) 1 overview 2 forward pass 3 pruning 4 model mats summary, pruning details 5 full model mats, internal details of operation
NULL (default) or a list of arguments to pass on to glm. See the documentation of glm for a description of these arguments See “Generalized linear models” in the vignette. Example: earth(survived~., data=etitanic, degree=2, glm=list(family=binomial)) The following arguments are for the forward pass.
Maximum degree of interaction (Friedman's $mi$). Default is 1, meaning build an additive model (i.e., no interaction terms).
Generalized Cross Validation (GCV) penalty per knot. Default is if(degree>1) 3 else 2. Simulation studies suggest values in the range of about 2 to 4. The FAQ section in the vignette has some information on GCVs. Special values (for use by knowledgeable users): The value 0 penalizes only terms, not knots. The value -1 means no penalty, so GCV = RSS/n.
Maximum number of model terms before pruning, i.e., the maximum number of terms created by the forward pass. Includes the intercept. The actual number of terms created by the forward pass will often be less than nk because of other stopping conditions. See “Termination conditions for the forward pass” in the vignette. The default is semi-automatically calculated from the number of predictors but may need adjusting.
Forward stepping threshold. Default is 0.001. This is one of the arguments used to decide when forward stepping should terminate: the forward pass terminates if adding a term changes RSq by less than thresh. See “Termination conditions for the forward pass” in the vignette.
Minimum number of observations between knots. (This increases resistance to runs of correlated noise in the input data.) The default minspan=0 is treated specially and means calculate the minspan internally, as per Friedman's MARS paper section 3.8 with $alpha$ = 0.05. Set trace>=2 to see the calculated value. Use minspan=1 and endspan=1 to consider all x values. Negative values of minspan specify the maximum number of knots per predictor. These will be equally spaced. For example, minspan=-3 allows three evenly spaced knots for each predictor. As always, knots that fall in the endzones specified by endspan will be ignored.
Minimum number of observations before the first and after the final knot. The default endspan=0 is treated specially and means calculate the minspan internally, as per the MARS paper equation 45 with $alpha$ = 0.05. Set trace>=2 to see the calculated value. Be wary of reducing endspan, especially if you plan to make predictions beyond or near the limits of the training data. Overfitting near the edges of training data is much more likely with a small endspan. The model's RSq and GRSq won't indicate when this overfitting is occurring. (A plotmo plot can help: look for sharp hinges at the edges of the data). See also the Adjust.endspan argumen.
Penalty for adding a new variable in the forward pass (Friedman's $gamma$, equation 74 in the MARS paper). Default is 0, meaning no penalty for adding a new variable. Useful non-zero values typically range from about 0.01 to 0.2 and sometimes higher --- you will need to experiment. A word of explanation. With the default newvar.penalty=0, if two variables have nearly the same effect (e.g. they are collinear), at any step in the forward pass earth will arbitrarily select one or the other (depending on noise in the sample). Both variables can appear in the final model, complicating model interpretation. On the other hand with a non-zero newvar.penalty, the forward pass will be reluctant to add a new variable --- it will rather try to use a variable already in the model, if that does not affect RSq too much. The resulting final model may be easier to interpret, if you are lucky. There will often be a small performance hit (a worse GCV).
Maximum number of parent terms considered at each step of the forward pass. (This speeds up the forward pass. See the Fast MARS paper section 3.0.) Default is 20. A value of 0 is treated specially (as being equivalent to infinity), meaning no Fast MARS. Typical values, apart from 0, are 20, 10, or 5. In general, with a lower fast.k (say 5), earth is faster; with a higher fast.k, or with fast.k disabled (set to 0), earth builds a better model. However, because of random variation this general rule often doesn't apply.
Fast MARS ageing coefficient, as described in the Fast MARS paper section 3.1. Default is 1. A value of 0 sometimes gives better results.
Index vector specifying which predictors should enter linearly, as in lm. The default is FALSE, meaning all predictors enter in the standard MARS fashion, i.e., in hinge functions. This does not say that a predictor must enter the model; only that if it enters, it enters linearly. See “The linpreds argument” in the vignette. A predictor's index in linpreds is the column number in the input matrix x (after factors have been expanded). linpreds=TRUE makes all predictors enter linearly (the TRUE gets recycled). linpreds may also be a character vector e.g. linpreds=c("wind", "vis"). Note: grep is used for matching. Thus "wind" will match all variables that have "wind" in their names. Use "^wind$" to match only the variable named "wind".
Function specifying which predictors can interact and how. Default is NULL, meaning all standard MARS terms are allowed. During the forward pass, earth calls the allowed function before considering a term for inclusion; the term can go into the model only if the allowed function returns TRUE. See “The allowed argument” in the vignette. The following arguments are for the pruning pass.
Pruning method. One of: backward none exhaustive forward seqrep cv. Default is "backward". New in version 4.4.0: Specify pmethod="cv" to use cross-validation to select the number of terms. This selects the number of terms that gives the maximum mean out-of-fold RSq on the fold models. Requires the nfold argument. Use "none" to retain all the terms created by the forward pass. If y has multiple columns, then only "backward" or "none" is allowed. Pruning can take a while if "exhaustive" is chosen and the model is big (more than about 30 terms). The current version of the leaps package used during pruning does not allow user interrupts (i.e., you have to kill your R session to interrupt; in Windows use the Task Manager or from the command line use taskkill).
Maximum number of terms (including intercept) in the pruned model. Default is NULL, meaning all terms created by the forward pass (but typically not all terms will remain after pruning). Use this to enforce an upper bound on the model size (that is less than nk), or to reduce exhaustive search time with pmethod="exhaustive". The following arguments are for cross validation.
Only applies if nfold>1. Number of cross-validations. Each cross-validation has nfold folds. Default 1.
