Description Usage Arguments Value References Examples
Assumes exposure given covariates and outcome is a normal-errors linear regression. Exposure measurements can be assumed precise or subject to additive normal measurement error. Some replicates are required for identifiability. Parameters are estimated using maximum likelihood.
1 |
y |
Numeric vector of Y values. |
xtilde |
List of numeric vectors with Xtilde values. |
c |
Numeric matrix with C values (if any), with one row for each subject. Can be a vector if there is only 1 covariate. |
constant_or |
Logical value for whether to assume a constant odds ratio
for X, which means that sigsq_1 = sigsq_0. If |
merror |
Logical value for whether there is measurement error. |
... |
Additional arguments to pass to |
List containing:
Numeric vector of parameter estimates.
Variance-covariance matrix.
Returned nlminb
object from maximizing the
log-likelihood function.
Akaike information criterion (AIC).
If constant_or = NULL
, two such lists are returned (one under a
constant odds ratio assumption and one not), along with a likelihood ratio
test for H0: sigsq_1 = sigsq_0
, which is equivalent to
H0: odds ratio is constant
.
Lyles, R.H., Guo, Y., and Hill, A.N. (2009) "A fresh look at the discriminant function approach for estimating crude or adjusted odds ratios." Am. Stat 63(4): 320–327.
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | # Load data frame with (Y, X, Xtilde, C) values for 1,000 subjects and list
# of Xtilde values where 25 subjects have replicates. Xtilde values are
# affected by measurement error. True log-OR = 0.5, sigsq = 1, sigsq_m = 0.5.
data(dat_ndfa)
dat <- dat_ndfa$dat
reps <- dat_ndfa$reps
# Estimate log-OR for X and Y adjusted for C using true X values
# (unobservable truth).
fit.unobservable <- ndfa(
y = dat$y,
xtilde = dat$x,
c = dat$c,
merror = FALSE
)
fit.unobservable$estimates
# Estimate log-OR for X and Y adjusted for C using observed Xtilde values,
# ignoring measurement error.
fit.naive <- ndfa(
y = dat$y,
xtilde = dat$xtilde,
c = dat$c,
merror = FALSE
)
fit.naive$estimates
# Repeat, but accounting for measurement error.
fit.corrected <- ndfa(
y = dat$y,
xtilde = reps,
c = dat$c,
merror = TRUE
)
fit.corrected$estimates
# Same as previous, but allowing for non-constant odds ratio.
fit.nonconstant <- ndfa(
y = dat$y,
xtilde = reps,
c = dat$c,
constant_or = FALSE,
merror = TRUE
)
fit.nonconstant$estimates
# Perform likelihood ratio test for H0: odds ratio is constant.
lrt <- ndfa(
y = dat$y,
xtilde = reps,
c = dat$c,
constant_or = NULL,
merror = TRUE
)
|
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