stepXR {XReg} | R Documentation |
stepXR
This functions calls builds extreme regression models in a step-wise fashion
stepXR(y, x, ypred = y, xpred = x, kstep = 5, varlist = NULL, outtemp = NULL, niter = 5, step.size = 0.2, niterfull = 20, step.sizefull = 0.2, kbest = 1, limsize = 0.05, step.adapt = F, upper = max(ypred), lower = min(ypred), predict = T, randinit=0,penalty = 2, update.all = T,survival=F,times=y,status=y)
y |
response variable |
x |
matrix of predictors |
xpred |
xmatrix for predictions |
ypred |
y values on test data set |
varlist |
is a list specifying the form of the extreme regression model. EXAMPLE: varlist=list(1, c(2,3),c(3,4)) –> max (a1+b1x1,min(a2+b2x2,a3+b3x3),min(a4+b4x3,a5+b5x4)) |
lower |
minimum prediction |
outtemp |
current output model |
step.adapt |
If T the step size is adapted so that the sum of squares is reduced at each step. This slows down the algorithm. |
predict |
make predictions on the xpred values |
update.all |
update all coefficients as terms are added |
upper |
maximum prediction EXAMPLE lower=min(y) and upper=max(y) predictions will always be in the range of y. |
limsize |
the targeted smallest number of observations used to estimate a univariate function. |
niter |
number of estimations steps |
step.adapt |
If T the step size is adapted so that the sum of squares is reduced at each step. This slows down the algorithm. |
survival |
If yes, the expoential survival model is used. |
times |
If survival=T, time under observation |
status |
If survival=T, survival status (1=dead, 0=alive) |
randinit |
If randinit>0 some noise is added to the initial estimates. Randinit adds noise of standard deviation form randinit/sqrt(n). This avoids bad initial starts when the same variable is involved in multiple terms. |
kstep |
the number of linear components to add |
penalty |
penalty used in model selection |
step.size |
step.size for each term considered for addition |
niterfull |
number of steps after term has been selected |
step.sizefull |
as above |
kbest |
used in addeval function and is the number of variables considered for selection. It is the kbest variables with highest univariate correlation. |
a list containing:
stepcoef |
list of coefficients for the stepwise process |
steplist |
list of variables for the stepwise process |
stepfit |
fit for training data for each model |
stepfitpred |
fit for test data (if given) for each model |
bestgcv |
best model by gcv |
Michael LeBlanc mleblanc@fhcrc.org
## # See XReg