Push Lec4-02

Ongoing Lecture 4

.Rproj.user/B584AE7E/pcs/windowlayoutstate.pper

@@ -1,6 +1,6 @@
 {
     "left": {
-        "splitterpos": 401,
+        "splitterpos": 155,
         "topwindowstate": "NORMAL",
         "panelheight": 959,
         "windowheight": 997

.Rproj.user/B584AE7E/sources/session-605e3cd7/77F68D30 → .Rproj.user/B584AE7E/sources/per/t/77F68D30

@@ -3,7 +3,7 @@
     "path": "C:/Users/lacor/OneDrive/100. Personal/SelfDevelopment/25. Project 2022/Statistical-Thinking-2023/Statistical Thinking 2023_v2.qmd",
     "project_path": "Statistical Thinking 2023_v2.qmd",
     "type": "quarto_markdown",
-    "hash": "1895704522",
+    "hash": "799310168",
     "contents": "",
     "dirty": false,
     "created": 1673908998203.0,
@@ -16,16 +16,16 @@
         "Source": "Source",
         "docOutlineVisible": "1",
         "rmdVisualCollapsedChunks": "",
-        "rmdVisualModeLocation": "10046:19198",
-        "cursorPosition": "527,0",
+        "rmdVisualModeLocation": "11232:22202",
+        "cursorPosition": "602,0",
         "scrollLine": "0"
     },
     "folds": "",
-    "lastKnownWriteTime": 1674170667,
+    "lastKnownWriteTime": 1674187233,
     "encoding": "UTF-8",
     "collab_server": "",
     "source_window": "",
-    "last_content_update": 1674170667303,
+    "last_content_update": 1674187233984,
     "read_only": false,
     "read_only_alternatives": []
 }

.Rproj.user/B584AE7E/sources/session-605e3cd7/77F68D30-contents → .Rproj.user/B584AE7E/sources/per/t/77F68D30-contents

