apc.frame, this
function draws a set of estimates from an APC-fit in the frame. An
optional drift parameter can be added to the period parameters and
subtracted from the cohort and age parameters.
"lines"( x, P, C, scale = c("log","ln","rates","inc","RR"), frame.par = options()[["apc.frame.par"]], drift = 0, c0 = median( C[,1] ), a0 = median( A[,1] ), p0 = c0 + a0, ci = rep( FALSE, 3 ), lwd = c(3,1,1), lty = 1, col = "black", type = "l", knots = FALSE, ... ) apc.lines( x, P, C, scale = c("log","ln","rates","inc","RR"), frame.par = options()[["apc.frame.par"]], drift = 0, c0 = median( C[,1] ), a0 = median( A[,1] ), p0 = c0 + a0, ci = rep( FALSE, 3 ), lwd = c(3,1,1), lty = 1, col = "black", type = "l", knots = FALSE, ... )apc-object, (see apc.fit), then
the arguments P, C, c0, a0 and p0
are ignored, and the estimates from x plotted. Can also be a 4-column matrix with columns age, age-specific
rates, lower and upper c.i., in which case period and cohort effects
are taken from the arguments P and C.
"log", "ln", "rates", "inc",
"RR". If "log" or "ln" it is assumed that
effects are log(rates) and log(RRs) otherwise the actual effects are
assumed given in A, P and C. If A is of
class apc, it is assumed to be "rates".apc.frame. See this for details.scale="log" this is assumed to be on the log-scale, otherwise
it is assumed to be a multiplicative factor per unit of the first
columns of A, P and C drift*(C[,1]-c0)."a" or "A"
produces confidence intervals for the age-effect. Similarly for
period and cohort.points
lines, matpoints or matlines used
for plotting the three sets of curves.APC.lines returns (invisibly) a list of three matrices of the
effects plotted.
apc.frame, pc.lines, apc.fit, apc.plot