predict.misrepEM {glmMisrep}

Predict method for 'misrepEM' Model Fits

Description

Predicted values based on a fitted 'misrepEM' model object.

Usage

## S3 method for class 'misrepEM'
predict(object, newdata, ...)

Arguments

object

a fit from one of gammaRegMisrepEM, LnRegMisrepEM, NormRegMisrepEM, nbRegMisrepEM, or poisRegMisrepEM.

newdata

a data frame containing predictors that are to be used to make predictions of the response.

...

currently not used.

Details

Currently, only predictions made on the scale of the response variable are supported.

Incomplete cases are automatically dropped, and predictions are made only on complete cases.

Value

predict.misrepEM returns a numeric vector of predictions.

References

Xia, Michelle, Rexford Akakpo, and Matthew Albaugh. "Maximum Likelihood Approaches to Misrepresentation Models in GLM ratemaking: Model Comparisons." Variance 16.1 (2023).

Akakpo, R. M., Xia, M., & Polansky, A. M. (2019). Frequentist inference in insurance ratemaking models adjusting for misrepresentation. ASTIN Bulletin: The Journal of the IAA, 49(1), 117-146.

Xia, M., Hua, L., & Vadnais, G. (2018). Embedded predictive analysis of misrepresentation risk in GLM ratemaking models. Variance, 12(1), 39-58.

Examples

# Simulate data
n <- 2000
p0 <- 0.25

X1 <- rbinom(n, 1, 0.4)
X2 <- rnorm(n, 0, 1)
X3 <- rbeta(n, 2, 1)

theta0 <- 0.3
V <- rbinom(n,1,theta0)
V_star <- V
V_star[V==1] <- rbinom(sum(V==1),1,1-p0)

a0 <- 1
a1 <- 2
a2 <- 0
a3 <- 4
a4 <- 2

mu <- exp(a0 + a1*X1 + a2*X2 + a3*X3 + a4*V)

phi <- 0.2
alpha0 <- 1/phi
beta <- 1/mu/phi
Y <- rgamma(n, alpha0, beta)

data <- data.frame(Y = Y, X1 = X1, X2 = X2, X3 = X3, V_star = V_star)

# Split data into training and testing sets
train <- data[1:1800,]
test  <- data[1801:2000,]

gamma_fit <- gammaRegMisrepEM(formula = Y ~ X1 + X2 + X3 + V_star,
                              v_star = "V_star", data = train)

# Predict on test set;
preds <- predict(gamma_fit, newdata = test)