Plotting function to assess the mean of generated data

Usage

plotMean(model, simdat, bins = 15, quants = c(0.025, 0.975))

Arguments

model: model object of class glm, gam or gamMRSea
simdat: matrix of simulated response data. Each column is a new simulated data set
bins: number of bins to be plotted on the histogram
quants: vector of length 2 stating the quantiles for the confidence interval bands for the simulated data

Details

The histogram returned shows the lower 2.5 and upper 97.5 quantiles (dark grey, long dash lines) for the mean. The grey solid line is the mean of the fitted values from the inputed model. The black short dashed line is the mean of the response data.

Author

Lindesay Scott-Hayward (University of St Andrews)

Examples

data(nystedA_slim)

initialModel<-MRSea::gamMRSea(response ~ 1 + as.factor(yearmonth)+depth +
                     x.pos + y.pos + offset(log(area)),  data=nysted,
                     family=quasipoisson)

nsim<-550
d<-as.numeric(summary(initialModel)$dispersion)
newdat<-generateNoise(nsim, fitted(initialModel), family='poisson', d=d)

plotMean(initialModel, newdat)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).