# earth.object

0th

Percentile

##### An earth object

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 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,]    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.

namesx

Column names of x, generated internally by earth when necessary so each column of x has a name. Used, for example, by predict.earth to name columns if necessary.

namesx.org

Original column names of x.

levels

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.

% \item{\code{x}}{} % \item{\code{y}}{} % \item{\code{data}}{} % \item{\code{subset}}{}{
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.bpairs NULL unless there are paired binomial columns. A logical vector, derived internally by earth, or a copy the bpairs specified by the user in the glm list. See “Binomial pairs” in the vignette. 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.

earth