fullMLE: Compute the full MLEs

In poisDoubleSamp: Confidence Intervals with Poisson Double Sampling

Compute the full MLEs

Description

Compute the MLEs of a two-sample Poisson rate problem with misclassified data given fallible and infallible datasets.

Usage

fullMLE(data, N1, N2, N01, N02)

Arguments

data	the vector of counts of the fallible data (z11, z12, z21, z22) followed by the infallible data (m011, m012, m021, m022, y01, y02)
N1	the opportunity size of group 1 for the fallible data
N2	the opportunity size of group 2 for the fallible data
N01	the opportunity size of group 1 for the infallible data
N02	the opportunity size of group 2 for the infallible data

Details

These are the closed-form expressions for the MLEs.

Value

a named vector containing the mles of each of the parameters (phi, la12, la21, la22, th1, and th2)

References

Kahle, D., P. Young, B. Greer, and D. Young (2016). "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way Differential Misclassification Using Double Sampling." Computational Statistics & Data Analysis, 95:122–132.

Examples

# small example
z11 <- 34; z12 <- 35; N1 <- 10;
z21 <- 22; z22 <- 31; N2 <- 10;
m011 <- 9; m012 <- 1; y01 <- 3; N01 <- 3;
m021 <- 8; m022 <- 8; y02 <- 2; N02 <- 3;
data <- c(z11, z12, z21, z22, m011, m012, m021, m022, y01, y02)

fullMLE(data, N1, N2, N01, N02)


## Not run: 

# big example :
z11 <- 477; z12 <- 1025; N1 <- 16186;
z21 <- 255; z22 <- 1450; N2 <- 18811;
m011 <- 38;  m012 <- 90; y01 <- 15; N01 <- 1500;
m021 <- 41; m022 <- 200; y02 <-  9; N02 <- 2500;
data <- c(z11, z12, z21, z22, m011, m012, m021, m022, y01, y02)

fullMLE(data, N1, N2, N01, N02)



## End(Not run)

poisDoubleSamp documentation built on May 10, 2022, 5:11 p.m.