aapc | R Documentation |
Computes the average annual per cent change to summarize piecewise linear relationships in segmented regression models.
aapc(ogg, parm, exp.it = FALSE, conf.level = 0.95, wrong.se = TRUE,
.vcov=NULL, .coef=NULL, ...)
ogg |
the fitted model returned by |
parm |
the single segmented variable of interest. It can be missing if the model includes a single segmented covariate. If missing and |
exp.it |
logical. If |
conf.level |
the confidence level desidered. |
wrong.se |
logical, if |
.vcov |
The full covariance matrix of estimates. If unspecified (i.e. |
.coef |
The regression parameter estimates. If unspecified (i.e. |
... |
further arguments to be passed on to |
To summarize the fitted piecewise linear relationship, Clegg et al. (2009) proposed the 'average annual per cent change' (AAPC)
computed as the sum of the slopes (\beta_j
) weighted by corresponding covariate sub-interval width (w_j
), namely
\mu=\sum_j \beta_jw_j
. Since the weights are the breakpoint differences, the standard error of the AAPC should account
for uncertainty in the breakpoint estimate, as discussed in Muggeo (2010) and implemented by aapc()
.
aapc
returns a numeric vector including point estimate, standard error and confidence interval for the AAPC relevant to variable specified in parm
.
exp.it=TRUE
would be appropriate only if the response variable is the log of (any) counts.
Vito M. R. Muggeo, vito.muggeo@unipa.it
Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK (2009) Estimating average annual per cent change in trend analysis. Statistics in Medicine, 28; 3670-3682.
Muggeo, V.M.R. (2010) Comment on ‘Estimating average annual per cent change in trend analysis’ by Clegg et al., Statistics in Medicine; 28, 3670-3682. Statistics in Medicine, 29, 1958–1960.
set.seed(12)
x<-1:20
y<-2-.5*x+.7*pmax(x-9,0)-.8*pmax(x-15,0)+rnorm(20)*.3
o<-lm(y~x)
os<-segmented(o, psi=c(5,12))
aapc(os)
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