earth.object: An earth object

In earth: Multivariate Adaptive Regression Splines

An earth object

Description

The object returned by the earth function.

This is an S3 model of class "earth". It is a list with the components listed below.

Term refers to a term created during the forward pass (each line of the output from format.earth is a term). Term number 1 is always the intercept.

Value

rss	Residual sum-of-squares (RSS) of the model (summed over all responses, if y has multiple columns).
rsq	1-rss/tss. R-Squared of the model (calculated over all responses, and calculated using the weights argument if it was supplied). A measure of how well the model fits the training data. Note that tss is the total sum-of-squares, sum((y - mean(y))^2).
gcv	Generalized Cross Validation (GCV) of the model (summed over all responses). The GCV is calculated using the penalty argument. For details of the GCV calculation, see equation 30 in Friedman's MARS paper and earth:::get.gcv.
grsq	1-gcv/gcv.null. An estimate of the predictive power of the model (calculated over all responses, and calculated using the weights argument if it was supplied). gcv.null is the GCV of an intercept-only model. See “Can GRSq be negative?” in the vignette.
bx	Matrix of basis functions applied to x. Each column corresponds to a selected term. Each row corresponds to a row in in the input matrix x, after taking subset. See model.matrix.earth for an example of bx handling. Example bx: (Intercept) h(Girth-12.9) h(12.9-Girth) h(Girth-12.9)*h(... [1,] 1 0.0 4.6 0 [2,] 1 0.0 4.3 0 [3,] 1 0.0 4.1 0 ...
dirs	Matrix with one row per MARS term, and with with ij-th element equal to 0 if predictor j is not in term i -1 if an expression of the form h(const - xj) is in term i 1 if an expression of the form h(xj - const) is in term i 2 if predictor j should enter term i linearly (either because specified by the linpreds argument or because earth discovered that a knot was unnecessary). This matrix includes all terms generated by the forward pass, including those not in selected.terms. Note that here the terms may not all be in pairs, because although the forward pass add terms as hinged pairs (so both sides of the hinge are available as building blocks for further terms), it also deletes linearly dependent terms before handing control to the pruning pass. Example dirs: Girth Height (Intercept) 0 0 # intercept h(12.9-Girth) -1 0 # 2nd term uses Girth h(Girth-12.9) 1 0 # 3rd term uses Girth h(Girth-12.9)*h(Height-76) 1 1 # 4th term uses Girth and Height ...
cuts	Matrix with ij-th element equal to the cut point (hinge value) for predictor j in term i. This matrix includes all terms generated by the forward pass, including those not in selected.terms. Note for programmers: the precedent is to use dirs for term names etc. and to only use cuts where cut information needed. Example cuts: Girth Height (Intercept) 0 0 # intercept, no cuts h(12.9-Girth) 12.9 0 # 2nd term has cut at 12.9 h(Girth-12.9) 12.9 0 # 3rd term has cut at 12.9 h(Girth-12.9)*h(Height-76) 12.9 76 # 4th term has two cuts ...
prune.terms	A matrix specifying which terms appear in which pruning pass subsets. The row index of prune.terms is the model size. (The model size is the number of terms in the model. The intercept is counted as a term.) Each row is a vector of term numbers for the best model of that size. An element is 0 if the term is not in the model, thus prune.terms is a lower triangular matrix, with dimensions nprune x nprune. The model selected by the pruning pass is at row number length(selected.terms). Example prune.terms: [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 1 0 0 0 0 0 0 # intercept-only model [2,] 1 2 0 0 0 0 0 # best 2 term model uses terms 1,2 [3,] 1 2 4 0 0 0 0 # best 3 term model uses terms 1,2,4 [4,] 1 2 6 9 0 0 0 # and so on ...
selected.terms	Vector of term numbers in the selected model. Can be used as a row index vector into cuts and dirs. The first element selected.terms[1] is always 1, the intercept.
fitted.values	Fitted values. A matrix with dimensions nrow(y) x ncol(y) after factors in y have been expanded.
residuals	Residuals. A matrix with dimensions nrow(y) x ncol(y) after factors in y have been expanded.
coefficients	Regression coefficients. A matrix with dimensions length(selected.terms) x ncol(y) after factors in y have been expanded. Each column holds the least squares coefficients from regressing that column of y on bx. The first row holds the intercept coefficient(s).
