# approxMargMLE: Compute the marginal MLE of phi In poisDoubleSamp: Confidence Intervals with Poisson Double Sampling

## Description

Compute the marginal MLE of the ratio of two Poisson rates in a two-sample Poisson rate problem with misclassified data given fallible and infallible datasets.

## Usage

 ```1 2``` ```approxMargMLE(data, N1, N2, N01, N02, l = 0, u = 1000, out = c("par", "all"), tol = 1e-10) ```

## 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 `l` the lower end of the range of possible phi's (for optim) `u` the upper end of the range of possible phi's (for optim) `out` "par" or "all" (for the output of optim) `tol` tolerance parameter for the rmle EM algorithm

## Value

a named vector containing the marginal mle of phi

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```## Not run: # 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) margMLE(data, N1, N2, N01, N02) approxMargMLE(data, N1, N2, N01, N02) # 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) # margMLE(data, N1, N2, N01, N02) # ~1 min approxMargMLE(data, N1, N2, N01, N02) ## End(Not run) ```

poisDoubleSamp documentation built on May 29, 2017, 10:20 a.m.