@@ -549,3 +549,57 @@ ggplot(df, aes(x=W_contrast,fill=W_flag))+
   geom_density()+
   theme_bw()
 ```
+
+Now, what is the direct effect of sex over weight?
+
+```{r, message=FALSE}
+S <- rbern(100)+1
+dat <- sim_HW(S, b=c(.5,.5), a=c(0,10))
+
+# Indirect effect (b) is the same for men and women
+dat |> 
+  ggplot(aes(x=H, y=W, color=factor(S)))+
+  geom_point()+
+  geom_smooth(method = lm)+
+  # facet_wrap(~S)+
+  theme_bw()
+```
+
+```{r, message=FALSE}
+dat <- list(
+  W = d$weight,
+  H = d$height,
+  Hbar = mean(d$height),
+  S = d$male+1 )
+
+m_SHW <- quap(
+  alist(
+    W ~ dnorm(mu,sigma),
+    mu <- a[S]+b[S]*(H-Hbar),
+    a[S] ~ dnorm(60,10),
+    b[S] ~ dunif(0,1),
+    sigma ~ dunif(0,10)
+  ), data=dat
+)
+data.frame(dat) |> 
+  ggplot(aes(x=H, y=W, color=factor(S)))+
+  geom_point()+
+  geom_smooth(method = lm)+
+  # facet_wrap(~S)+
+  theme_bw()
+
+precis(m_SHW,depth=2)
+```
+
+Note that regression line's slopes are almost the same for both male and female.
+
+```{r}
+post <- extract.samples(m_SHW)
+dens(post$b[,1],xlim=c(.3, .9),ylim=c(0,8),
+     lwd=3,col=2,xlab="posterior mean weight(kg)")
+dens(post$b[,2],lwd=3,col=4,add=TRUE)
+```
+
+```{r}
+
+```

.Rproj.user/B584AE7E/sources/session-605e3cd7/E0A14F48 → .Rproj.user/B584AE7E/sources/per/t/E0A14F48

@@ -3,7 +3,7 @@
     "path": "C:/Users/lacor/OneDrive/100. Personal/SelfDevelopment/20. Project 2019/StockMarket/Asset_Analysis_v23.Rmd",
     "project_path": null,
     "type": "r_markdown",
-    "hash": "0",
+    "hash": "1870848255",
     "contents": "",
     "dirty": false,
     "created": 1674164268355.0,

.Rproj.user/B584AE7E/sources/prop/EA93BB00

@@ -5,7 +5,7 @@
     "Source": "Source",
     "docOutlineVisible": "1",
     "rmdVisualCollapsedChunks": "",
-    "rmdVisualModeLocation": "10046:19198",
-    "cursorPosition": "527,0",
+    "rmdVisualModeLocation": "11232:22202",
+    "cursorPosition": "602,0",
     "scrollLine": "0"
 }

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/c6t5kj9r0xqzx/000019.csv

@@ -0,0 +1,10 @@
+"0","S <- rbern(100)+1"
+"0","dat <- sim_HW(S, b=c(.5,.5), a=c(0,10))"
+"0",""
+"0","# Indirect effect (b) is the same for men and women"
+"0","dat |> "
+"0","  ggplot(aes(x=H, y=W, color=factor(S)))+"
+"0","  geom_point()+"
+"0","  geom_smooth(method = lm)+"
+"0","  # facet_wrap(~S)+"
+"0","  theme_bw()"

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/c6t5kj9r0xqzx/00001a.metadata

@@ -0,0 +1 @@
+{"height":408.5290482076638,"width":661.0,"size_behavior":0,"conditions":[]}

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cgq5kq1n6i5pg/00001e.csv

@@ -0,0 +1,21 @@
+"0","dat <- list("
+"0","  W = d$weight,"
+"0","  H = d$height,"
+"0","  Hbar = mean(d$height),"
+"0","  S = d$male+1 )"
+"0",""
+"0","m_SHW <- quap("
+"0","  alist("
+"0","    W ~ dnorm(mu,sigma),"
+"0","    mu <- a[S]+b[S]*(H-Hbar),"
+"0","    a[S] ~ dnorm(60,10),"
+"0","    b[S] ~ dunif(0,1),"
+"0","    sigma ~ dunif(0,10)"
+"0","  ), data=dat"
+"0",")"
+"0","data.frame(dat) |> "
+"0","  ggplot(aes(x=H, y=W, color=factor(S)))+"
+"0","  geom_point()+"
+"0","  geom_smooth(method = lm)+"
+"0","  # facet_wrap(~S)+"
+"0","  theme_bw()"

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cgq5kq1n6i5pg/00001f.metadata

@@ -0,0 +1 @@
+{"height":408.5290482076638,"width":661.0,"size_behavior":0,"conditions":[]}

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cgq5kq1n6i5pg/000020.csv

@@ -0,0 +1,2 @@
+"0",""
+"0","precis(m_SHW,depth=2)"

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cgq5kq1n6i5pg/000021.metadata

@@ -0,0 +1 @@
+{"classes":["data.frame"],"nrow":5,"ncol":4,"summary":{"Description":["df [5 × 4]"]}}

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cnh2brq8uipgl/00000f.csv

@@ -0,0 +1,6 @@
+"0","xseq <- seq(from=130,to=190, len=50)"
+"0","muF <- "
+"0","  link(m_adults2, data=list(S=rep(1,50),H=xseq,Hbar=mean(d$height)))"
+"2","Error in h(simpleError(msg, call)) : 
+  error in evaluating the argument 'fit' in selecting a method for function 'link': object 'm_adults2' not found
+"

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cnh2brq8uipgl/000010.error

@@ -0,0 +1 @@
+{"frames":[{"func":"link(m_adults2, data = list(S = rep(1, 50), H = xseq, Hbar = mean(d$height)))","file":"","line_number":0,"end_line_number":0,"character_number":0,"end_character_number":0},{"func":".handleSimpleError(function (cond) \n.Internal(C_tryCatchHelper(addr, 1L, cond)), \"object 'm_adults2' not found\", \n    base::quote(link(m_adults2, data = list(S = rep(1, 50), H = xseq, \n        Hbar = mean(d$height)))))","file":"","line_number":0,"end_line_number":0,"character_number":0,"end_character_number":0},{"func":"h(simpleError(msg, call))","file":"","line_number":0,"end_line_number":0,"character_number":0,"end_character_number":0}],"message":"Error in h(simpleError(msg, call)) : \n  error in evaluating the argument 'fit' in selecting a method for function 'link': object 'm_adults2' not found\n"}

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cuugb0843jsqr/00001f.csv

@@ -0,0 +1,3 @@
+"0","post <- extract.samples(m_SHW)"
+"0","dens(post$b[,1],xlim=c(.3, .9),ylim=c(0,8),"
+"0","     lwd=3,col=2,xlab=""posterior mean weight(kg"")"

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cuugb0843jsqr/000021.csv

@@ -0,0 +1 @@
+"0","dens(post$b[,2],lwd=3,col=4,add=TRUE)"

.Rproj.user/shared/notebooks/39C21CC9-Statistical Thinking 2023_v2/1/s/cuugb0843jsqr/000022.metadata

@@ -0,0 +1 @@
+{"height":408.5290482076638,"width":661.0,"size_behavior":0,"conditions":[]}

Statistical Thinking 2023_v2.qmd

@@ -549,3 +549,57 @@ ggplot(df, aes(x=W_contrast,fill=W_flag))+
   geom_density()+
   theme_bw()
 ```
+
+Now, what is the direct effect of sex over weight?
+
+```{r, message=FALSE}
+S <- rbern(100)+1
+dat <- sim_HW(S, b=c(.5,.5), a=c(0,10))
+
+# Indirect effect (b) is the same for men and women
+dat |> 
+  ggplot(aes(x=H, y=W, color=factor(S)))+
+  geom_point()+
+  geom_smooth(method = lm)+
+  # facet_wrap(~S)+
+  theme_bw()
+```
+
+```{r, message=FALSE}
+dat <- list(
+  W = d$weight,
+  H = d$height,
+  Hbar = mean(d$height),
+  S = d$male+1 )
+
+m_SHW <- quap(
+  alist(
+    W ~ dnorm(mu,sigma),
+    mu <- a[S]+b[S]*(H-Hbar),
+    a[S] ~ dnorm(60,10),
+    b[S] ~ dunif(0,1),
+    sigma ~ dunif(0,10)
+  ), data=dat
+)
+data.frame(dat) |> 
+  ggplot(aes(x=H, y=W, color=factor(S)))+
+  geom_point()+
+  geom_smooth(method = lm)+
+  # facet_wrap(~S)+
+  theme_bw()
+
+precis(m_SHW,depth=2)
+```
+
+Note that regression line's slopes are almost the same for both male and female.
+
+```{r}
+post <- extract.samples(m_SHW)
+dens(post$b[,1],xlim=c(.3, .9),ylim=c(0,8),
+     lwd=3,col=2,xlab="posterior mean weight(kg)")
+dens(post$b[,2],lwd=3,col=4,add=TRUE)
+```
+
+```{r}
+
+```