rss.per.response	A vector of the RSS for each response. Length is the number of responses, i.e., ncol(y) after factors in y have been expanded. The rss component above is equal to sum(rss.per.response).
rsq.per.response	A vector of the R-Squared for each response (where R-Squared is calculated using the weights argument if it was supplied). Length is the number of responses.
gcv.per.response	A vector of the GCV for each response. Length is the number of responses. The gcv component above is equal to sum(gcv.per.response).
grsq.per.response	A vector of the GRSq for each response (calculated using the weights argument if it was supplied). Length is the number of responses.
rss.per.subset	A vector of the RSS for each model subset generated by the pruning pass. Length is nprune. For multiple responses, the RSS is summed over all responses for each subset. The rss above is rss.per.subset[length(selected.terms)]. The RSS of an intercept only-model is rss.per.subset[1].
gcv.per.subset	A vector of the GCV for each model in prune.terms. Length is nprune. For multiple responses, the GCV is summed over all responses for each subset. The gcv above is gcv.per.subset[length(selected.terms)]. The GCV of an intercept-only model is gcv.per.subset[1].
leverages	Diagonal of the hat matrix (from the linear regression of the response on bx).
penalty,nk,thresh	Copies of the corresponding arguments to earth.
pmethod,nprune	Copies of the corresponding arguments to earth.
weights,wp	Copies of the corresponding arguments to earth.
termcond	Reason the forward pass terminated (an integer).
call	The call used to invoke earth.
terms	Model frame terms. This component exists only if the model was built using earth.formula.
modvars	A matrix specifying which input variables are used in each column of the model matrix. (This field is new in earth 5.2.0.) Columns correspond to columns of the model matrix (same as cols of dirs, see above). Rows correspond to variables in the formula. For example, the formula: survived ~ age + pclass + sqrt(age) - sex results in: attr(terms,"factors"): age pclass sqrt(age) survived 0 0 0 # the response will be dropped age 1 0 0 pclass 0 1 0 sqrt(age) 0 0 1 # sqrt(age) will be merged with age sex 0 0 0 # sex is unused and will be dropped modvars: age pclass2nd pclass3rd sqrt(age) age 1 0 0 1 # age and sqrt(age) use "age" pclass 0 1 1 0 # pclass2nd and pclass3rd use "pclass" Note that for models built with earth.default (x,y models), “derived variables” are not combined in modvars as they are for formula models. The row names of modvars match the column names of x, after factor expansion. Columns in x named "age" and "sqrt(age)" will be treated as two separate variables.
namesx	Variable names in the input data. Deprecated (subsumed by modvars).
xlevels	This component exists only if the model was built using earth.formula. Same as lm. A record of the levels of the factors used in fitting, needed under certain conditions by predict.earth.
levels	This component exists only if the model was built using earth.default. Levels of y if y is a factor, c(FALSE,TRUE) if y is logical, Else NULL. The following fields appear only if earth's argument keepxy is TRUE.
x,y,data,subset	Copies of the corresponding arguments to earth. Only exist if keepxy=TRUE. The following fields appear only if earth's glm argument is used.
glm.list	List of GLM models. Each element is the value returned by earth's internal call to glm for each response. Thus if there is a single response (or a single binomial pair, see “Binomial pairs” in the vignette) this will be a one element list and you access the GLM model with earth.mod$glm.list[[1]].
glm.coefficients	GLM regression coefficients. Analogous to the coefficients field described above but for the GLM model(s). A matrix with dimensions length(selected.terms) x ncol(y) after factors in y have been expanded. Each column holds the coefficients from the GLM regression of that column of y on bx. This duplicates, for convenience, information buried in glm.list.
glm.stats	GLM summary statistics such as devratio, AIC, and iters.
glm.bpairs	Is NULL unless there are paired binomial columns. Else a logical vector c(TRUE, FALSE). See “Binomial pairs” in the vignette. Retained for backwards compatibility with old versions of earth. The following fields appear only if the nfold argument is greater than 1.
