ppr {stats} | R Documentation

## Projection Pursuit Regression

### Description

Fit a projection pursuit regression model.

### Usage

    ppr(x, ...)

    ## S3 method for class 'formula'
    ppr(formula, data, weights, subset, na.action,
        contrasts = NULL, ..., model = FALSE)

    ## Default S3 method:
    ppr(x, y, weights = rep(1, n),
        ww = rep(1, q), nterms, max.terms = nterms, optlevel = 2,
        sm.method = c("supsmu", "spline", "gcvspline"),
        bass = 0, span = 0, df = 5, gcvpen = 1, trace = FALSE, ...)

### Arguments

`formula` | a formula specifying one or more numeric response variables and the explanatory variables.
`x` | numeric matrix of explanatory variables. Rows represent observations, and columns represent variables. Missing values are not accepted.
`y` | numeric matrix of response variables. Rows represent observations, and columns represent variables. Missing values are not accepted.
`nterms` | number of terms to include in the final model.
`data` | a data frame (or similar: see `model.frame`) from which variables specified in `formula` are preferentially to be taken.
`weights` | a vector of weights `w_i` for each case.
`ww` | a vector of weights for each response, so the fit criterion is the sum over case `i` and responses `j` of `w_i ww_j (y_ij - fit_ij)^2` divided by the sum of `w_i`.
`subset` | an index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
`na.action` | a function to specify the action to be taken if `NA`s are found. The default action is given by `getOption("na.action")`. (NOTE: If given, this argument must be named.)
`contrasts` | the contrasts to be used when any factor explanatory variables are coded.
`max.terms` | maximum number of terms to choose from when building the model.
`optlevel` | integer from 0 to 3 which determines the thoroughness of an optimization routine in the SMART program. See the ‘Details’ section.
`sm.method` | the method used for smoothing the ridge functions. The default is to use Friedman's super smoother `supsmu`. The alternatives are to use the smoothing spline code underlying `smooth.spline`, either with a specified (equivalent) degrees of freedom for each ridge functions, or to allow the smoothness to be chosen by GCV. Can be abbreviated.
`bass` | super smoother bass tone control used with automatic span selection (see `supsmu`); the range of values is 0 to 10, with larger values resulting in increased smoothing.
`span` | super smoother span control (see `supsmu`). The default, `0`, results in automatic span selection by local cross validation. `span` can also take a value in `(0, 1]`.
`df` | if `sm.method` is `"spline"` specifies the smoothness of each ridge term via the requested equivalent degrees of freedom.
`gcvpen` | if `sm.method` is `"gcvspline"` this is the penalty used in the GCV selection for each degree of freedom used.
`trace` | logical indicating if each spline fit should produce diagnostic output (about `lambda` and `df`), and the supsmu fit about its steps.
`...` | arguments to be passed to or from other methods.
`model` | logical. If true, the model frame is returned.

### Details

The basic method is given by Friedman (1984), and is essentially the same code used by S-PLUS's `ppreg`. This code is extremely sensitive to the compiler used.

The algorithm first adds up to `max.terms` ridge terms one at a time; it will use less if it is unable to find a term to add that makes sufficient difference. It then removes the least important term at each step until `nterms` terms are left.

The levels of optimization (argument `optlevel`) differ in how thoroughly the models are refitted during this process. At level 0 the existing ridge terms are not refitted. At level 1 the projection directions are not refitted, but the ridge functions and the regression coefficients are. Levels 2 and 3 refit all the terms and are equivalent for one response; level 3 is more careful to re-balance the contributions from each regressor at each step and so is a little less likely to converge to a saddle point of the sum of squares criterion.

### Value

A list with the following components, many of which are for use by the method functions.

`call` | the matched call
`p` | the number of explanatory variables (after any coding)
`q` | the number of response variables
`mu` | the argument `nterms`
`ml` | the argument `max.terms`
`gof` | the overall residual (weighted) sum of squares for the selected model
`gofn` | the overall residual (weighted) sum of squares against the number of terms, up to `max.terms`. Will be invalid (and zero) for less than `nterms`.
`df` | the argument `df`
`edf` | if `sm.method` is `"spline"` or `"gcvspline"` the equivalent number of degrees of freedom for each ridge term used.
`xnames` | the names of the explanatory variables
`ynames` | the names of the response variables
`alpha` | a matrix of the projection directions, with a column for each ridge term
`beta` | a matrix of the coefficients applied for each response to the ridge terms: the rows are the responses and the columns the ridge terms
`yb` | the weighted means of each response
`ys` | the overall scale factor used: internally the responses are divided by `ys` to have unit total weighted sum of squares.
`fitted.values` | the fitted values, as a matrix if `q > 1`.
`residuals` | the residuals, as a matrix if `q > 1`.
`smod` | internal work array, which includes the ridge functions evaluated at the training set points.
`model` | (only if `model = TRUE`) the model frame.

### Source

Friedman (1984): converted to double precision and added interface to smoothing splines by B. D. Ripley, originally for the MASS package.

### References

Friedman, J. H. and Stuetzle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817–823. doi: 10.2307/2287576.

Friedman, J. H. (1984). SMART User's Guide. Laboratory for Computational Statistics, Stanford University Technical Report No. 1.

Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Springer.

### See Also

`plot.ppr`, `supsmu`, `smooth.spline`

### Examples

    require(graphics)

    # Note: your numerical values may differ
    attach(rock)
    area1 <- area/10000; peri1 <- peri/10000
    rock.ppr <- ppr(log(perm) ~ area1 + peri1 + shape,
                    data = rock, nterms = 2, max.terms = 5)
    rock.ppr
    # Call:
    # ppr.formula(formula = log(perm) ~ area1 + peri1 + shape, data = rock,
    #     nterms = 2, max.terms = 5)
    #
    # Goodness of fit:
    #  2 terms  3 terms  4 terms  5 terms
    # 8.737806 5.289517 4.745799 4.490378

    summary(rock.ppr)
    # .....  (same as above)
    # .....
    #
    # Projection direction vectors ('alpha'):
    #       term 1      term 2
    # area1  0.34357179  0.37071027
    # peri1 -0.93781471 -0.61923542
    # shape  0.04961846  0.69218595
    #
    # Coefficients of ridge terms:
    #    term 1    term 2
    # 1.6079271 0.5460971

    par(mfrow = c(3,2))   # maybe: , pty = "s")
    plot(rock.ppr, main = "ppr(log(perm)~ ., nterms=2, max.terms=5)")
    plot(update(rock.ppr, bass = 5), main = "update(..., bass = 5)")
    plot(update(rock.ppr, sm.method = "gcv", gcvpen = 2),
         main = "update(..., sm.method=\"gcv\", gcvpen=2)")
    cbind(perm = rock$perm, prediction = round(exp(predict(rock.ppr)), 1))
    detach()

[Package stats version 3.6.0 Index]