In one of the papers... We developed core models with a generalized additive Poisson regression allowing for over-dispersion in the model (Wood, 2006). For each mortality outcome, variations in seasonality, trends, mean temperature, and mean humidity of current and previous days (lag 0–1) were fitted with penalized cubic regression splines. Dummy variables were used to control the variations for days of the week, holidays, and influenza epidemics. We added a dummy variable for the 2003 severe acute respiratory syndrome (SARS) epidemic. We chose 4 degrees of freedom (df) per year for smoothing function of the trends and 3 df for temperature and humidity. The choice of df for each smoothing function in the core models was made on the basis of observed residual autocorrelations using partial autocorrelation function (PACF). For the core models fitted to the mortality data, time variant confounding factors were considered as adequately controlled if absolute values of PACF coefficients were <0.1 for the first two lag days and there were no systematic patterns in the PACF plots.
Following the construction of an adequate core model for each mortality outcome, we entered visibility as a linear term into the regression model and examined the effects of visibility on mortality for single day lags 0–5 days, lag 0–1, and distributed lag 0–4 days ([Schwartz, 2000] and [Zanobetti et al., 2000]). The distributed lag effect take into account the possibility that visibility can affect deaths occurring on the same day and on several subsequent days. The net effect of visibility was the sum of the effect estimates for all six days. We expressed the effect of visibility as the percentage change in daily mortality with a decrease in the interquartile range (IQR) of visibility as 100%×IQR×β, where β is the estimated Poisson regression coefficient, and referred to as the excess risk (ER%). in one of the figures, they reported "Estimated excess risks (ER%) for daily mortality and associated 95% confidence intervals per interquartile range decrease in visibility (6.5 km) at single lags 0–5, mean lag 0–1 (0–1) and distributed lag (DL) for lag 0–4 days" What do they mean??! Thanks a lot! -- View this message in context: http://r.789695.n4.nabble.com/mgcv-beta-coefficient-and-95-CI-tp3320491p3321099.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