cv.list	List of earth models, one model for each fold (ncross * nfold models). The fold models have two extra fields, icross (an integer from 1 to ncross) and ifold (an integer from 1 to nfold). To save memory, lengthy fields in the fold models are removed unless you use keepxy=TRUE. The “lengthy fields” are $bx, $fitted.values, and $residuals.
cv.nterms	Vector of length ncross * nfold + 1. Number of MARS terms in the model generated at each cross-validation fold, with the final element being the mean of these.
cv.nvars	Vector of length ncross * nfold + 1. Number of predictors in the model generated at each cross-validation fold, with the final element being the mean of these.
cv.groups	Specifies which cases went into which folds. Matrix with two columns and number of rows equal to the the number of cases nrow(x) Elements of the first column specify the cross-validation number, 1:ncross. Elements of the second column specify the fold number, 1:nfold.
cv.rsq.tab	Matrix with ncross * nfold + 1 rows and nresponse+1 columns, where nresponse is the number of responses, i.e., ncol(y) after factors in y have been expanded. The first nresponse elements of a row are the cv.rsq's on the out-of-fold data for each response of the model generated at that row's fold. (A cv.rsq is calculated from predictions on the out-of-fold data using the best model built from the in-fold data; where “best” means the model was selected using the in-fold GCV. The R-Squareds are calculated using the weights argument if it was supplied. The final column holds the row mean (a weighted mean if wp if specified)). The final row holds the column means. The values in this final row is the mean cv.rsq printed by summary.earth. Example for a single response model (where the mean column is redundant but included for uniformity with multiple response models): y mean fold1 0.909 0.909 fold2 0.869 0.869 fold3 0.952 0.952 fold4 0.157 0.157 fold5 0.961 0.961 mean 0.769 0.769 Example for a multiple response model: y1 y2 y3 mean fold1 0.915 0.951 0.944 0.937 fold2 0.962 0.970 0.970 0.968 fold3 0.914 0.940 0.942 0.932 fold4 0.907 0.929 0.925 0.920 fold5 0.947 0.987 0.979 0.971 mean 0.929 0.955 0.952 0.946
cv.class.rate.tab	Like cv.rsq.tab but is the classification rate at each fold i.e. the fraction of classes correctly predicted. Models with discrete response only. Calculated with thresh=.5 for binary responses. For responses with more than two levels, the final row is the overall classification rate. The other rows are the classification rates for each level (the level versus not-the-level), which are usually higher than the overall classification rate (predicting the level versus not-the-level is easier than correctly predicting one of many levels). The weights argument is ignored for all cross-validation stats except R-Squareds.
cv.maxerr.tab	Like cv.rsq.tab but is the MaxErr at each fold. This is the signed max absolute value at each fold. Results are aggregated for the final column and final row using the signed max absolute value. The signed max absolute value is defined as the maximum of the absolute difference between the predicted and observed response values, multiplied by -1 if the sign of that difference is negative.
cv.auc.tab	Like cv.rsq.tab but is the AUC at each fold. Binomial models only.
cv.cor.tab	Like cv.rsq.tab but is the cor at each fold. Poisson models only.
cv.deviance.tab	Like cv.rsq.tab but is the MeanDev at each fold. Binomial models only.
cv.calib.int.tab	Like cv.rsq.tab but is the CalibInt at each fold. Binomial models only.
cv.calib.slope.tab	Like cv.rsq.tab but is the CalibSlope at each fold. Binomial models only.
cv.oof.rsq.tab	Generated only if keepxy=TRUE or pmethod="cv". A matrix with ncross * nfold + 1 rows and max.nterms columns, Each element holds an out-of-fold RSq (oof.rsq), calculated from predictions from the out-of-fold observations using the model built with the in-fold data. The final row is the mean over all folds. The R-Squareds are calculated using the weights argument if it was supplied.
cv.infold.rsq.tab	Generated only if keepxy=TRUE. Like cv.oof.rsq.tab but from predictions made on the in-fold observations.
cv.oof.fit.tab	Generated only if the varmod.method argument is used. Predicted values on the out-of-fold data. Dataframe with nrow(data) rows and ncross columns. The following field appears only if the varmod.method is specified.
varmod	An object of class "varmod". See the varmod help page for a description. Only appears if the varmod.method argument is used.

See Also

earth

earth documentation built on Oct. 6, 2024, 1:08 a.m.