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