View source: R/approxMargMLECI.r
approxMargMLECI | R Documentation |
Compute the profile MLE confidence interval of the ratio of two Poisson rates in a two-sample Poisson rate problem with misclassified data given fallible and infallible datasets. This uses a C++ implemention of the EM algorithm.
approxMargMLECI( data, N1, N2, N01, N02, conf.level = 0.95, l = 0.001, u = 1000, tol = 1e-10 )
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 |
conf.level |
confidence level of the interval |
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) |
tol |
tolerance used in the EM algorithm to declare convergence |
a named vector containing the marginal mle of phi
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.
# 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) waldCI(data, N1, N2, N01, N02) margMLECI(data, N1, N2, N01, N02) profMLECI(data, N1, N2, N01, N02) approxMargMLECI(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) waldCI(data, N1, N2, N01, N02) margMLECI(data, N1, N2, N01, N02) profMLECI(data, N1, N2, N01, N02) approxMargMLECI(data, N1, N2, N01, N02) ## End(Not run)
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