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Optional parameters for growth rate methods

Source: R/growth_rate.R
growth_rate_params.Rd

Construct an object containing non-standard arguments for growth_rate().

Usage

growth_rate_params(
  df = NULL,
  lambda = NULL,
  cv = FALSE,
  spar = NULL,
  all.knots = FALSE,
  df.offset = 0,
  penalty = 1,
  k = 3L,
  family = c("gaussian", "logistic", "poisson"),
  nlambda = 50L,
  lambda_max = NULL,
  lambda_min = NULL,
  lambda_min_ratio = 1e-05,
  error_measure = c("deviance", "mse", "mae"),
  nfolds = 3L
)

Arguments

df

Numeric or NULL for "smooth_spline". May also be one of "min" or "max" in the case of "trend_filter". The desired equivalent number of degrees of freedom of the fit. Lower values give smoother estimates.

lambda

The desired smoothing parameter. For "smooth_spline", this can be specified instead of spar. For "trend_filter", this sequence determines the balance between data fidelity and smoothness of the estimated curve; larger lambda results in a smoother estimate. The default, NULL results in an automatic computation based on nlambda, the largest value of lambda that would result in a maximally smooth estimate, and lambda_min_ratio. Supplying a value of lambda overrides this behaviour.

cv

For "smooth_spline", ordinary leave-one-out (TRUE) or ‘generalized’ cross-validation (GCV) when FALSE; is used for smoothing parameter computation only when both spar and df are not specified. For "trend_filter", cv determines whether or not cross-validation is used to choose the tuning parameter. If FALSE, then the user must specify either lambda or df.

spar

smoothing parameter, typically (but not necessarily) in \((0,1]\). When spar is specified, the coefficient \(\lambda\) of the integral of the squared second derivative in the fit (penalized log likelihood) criterion is a monotone function of spar, see the details below. Alternatively lambda may be specified instead of the scale free spar=\(s\).

all.knots

if TRUE, all distinct points in x are used as knots. If FALSE (default), a subset of x[] is used, specifically x[j] where the nknots indices are evenly spaced in 1:n, see also the next argument nknots.

Alternatively, a strictly increasing numeric vector specifying “all the knots” to be used; must be rescaled to \([0, 1]\) already such that it corresponds to the ans $ fit$knots sequence returned, not repeating the boundary knots.

df.offset

allows the degrees of freedom to be increased by df.offset in the GCV criterion.

penalty

the coefficient of the penalty for degrees of freedom in the GCV criterion.

k

Integer. Degree of the piecewise polynomial curve to be estimated. For example, k = 0 corresponds to a piecewise constant curve.

family

Character or function. Specifies the loss function to use. Valid options are:

  • "gaussian" - least squares loss (the default),

  • "binomial" - logistic loss (classification),

  • "poisson" - Poisson loss for count data

For any other type, a valid stats::family() object may be passed. Note that these will generally be much slower to estimate than the built-in options passed as strings. So for example, family = "gaussian" and family = gaussian() will produce the same results, but the first will be much faster.character.

nlambda

Integer. Number of lambda values to use in the sequence.

lambda_max

Optional value for the largest lambda to use.

lambda_min

Optional value for the smallest lambda to use (> 0).

lambda_min_ratio

If neither lambda nor lambda_min is specified, lambda_min = lambda_max * lambda_min_ratio. A very small value will lead to the solution theta = y (for the Gaussian loss). This argument has no effect if there is a user-defined lambda sequence.

error_measure

Metric used to calculate cross validation scores. May be mse, mae, or deviance.

nfolds

Integer. The number of folds to use. For leave-vth-out cross validation, every vth y value and its corresponding position (and weight) are placed into the same fold. The first and last observations are not assigned to any folds. This value must be at least 2. As an example, with 15 data points and nfolds = 4, the points are assigned to folds in the following way: $$ 0 \; 1 \; 2 \; 3 \; 4 \; 1 \; 2 \; 3 \; 4 \; 1 \; 2 \; 3 \; 4 \; 1 \; 0 $$ where 0 indicates no assignment. Therefore, the folds are not random and running cv_trendfilter() twice will give the same result.

Value

A list of parameter configurations.