predict.miss.glm: Prediction on test with missing values for the logistic...

In misaem: Linear Regression and Logistic Regression with Missing Covariates

Prediction on test with missing values for the logistic regression model.

Description

Prediction on test with missing values for the logistic regression model.

Usage

## S3 method for class 'miss.glm'
predict(object, newdata = NULL, seed = NA, method = "map", mc.size = 100, ...)

Arguments

object	a fitted object of class inheriting from "miss.glm".
newdata	a data frame in which to look for variables with which to predict. It can contain missing values.
seed	An integer as a seed set for the random generator.
method	The name of method to deal with missing values in test set. It can be 'map'(maximum a posteriori) or 'impute' (imputation by conditional expectation). Default is 'map'.
mc.size	The number of Monte Carlo samples to use when method is 'map'. Default is 100.
...	Further arguments passed to or from other methods.

Value

pr.saem

The prediction result for logistic regression: the probability of response y=1.

Examples

# Generate dataset
N <- 100  # number of subjects
p <- 3     # number of explanatory variables
mu.star <- rep(0,p)  # mean of the explanatory variables
Sigma.star <- diag(rep(1,p)) # covariance
beta.star <- c(1, 1,  0) # coefficients
beta0.star <- 0 # intercept
beta.true = c(beta0.star,beta.star)
X.complete <- matrix(rnorm(N*p), nrow=N)%*%chol(Sigma.star) +
              matrix(rep(mu.star,N), nrow=N, byrow = TRUE)
p1 <- 1/(1+exp(-X.complete%*%beta.star-beta0.star))
y <- as.numeric(runif(N)<p1)

# Generate missingness
p.miss <- 0.10
patterns <- runif(N*p)<p.miss #missing completely at random
X.obs <- X.complete
X.obs[patterns] <- NA

df.obs = data.frame(y,X.obs)

# SAEM
miss.list = miss.glm(y~., data=df.obs, print_iter=FALSE,seed=100)

# Generate new dataset for prediction
Nt <- 20
Xt <- matrix(rnorm(Nt*p), nrow=Nt)%*%chol(Sigma.star)+
  matrix(rep(mu.star,Nt), nrow=Nt, byrow = TRUE)
# Generate missingness in new dataset
patterns <- runif(Nt*p)<p.miss
Xt.obs <- Xt
Xt.obs[patterns] <- NA

# Prediction with missing values
miss.prob = predict(miss.list, data.frame(Xt.obs), method='map')
print(miss.prob)

misaem documentation built on Sept. 12, 2025, 1:08 a.m.