## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = 'center', prompt = TRUE ) ## ----message=FALSE------------------------------------------------------------ library(bdrc) set.seed(1) #set seed for reproducibility ## ---- data-------------------------------------------------------------------- data(krokfors) krokfors ## ---- cache=TRUE-------------------------------------------------------------- gplm.fit <- gplm(Q~W,data=krokfors,parallel=TRUE,num_cores=2) # parallel=TRUE by default and by default, the number of cores is detected on the machine ## ----------------------------------------------------------------------------- summary(gplm.fit) ## ---- fig.width=8, fig.height=6----------------------------------------------- plot(gplm.fit) ## ---- fig.width=8, fig.height=6----------------------------------------------- plot(gplm.fit,type='histogram',param='c') ## ---- fig.width=8, fig.height=6----------------------------------------------- plot(gplm.fit,type='histogram',param='c',transformed=TRUE) ## ---- fig.width=8, fig.height=3----------------------------------------------- plot(gplm.fit,type='histogram',param=c('a','c')) ## ---- fig.width=10, fig.height=6---------------------------------------------- plot(gplm.fit,type='histogram',param='hyperparameters') ## ---- fig.width=10, fig.height=6---------------------------------------------- plot(gplm.fit,type='histogram',param='hyperparameters',transformed=TRUE) ## ---- fig.width=8, fig.height=6----------------------------------------------- plot(gplm.fit,type='f') ## ---- fig.width=8, fig.height=6----------------------------------------------- plot(gplm.fit,type='sigma_eps') ## ---- fig.width=8, fig.height=6,results='hide'-------------------------------- plot(gplm.fit,type='panel',transformed=TRUE) ## ---- fig.width=8, fig.height=6----------------------------------------------- plot(gplm.fit,type='residuals') ## ---- fig.width=10, fig.height=6---------------------------------------------- plot(gplm.fit,type='trace',param='c',transformed=TRUE) ## ---- fig.width=10, fig.height=6---------------------------------------------- plot(gplm.fit,type='trace',param='hyperparameters',transformed=TRUE) ## ----fig.width=8, fig.height=6------------------------------------------------ plot(gplm.fit,type='r_hat') ## ----fig.width=8, fig.height=6------------------------------------------------ plot(gplm.fit,type='autocorrelation') ## ----cache=TRUE,eval=FALSE---------------------------------------------------- # gplm.fit.known_c <- gplm(Q~W,krokfors,c_param=7.65,h_max=10,parallel=FALSE) ## ----------------------------------------------------------------------------- h_grid <- seq(8,9,by=0.01) rating_curve_h_grid <- predict(gplm.fit,newdata=h_grid) print(rating_curve_h_grid)