library(shellpipes)
library(vareffects); varefftheme()
library(ggpubr)
library(ggplot2)
library(dplyr)
rpcall("mediate_bin_preds_adjust.Rout mediate_bin_preds_adjust.R mediate_model.rda")
loadEnvironments()
startGraphics()
## Not mediated
### No bias adjustment
pred_none_notmediated <- varpred(mod_notmediated_bin
, "x"
, isolate=TRUE
, bias.adjust="none"
, modelname="none"
)
### Delta-sigma
pred_delta_mod_notmediated <- varpred(mod_notmediated_bin
, "x"
, isolate=TRUE
, bias.adjust="delta"
, sigma="mod"
, modelname="delta-mod"
)
### Delta-lp
pred_delta_lp_notmediated <- varpred(mod_notmediated_bin
, "x"
, isolate=TRUE
, bias.adjust="delta"
, sigma="lp"
, modelname="delta-lp"
)
### mcculloch-sigma
pred_mc_mod_notmediated <- varpred(mod_notmediated_bin
, "x"
, isolate=TRUE
, bias.adjust="mcculloch"
, sigma="mod"
, modelname="mc-mod"
)
### mcculloch-sigma
pred_mc_lp_notmediated <- varpred(mod_notmediated_bin
, "x"
, isolate=TRUE
, bias.adjust="mcculloch"
, sigma="lp"
, modelname="mc-lp"
)
### population
pred_pop_notmediated <- varpred(mod_notmediated_bin
, "x"
, steps=50
, isolate=TRUE
, bias.adjust="population"
, modelname="pop"
)
## Bins
binned_df <- binfun(mod_notmediated_bin, focal="x", bins=50, groups=NULL)
### Combine all predictions
vlist <- list(pred_none_notmediated
# , pred_delta_mod_notmediated
# , pred_delta_lp_notmediated
# , pred_mc_mod_notmediated
# , pred_mc_lp_notmediated
, pred_pop_notmediated
)
pred_notmediated_plots <- (comparevarpred(vlist=vlist
, lnames=NULL
, plotit=TRUE
, addmarginals=FALSE
, ci=FALSE
)
# + geom_hline(yintercept=pred_prop_notmed, lty=2, col="black")
+ geom_point(data=binned_df, aes(x=x, y=zbin), colour="grey")
+ geom_hline(data=observed_df_med, aes(yintercept=zbin), lty=2, col="red")
+ geom_vline(data=observed_df_med, aes(xintercept=x), lty=2, col="black")
+ labs(y="Predictions", colour="Method", title="Not mediated")
+ theme(legend.position="right")
)
print(pred_notmediated_plots)
## Mediated
### No bias adjustment
pred_none_mediated <- varpred(mod_mediated_bin
, "x"
, isolate=TRUE
, bias.adjust="none"
, modelname="none"
)
### Delta-sigma
pred_delta_mod_mediated <- varpred(mod_mediated_bin
, "x"
, isolate=TRUE
, bias.adjust="delta"
, sigma="mod"
, modelname="delta-mod"
)
### Delta-lp
pred_delta_lp_mediated <- varpred(mod_mediated_bin
, "x"
, isolate=TRUE
, bias.adjust="delta"
, sigma="lp"
, modelname="delta-lp"
)
### mcculloch-sigma
pred_mc_mod_mediated <- varpred(mod_mediated_bin
, "x"
, isolate=TRUE
, bias.adjust="mcculloch"
, sigma="mod"
, modelname="mc-mod"
)
### mcculloch-sigma
pred_mc_lp_mediated <- varpred(mod_mediated_bin
, "x"
, isolate=TRUE
, bias.adjust="mcculloch"
, sigma="lp"
, modelname="mc-lp"
)
### population
pred_pop_mediated <- varpred(mod_mediated_bin
, "x"
, isolate=TRUE
, bias.adjust="population"
, modelname="pop"
)
## Bins
binned_df <- binfun(mod_mediated_bin, focal="x", bins=50, groups=NULL)
### Combine all predictions
vlist <- list(pred_none_mediated
# , pred_delta_mod_mediated
# , pred_delta_lp_mediated
# , pred_mc_mod_mediated
# , pred_mc_lp_mediated
, pred_pop_mediated
)
pred_mediated_plots <- (comparevarpred(vlist=vlist
, lnames=NULL
, plotit=TRUE
, addmarginals=FALSE
, ci=FALSE
)
# + geom_hline(yintercept=pred_prop_notmed, lty=2, col="black")
# + geom_point(data=binned_df, aes(x=x, y=zbin), colour="grey")
+ geom_hline(data=observed_df_med, aes(yintercept=zbin), lty=2, col="red")
+ geom_vline(data=observed_df_med, aes(xintercept=x), lty=2, col="black")
+ labs(y="Predictions", colour="Method", title="Mediated")
+ theme(legend.position="right")
)
print(pred_mediated_plots)
pred_mediate_plots <- ggarrange(pred_notmediated_plots
, pred_mediated_plots + rremove("ylab")
, common.legend=TRUE
, legend="bottom"
, ncol=2
)
print(pred_mediate_plots)
saveEnvironment()
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