some updates

day-1/index.Rmd

@@ -350,6 +350,31 @@ $$\log_e \left( \frac{\pi}{1-\pi} \right) = \beta_0 + \beta_1 X_i$$
 
 ---
 
+# Link function
+
+```{r logit-transformation, echo = FALSE, fig.align = "center"}
+wasp <- read_csv(here("data", "darlingtonia.csv"), comment = "#",
+                 col_types = "dl")
+# plot
+p1 <- ggplot(wasp,
+  aes(x = leafHeight, y = as.numeric(visited))
+) +
+  geom_point() +
+  labs(
+    x = "Leaf Height [cm]",
+    y = "Probability of visitation"
+  )
+df <- data.frame(p = (1:999) / 1000)
+df <- transform(df, logitp = qlogis(p))
+p2 <- p1 + geom_smooth(method = "lm", se = FALSE)
+p3 <- ggplot(df, aes(x = p, y = logitp)) + geom_line() +
+  ylab(expression("Log Odds" ~~~ log * bgroup("(", frac(p, 1 - p),")"))) +
+  xlab("Probability of visitation") + coord_flip() # ? flip this
+p2 + p3
+```
+
+---
+
 # Logistic regression &mdash; *Darlingtonia*
 
 ```{r load-darl-data, echo = TRUE}

day-1/index.html

@@ -311,6 +311,12 @@
 
 ---
 
+# Link function
+
+<img src="index_files/figure-html/logit-transformation-1.svg" style="display: block; margin: auto;" />
+
+---
+
 # Logistic regression — *Darlingtonia*
 
 

day-2/index.Rmd

@@ -1317,81 +1317,6 @@ x2_bs |>
 ---
 class: inverse middle center big-subsection
 
-# Penalties
-
----
-
-# How wiggly?
-
-$$
-\int_{\mathbb{R}} [f^{\prime\prime}]^2 dx = \boldsymbol{\beta}^{\mathsf{T}}\mathbf{S}\boldsymbol{\beta}
-$$
-
-$\mathbf{S}$ is known as the penalty matrix
-
-Penalty matrix doesn't have to be derivative based — *P-splines*
-
-As long as we can write $\mathbf{S}$ so that it measures the complexity of smooth as a function of the parameters of the smooth we're OK
-
----
-
-# TPRS basis
-
-```{r, out.width = "85%", fig.align = "center"}
-new_df <- with(mcycle, tibble(times = evenly(times, n = 100)))
-bfun <- basis(s(times), data = new_df, constraints = TRUE)
-draw(bfun) + facet_wrap(~ .bf)
-```
-
----
-
-# TPRS basis
-
-```{r}
-m <-  gam(accel ~ s(times), dat = mcycle, method = "REML")
-S <- penalty(m, smooth = "s(times)")
-S
-```
-
----
-
-# TPRS basis & penalty
-
-.center[
-```{r, echo = FALSE, dev = "png", out.width = "100%", dpi = 300}
-library("patchwork")
-p1 <- draw(bfun) + facet_wrap(~ .bf)
-p2 <- draw(S)
-p1 + p2 + plot_layout(ncol = 2)
-```
-]
----
-
-# CRS basis & penalty
-
-```{r}
-m <-  gam(accel ~ s(times, bs = "cr"), data = mcycle, method = "REML")
-S <- penalty(m, smooth = "s(times)")
-# draw(S)
-```
-
----
-
-# CRS basis & penalty
-
-.center[
-```{r, echo = FALSE, dev = "png", out.width = "100%", dpi = 300}
-library("patchwork")
-bfun_cc <- basis(s(times, bs = "cr"), data = new_df, constraints = TRUE)
-p1 <- draw(bfun_cc) + facet_wrap(~ .bf)
-p2 <- draw(S)
-p1 + p2 + plot_layout(ncol = 2)
-```
-]
-
----
-class: inverse middle center big-subsection
-
 # gratia 📦
 
 ---
@@ -1472,3 +1397,78 @@ draw(m_sim, residuals = TRUE, overall_uncertainty = TRUE,
      unconditional = TRUE, rug = FALSE)
 ```
 ]
+
+---
+class: inverse middle center big-subsection
+
+# Penalties
+
+---
+
+# How wiggly?
+
+$$
+\int_{\mathbb{R}} [f^{\prime\prime}]^2 dx = \boldsymbol{\beta}^{\mathsf{T}}\mathbf{S}\boldsymbol{\beta}
+$$
+
+$\mathbf{S}$ is known as the penalty matrix
+
+Penalty matrix doesn't have to be derivative based &mdash; *P-splines*
+
+As long as we can write $\mathbf{S}$ so that it measures the complexity of smooth as a function of the parameters of the smooth we're OK
+
+---
+
+# TPRS basis
+
+```{r, out.width = "85%", fig.align = "center"}
+new_df <- with(mcycle, tibble(times = evenly(times, n = 100)))
+bfun <- basis(s(times), data = new_df, constraints = TRUE)
+draw(bfun) + facet_wrap(~ .bf)
+```
+
+---
+
+# TPRS basis
+
+```{r}
+m <-  gam(accel ~ s(times), dat = mcycle, method = "REML")
+S <- penalty(m, smooth = "s(times)")
+S
+```
+
+---
+
+# TPRS basis & penalty
+
+.center[
+```{r, echo = FALSE, dev = "png", out.width = "100%", dpi = 300}
+library("patchwork")
+p1 <- draw(bfun) + facet_wrap(~ .bf)
+p2 <- draw(S)
+p1 + p2 + plot_layout(ncol = 2)
+```
+]
+---
+
+# CRS basis & penalty
+
+```{r}
+m <-  gam(accel ~ s(times, bs = "cr"), data = mcycle, method = "REML")
+S <- penalty(m, smooth = "s(times)")
+# draw(S)
+```
+
+---
+
+# CRS basis & penalty
+
+.center[
+```{r, echo = FALSE, dev = "png", out.width = "100%", dpi = 300}
+library("patchwork")
+bfun_cc <- basis(s(times, bs = "cr"), data = new_df, constraints = TRUE)
+p1 <- draw(bfun_cc) + facet_wrap(~ .bf)
+p2 <- draw(S)
+p1 + p2 + plot_layout(ncol = 2)
+```
+]