Number of cross-validation folds. Default is 0, no cross validation. If greater than 1, earth first builds a standard model as usual with all the data. It then builds nfold cross-validated models, measuring R-Squared on the out-of-fold (left out) data each time. The final cross validation R-Squared (CVRSq) is the mean of these out-of-fold R-Squareds. The above process of building nfold models is repeated ncross times (by default, once). Use trace=.5 to trace cross-validation. Further statistics are calculated if keepxy=TRUE or if a binomial or poisson model (specified with the glm argument). See “Cross validation” in the vignette.
Only applies if nfold>1. Default is TRUE. Stratify the cross-validation samples so that an approximately equal number of cases with a non-zero response occur in each cross validation subset. So if the response y is logical, the TRUEs will be spread evenly across folds. And if the response is a multilevel factor, there will be an approximately equal number of each factor level in each fold (because a multilevel factor response gets expanded to columns of zeros and ones, see “Factors” in the vignette). We say “approximately equal” because the number of occurrences of a factor level may not be exactly divisible by the number of folds. The following arguments are for variance models (new in version 4.0.0).
Construct a variance model. For details, see varmod and the vignette “Variance models in earth”. Use trace=.3 to trace construction of the variance model. This argument requires nfold and ncross. (We suggest at least ncross=30 here to properly calculate the variance of the errors --- although you can use a smaller value, say 3, for debugging.) The varmod.method argument should be one of "none" Default. Don't build a variance model. "const" Assume homoscedastic errors. "lm" Use lm to estimate standard deviation as a function of the predicted response. "rlm" Use rlm. "earth" Use earth. "gam" Use gam. This will use either gam or the mgcv package, whichever is loaded. "power" Estimate standard deviation as intercept + coef * predicted.response^exponent, where intercept, coef, and exponent will be estimated by nls. This is equivalent to varmod.method="lm" except that exponent is automatically estimated instead of being held at the value set by the varmod.exponent argument. "power0" Same as "power" but no intercept (offset) term. "x.lm", "x.rlm", "", "x.gam" Like the similarly named options above, but estimate standard deviation by regressing on the predictors x (instead of the predicted response). A current implementation restriction is that "x.gam" allows only models with one predictor (x must have only one column).
Power transform applied to the rhs before regressing the absolute residuals with the specified varmod.method. Default is 1. For example, with varmod.method="lm", if you expect the standard deviance to increase linearly with the mean response, use varmod.exponent=1. If you expect the standard deviance to increase with the square root of the mean response, use varmod.exponent=.5 (where negative response values will be treated as 0, and you will get an error message if more than 20% of them are negative).
Convergence criterion for the Iteratively Reweighted Least Squares used when creating the variance model. Iterations stop when the mean value of the coefficients of the residual model change by less than varmod.conv percent. Default is 1 percent. Negative values force the specified number of iterations, e.g. varmod.conv=-2 means iterate twice. Positive values are ignored for varmod="const" and also currently ignored for varmod="earth" (these are iterated just once, the same as using varmod.conv=-1).
The estimated standard deviation of the main model errors is forced to be at least a small positive value, which we call This prevents negative or absurdly small estimated standard deviations. Clamping takes place in predict.varmod, which is called by when estimating prediction intervals. The value of is determined when building the variance model as = varmod.clamp * mean(sd(training.residuals)). The default varmod.clamp is 0.1.
Only applies when varmod.method="earth" or "". This is the minspan used in the internal call to earth when creating the variance model (not the main earth model). Default is -3, i.e., three evenly spaced knots per predictor. Residuals tend to be very noisy, and allowing only this small number of knots helps prevent overfitting. The following arguments are for internal or advanced use.
Earth object to be updated, for use by
Scale y internally in the forward pass for better numeric stability. This is invisible to the user, up to numerical differences. Scaling here means subtract the mean and divide by the standard deviation. Default is NCOL(y)==1, i.e., scale y unless y has multiple columns.
New in version 4.2.0. In interaction terms, endspan gets multiplied by this value. This reduces the possibility of an overfitted interaction term supported by just a few cases on the boundary of the predictor space (as sometimes seen in our simulation studies). The default is 2. Use Adjust.endspan=1 for compatibility with old versions of earth.
Default is FALSE. For testing the weights argument. Force use of the code for handling weights in the earth code, even if weights=NULL or all the weights are the same. This will not necessarily generate an identical model, primarily because the non-weighted code requires some tests for numerical stability that can sometimes affect knot selection.
Default is TRUE. Using the “beta cache” takes a little more memory but is faster (by 20% and often much more for large models). The beta cache uses nk * nk * ncol(x) * sizeof(double) bytes. (The beta cache is an innovation in this implementation of MARS and does not appear in Friedman's papers. It is not related to the fast.beta argument. Certain regression coefficients in the forward pass can be saved and re-used, thus saving recalculation time.)
Default is FALSE. This argument pertains to subset evaluation in the pruning pass. By default, if y has a single column then earth calls the leaps routines; if y has multiple columns then earth calls EvalSubsetsUsingXtx. The leaps routines are numerically more stable but do not support multiple responses (leaps is based on the QR decomposition and EvalSubsetsUsingXtx is based on the inverse of X'X). Setting Force.xtx.prune=TRUE forces use of EvalSubsetsUsingXtx, even if y has a single column.
New in version 4.4.0. Default is TRUE unless the model has more than 100 thousand cases. The leverages are the diagonal hat values for the linear regression of y on bx. The leverages are needed only for certain model checks, for example when plotres is called with versus=4). Details: This argument was introduced to reduce peak memory usage. When n >> p, memory use peaks when earth is calculating the leverages.
Default 1e-10. Applies only when pmethod="exhaustive". If the reciprocal of the condition number of bx is less than Exhaustive.tol, earth forces pmethod="backward". See “XHAUST returned error code -999” in the vignette.
Dots are passed on to

