diff --git a/examples/howto_examples/gam-tract-profiles-example.Rmd b/examples/howto_examples/gam-tract-profiles-example.Rmd
index 31ca1e485..25db99aac 100644
--- a/examples/howto_examples/gam-tract-profiles-example.Rmd
+++ b/examples/howto_examples/gam-tract-profiles-example.Rmd
@@ -41,9 +41,9 @@ ggplot(aes(x=nodeID,y=est, color=class)) +
```{r, message=FALSE, warning=FALSE}
# The above plot looks good but now let's let the curves vary across # subjects too. What I notice here is that it dramatically effects the standard error
-g1 <- gam(md ~ s(nodeID,class, bs='fs') + s(nodeID, subjectID, bs= 're'), data=df)
+g2 <- gam(md ~ s(nodeID,class, bs='fs') + s(nodeID, subjectID, bs= 're'), data=df)
# Get smooth estimates
-dfs <- smooth_estimates(g1) %>% add_confint(coverage=.68)
+dfs <- smooth_estimates(g2) %>% add_confint(coverage=.68)
# Remove random effects and plot
filter(dfs, type != 'Random effect') %>%
@@ -54,9 +54,9 @@ ggplot(aes(x=nodeID,y=est, color=class)) +
```{r, message=FALSE, warning=FALSE}
# Yet another way to do the same thing and I don't get the difference
-g1 <- gam(md ~ s(nodeID,by=class, bs='fs') + s(subjectID, bs= 're'), data=df)
+g3 <- gam(md ~ s(nodeID,by=class, bs='fs') + s(subjectID, bs= 're'), data=df)
# Get smooth estimates
-dfs <- smooth_estimates(g1) %>% add_confint(coverage=.68)
+dfs <- smooth_estimates(g3) %>% add_confint(coverage=.68)
# Remove random effects and plot
filter(dfs, type != 'Random effect') %>%
@@ -64,3 +64,13 @@ ggplot(aes(x=nodeID,y=est, color=class)) +
geom_line() +
geom_ribbon(aes(ymin = lower_ci, ymax=upper_ci),alpha = 0.2 )
```
+
+Now calculate differences between the smooths for the different factors and plot out the difference an 95% CI
+
+```{r}
+ds<-difference_smooths(g3, smooth="s(nodeID)")
+ggplot(ds,aes(x=nodeID,y=diff)) +
+ geom_line() +
+ geom_ribbon(aes(ymin = lower, ymax=upper),alpha = 0.2 ) +
+ geom_hline(yintercept=0)
+```
diff --git a/examples/howto_examples/gam-tract-profiles-example.html b/examples/howto_examples/gam-tract-profiles-example.html
index 01d41d9c2..395c1cd3e 100644
--- a/examples/howto_examples/gam-tract-profiles-example.html
+++ b/examples/howto_examples/gam-tract-profiles-example.html
@@ -381,9 +381,9 @@ GAMs Tutorial
geom_ribbon(aes(ymin = lower_ci, ymax=upper_ci),alpha = 0.2 )
# The above plot looks good but now let's let the curves vary across # subjects too. What I notice here is that it dramatically effects the standard error
-g1 <- gam(md ~ s(nodeID,class, bs='fs') + s(nodeID, subjectID, bs= 're'), data=df)
+g2 <- gam(md ~ s(nodeID,class, bs='fs') + s(nodeID, subjectID, bs= 're'), data=df)
# Get smooth estimates
-dfs <- smooth_estimates(g1) %>% add_confint(coverage=.68)
+dfs <- smooth_estimates(g2) %>% add_confint(coverage=.68)
# Remove random effects and plot
filter(dfs, type != 'Random effect') %>%
@@ -392,9 +392,9 @@ GAMs Tutorial
geom_ribbon(aes(ymin = lower_ci, ymax=upper_ci),alpha = 0.2 )
# Yet another way to do the same thing and I don't get the difference
-g1 <- gam(md ~ s(nodeID,by=class, bs='fs') + s(subjectID, bs= 're'), data=df)
+g3 <- gam(md ~ s(nodeID,by=class, bs='fs') + s(subjectID, bs= 're'), data=df)
# Get smooth estimates
-dfs <- smooth_estimates(g1) %>% add_confint(coverage=.68)
+dfs <- smooth_estimates(g3) %>% add_confint(coverage=.68)
# Remove random effects and plot
filter(dfs, type != 'Random effect') %>%
@@ -402,6 +402,14 @@ GAMs Tutorial
geom_line() +
geom_ribbon(aes(ymin = lower_ci, ymax=upper_ci),alpha = 0.2 )
+Now calculate differences between the smooths for the different
+factors and plot out the difference an 95% CI
+ds<-difference_smooths(g3, smooth="s(nodeID)")
+ggplot(ds,aes(x=nodeID,y=diff)) +
+ geom_line() +
+ geom_ribbon(aes(ymin = lower, ymax=upper),alpha = 0.2 ) +
+ geom_hline(yintercept=0)
+