day-2/index.html

@@ -1232,92 +1232,6 @@
 ---
 class: inverse middle center big-subsection
 
-# Penalties
-
----
-
-# How wiggly?
-
-$$
-\int_{\mathbb{R}} [f^{\prime\prime}]^2 dx = \boldsymbol{\beta}^{\mathsf{T}}\mathbf{S}\boldsymbol{\beta}
-$$
-
-`\(\mathbf{S}\)` is known as the penalty matrix
-
-Penalty matrix doesn't have to be derivative based — *P-splines*
-
-As long as we can write `\(\mathbf{S}\)` so that it measures the complexity of smooth as a function of the parameters of the smooth we're OK
-
----
-
-# TPRS basis
-
-
-```r
-new_df <- with(mcycle, tibble(times = evenly(times, n = 100)))
-bfun <- basis(s(times), data = new_df, constraints = TRUE)
-draw(bfun) + facet_wrap(~ .bf)
-```
-
-<img src="index_files/figure-html/unnamed-chunk-6-1.svg" width="85%" style="display: block; margin: auto;" />
-
----
-
-# TPRS basis
-
-
-```r
-m <-  gam(accel ~ s(times), dat = mcycle, method = "REML")
-S <- penalty(m, smooth = "s(times)")
-S
-```
-
-```
-## # A tibble: 81 × 6
-##    .smooth  .type .penalty .row  .col  .value
-##    <chr>    <chr> <chr>    <chr> <chr>  <dbl>
-##  1 s(times) TPRS  s(times) F1    F1    13.1  
-##  2 s(times) TPRS  s(times) F1    F2     2.71 
-##  3 s(times) TPRS  s(times) F1    F3     6.61 
-##  4 s(times) TPRS  s(times) F1    F4     2.05 
-##  5 s(times) TPRS  s(times) F1    F5     6.49 
-##  6 s(times) TPRS  s(times) F1    F6    -2.38 
-##  7 s(times) TPRS  s(times) F1    F7     7.97 
-##  8 s(times) TPRS  s(times) F1    F8     0.450
-##  9 s(times) TPRS  s(times) F1    F9     0    
-## 10 s(times) TPRS  s(times) F2    F1     2.71 
-## # ℹ 71 more rows
-```
-
----
-
-# TPRS basis & penalty
-
-.center[
-<img src="index_files/figure-html/unnamed-chunk-8-1.png" width="100%" />
-]
----
-
-# CRS basis & penalty
-
-
-```r
-m <-  gam(accel ~ s(times, bs = "cr"), data = mcycle, method = "REML")
-S <- penalty(m, smooth = "s(times)")
-# draw(S)
-```
-
----
-
-# CRS basis & penalty
-
-.center[
-<img src="index_files/figure-html/unnamed-chunk-10-1.png" width="100%" />
-]
-
----
-class: inverse middle center big-subsection
-
 # gratia 📦
 
