appe.lm: L_1 and L_2 errors adjusted for predictor distributions

In APPEstimation: Adjusted Prediction Model Performance Estimation

Description

Calculates adjusted L_1 and L_2 errors by predictor distributions for a linear model.

Usage

appe.lm(mdl, dat.train, dat.test, method = "uLSIF", sigma = NULL,
        lambda = NULL, kernel_num = NULL, fold = 5, stabilize = TRUE,
        qstb = 0.025, reps = 2000, conf.level = 0.95)

Arguments

mdl	a lm object describing a prediction model to be evaluated.
dat.train	same as in appe.glm.
dat.test	same as in appe.glm.
method	same as in appe.glm.
sigma	same as in appe.glm.
lambda	same as in appe.glm.
kernel_num	same as in appe.glm.
fold	same as in appe.glm.
stabilize	same as in appe.glm.
qstb	same as in appe.glm.
reps	same as in appe.glm.
conf.level	same as in appe.glm.

Value

Adjusted and non-adjusted estimates of L_1 and L_2 errors are provided as matrix form. "L1" and "L2" indicate non-adjusted versions, "L1 adjusted by score" and "L2 adjusted by score" indicate adjusted versions by linear predictors distribution, "L1 adjusted by predictors" and "L2 adjusted by predictors" indicate adjusted versions by predictor distributions (multi-dimensionally). For confidence intervals, "Percentile" indicates a confidence interval by percentile method and "Approx" indicates approximated versions by Normal distribution.

Examples

set.seed(100)

# generating development data
n0  = 100
Z   = cbind(rbeta(n0, 3, 3), rbeta(n0, 3, 3))
Y   = apply(Z, 1, function(xx) { rlnorm(1, sum(c(1, 1) * xx), 0.3) })
dat = data.frame(Za=Z[,1], Zb=Z[,2], Y=Y)

# the model to be evaluated
mdl = lm(Y~ Za + Zb, data=dat)

# generating validation dataset 
n1   = 100
Z1   = cbind(rbeta(n0, 3.5, 2.5), rbeta(n0, 3.5, 2.5))
Y1   = apply(Z1, 1, function(xx) { rlnorm(1, sum(c(1, 1) * xx), 0.3) })
dat1 = data.frame(Za=Z1[,1], Zb=Z1[,2], Y=Y1)

# calculation of L1 and L2 for this model
appe.lm(mdl, dat, dat1, reps=0)

APPEstimation documentation built on May 2, 2019, 6:34 a.m.