Inverse standardisation

Transforms response variable back to original scale or transforms coefficients for predictor variables and response variable on original scales.

Usage

.backscale(pars, y = NULL, coef = NULL)

Arguments

pars

list with slots mu.x and sd.x (\(p\)-dimensional vectors of means and standard deviations of the predictor variables), mu.y and sd.y (mean and standard deviation of response variable for Gaussian family, 0 and 1 for other families), and family (character string "gaussian", "binomial", "poisson", or "cox")

y

\(n_1\)-dimensional response vector or response matrix with \(n_1\) rows and multiple columns (for multiple values of the regularisation parameter), or NULL (default)

coef

\((1 + p)\)-dimensional vector containing the estimated intercept and the estimated slopes, or NULL (default)

Value

Returns a list with slots y or coef.

Details

This function is called by predict.cv.corila() for the predicted values and by coef.cv.corila() for the estimated coefficients.

See also

Use function .forescale() to standardise variables.

Examples

# \donttest{

# simulate data
family <- "gaussian"
data <- simulate_data(family = family, prob_primary = 1.0)

# regression without standardisation
if (identical(family, "cox")) {
  lm1 <- survival::coxph(data$y_train~., data = data.frame(data$x_train))
} else {
  lm1 <- stats::glm(data$y_train~., data = data.frame(data$x_train),
                    family = family)
}
coef1 <- stats::coef(lm1)
yhat1 <- predict(lm1, newdata = data.frame(data$x_test))

# regression with standardisation
scale <- .forescale(x = data$x_train,
                    y = data$y_train,
                    family = family)
if (identical(family, "cox")) {
  lm2 <- survival::coxph(scale$y~., data = data.frame(scale$x))
} else {
  lm2 <- stats::glm(scale$y~., data = data.frame(scale$x), family = family)
}
coef_temp <- stats::coef(lm2)
newx_temp <- .forescale(x = data$x_test,
                        pars = scale$pars)$x
yhat_temp <- predict(object = lm2, newdata = data.frame(newx_temp))
result <- .backscale(pars = scale$pars,
                     y = yhat_temp,
                     coef = coef_temp)
coef2 <- result$coef
yhat2 <- result$y

# equality
all.equal(coef1, coef2)
#> [1] TRUE
all.equal(yhat1, yhat2)
#> [1] TRUE
# }