 ---
@@ -1417,6 +1331,92 @@
 
 .center[
 <img src="index_files/figure-html/draw-mcycle-options-plot-1.svg" width="95%" />
+]
+
+---
+class: inverse middle center big-subsection
+
+# Penalties
+
+---
+
+# How wiggly?
+
+$$
+\int_{\mathbb{R}} [f^{\prime\prime}]^2 dx = \boldsymbol{\beta}^{\mathsf{T}}\mathbf{S}\boldsymbol{\beta}
+$$
+
+`\(\mathbf{S}\)` is known as the penalty matrix
+
+Penalty matrix doesn't have to be derivative based — *P-splines*
+
+As long as we can write `\(\mathbf{S}\)` so that it measures the complexity of smooth as a function of the parameters of the smooth we're OK
+
+---
+
+# TPRS basis
+
+
+```r
+new_df <- with(mcycle, tibble(times = evenly(times, n = 100)))
+bfun <- basis(s(times), data = new_df, constraints = TRUE)
+draw(bfun) + facet_wrap(~ .bf)
+```
+
+<img src="index_files/figure-html/unnamed-chunk-6-1.svg" width="85%" style="display: block; margin: auto;" />
+
+---
+
+# TPRS basis
+
+
+```r
+m <-  gam(accel ~ s(times), dat = mcycle, method = "REML")
+S <- penalty(m, smooth = "s(times)")
+S
+```
+
+```
+## # A tibble: 81 × 6
+##    .smooth  .type .penalty .row  .col  .value
+##    <chr>    <chr> <chr>    <chr> <chr>  <dbl>
+##  1 s(times) TPRS  s(times) F1    F1    13.1  
+##  2 s(times) TPRS  s(times) F1    F2     2.71 
+##  3 s(times) TPRS  s(times) F1    F3     6.61 
+##  4 s(times) TPRS  s(times) F1    F4     2.05 
+##  5 s(times) TPRS  s(times) F1    F5     6.49 
+##  6 s(times) TPRS  s(times) F1    F6    -2.38 
+##  7 s(times) TPRS  s(times) F1    F7     7.97 
+##  8 s(times) TPRS  s(times) F1    F8     0.450
+##  9 s(times) TPRS  s(times) F1    F9     0    
+## 10 s(times) TPRS  s(times) F2    F1     2.71 
+## # ℹ 71 more rows
+```
+
+---
+
+# TPRS basis & penalty
+
+.center[
+<img src="index_files/figure-html/unnamed-chunk-8-1.png" width="100%" />
+]
+---
+
+# CRS basis & penalty
+
+
+```r
+m <-  gam(accel ~ s(times, bs = "cr"), data = mcycle, method = "REML")
+S <- penalty(m, smooth = "s(times)")
+# draw(S)
+```
+
+---
+
+# CRS basis & penalty
+
+.center[
+<img src="index_files/figure-html/unnamed-chunk-10-1.png" width="100%" />
 ]
     </textarea>
 <style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>