An S3 model of class "earth". See earth.object for a complete description.


The primary references are the Friedman papers. Readers may find the MARS section in Hastie, Tibshirani, and Friedman a more accessible introduction. The Wikipedia article is recommended for an elementary introduction. Faraway takes a hands-on approach, using the ozone data to compare mda::mars with other techniques. (If you use Faraway's examples with earth instead of mars, use $bx instead of $x, and check out the book's errata.) Friedman and Silverman is recommended background reading for the MARS paper. Earth's pruning pass uses code from the leaps package which is based on techniques in Miller. Faraway (2005) Extending the Linear Model with R Friedman (1991) Multivariate Adaptive Regression Splines (with discussion) Annals of Statistics 19/1, 1--141 Friedman (1993) Fast MARS Stanford University Department of Statistics, Technical Report 110 Friedman and Silverman (1989) Flexible Parsimonious Smoothing and Additive Modeling Technometrics, Vol. 31, No. 1. Hastie, Tibshirani, and Friedman (2009) The Elements of Statistical Learning (2nd ed.) Leathwick, J.R., Rowe, D., Richardson, J., Elith, J., & Hastie, T. (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish Freshwater Biology, 50, 2034-2052, Miller, Alan (1990, 2nd ed. 2002) Subset Selection in Regression Wikipedia article on MARS

See Also

Start with,, evimp, and plotmo. Please see the main package vignette “Notes on the earth package”. The vignette can also be downloaded from The vignette “Variance models in earth” is also included with the package. It describes how to build variance models and generate prediction intervals for earth models.

  • earth
  • earth.default
  • earth.formula
earth.mod <- earth(Volume ~ ., data = trees)
summary(earth.mod, digits = 2, style = "pmax")
Documentation reproduced from package earth, version 4.4.7, License: GPL-3

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