get_mu_vec {linelistBayes}

Calculate Exponential of Linear Combinations

Description

Computes the exponential of linear combinations of beta coefficients and a matrix of predictors, typically used in Poisson or logistic regression models for estimating rates or probabilities. This function specifically handles the exponential transformation, which is commonly used to ensure that rates or probabilities are non-negative.

Usage

get_mu_vec(x12, beta)

Arguments

x12

NumericMatrix representing a matrix of predictors, where each row corresponds to an observation and columns correspond to different predictor variables (e.g., weeks and weekends).

beta

NumericVector of coefficients corresponding to the predictors in 'x12'. This should include coefficients for both weekly effects and potentially an additional coefficient for weekends.

Details

The function multiplies the matrix 'x12' by the vector 'beta' to get the linear predictors, then applies the exponential function to convert these linear predictors to a scale suitable for models where the response variable is a count or probability. This is a critical step in generalized linear models where the link function is the natural logarithm.

Value

NumericVector where each element is the exponential of the linear combination of the predictors and coefficients for a given observation. This vector represents the model-estimated mean values for each observation.

Examples

# Assuming x12 is a matrix with 10 observations and 3 predictors
# and beta is a vector of 3 coefficients
x12 <- matrix(rnorm(30), ncol=3)
beta <- c(0.1, -0.2, 0.05)
get_mu_vec(x12, beta)