Update lab10

courses/stat5870Eng/labs/index.md

@@ -17,10 +17,10 @@ tagline: Statistical Methods for Research Workers
  - [Lab07](lab07/lab07.html) - RMarkdown
  - [Lab08](lab08/lab08.html) - Categorical Explanatory Variables
  - [Lab09](lab09/lab09.html) - Multiple Regression in R
+ - [Lab10](lab10/lab10.html) - ANOVA and F-tests
 
    === updated through here ===
 
- - [Lab10](lab10/lab10.html) - ANOVA and F-tests
  - [Lab11](lab11/lab11.html) - Contrasts
  - [Lab12](lab12/lab12.html) - Generalized linear mixed effect models
 

courses/stat5870Eng/labs/lab09/lab09.R

@@ -6,12 +6,14 @@
 
 
 ## install.packages("GGally") # ggpairs()
-## install.packages("GGally") # resid_panel() and resid_xpanel()
+## install.packages("ggResidpanel") # resid_panel() and resid_xpanel()
 
 library("tidyverse"); theme_set(theme_bw())
-library("GGally")
 library("Sleuth3")
+library("GGally")
+library("ggResidpanel")
 
+# My diagnostic plots
 my_diag <- function(m) {
   resid_panel(m, 
               plots = c("qq",     # qqplot for normality 
@@ -23,7 +25,7 @@ my_diag <- function(m) {
 }
 
 ggplot(case1001, aes(Height, Distance)) +
-  geom_point() + theme_bw()
+  geom_point()
 
 # First create the variables yourself
 case1001_tmp <- case1001 |>

courses/stat5870Eng/labs/lab10/lab10.R

@@ -1,82 +1,54 @@
-## -----------------------------------------------------------------------------
 library("tidyverse")
 library("Sleuth3")
 
-
-## -----------------------------------------------------------------------------
 m <- lm(Lifetime ~ Diet, data = case0501)
 anova(m)
 
-
-## -----------------------------------------------------------------------------
 summary(m)
 
-
-## -----------------------------------------------------------------------------
 m0 <- lm(Lifetime ~ 1, data = case0501) # 1 indicates intercept 
 anova(m0, m)
 
 
 
 
 
-
-## -----------------------------------------------------------------------------
 m <- lm(Speed ~ Starters + Track, data = ex0920)
 anova(m)
 
-
-## -----------------------------------------------------------------------------
 m0  <- lm(Speed ~ 1,                data = ex0920)
 mS  <- lm(Speed ~ Starters,         data = ex0920)
 mT  <- lm(Speed ~            Track, data = ex0920)
 mST <- lm(Speed ~ Starters + Track, data = ex0920)
 
-
-## -----------------------------------------------------------------------------
 anova(m0, mS ) # we are looking to add (S)tarters to the model
 anova(mS, mST) # we are looking to add (T)rack    to the model
 
-
-## -----------------------------------------------------------------------------
 anova(m0, mS, mST)
 
-
-## -----------------------------------------------------------------------------
 m  <- lm(Speed ~ Track*Starters, data = Sleuth3::ex0920)
 anova(m)
 
 
 
 
 
-
-## -----------------------------------------------------------------------------
 m <- lm(Speed ~ Track + Starters, data = Sleuth3::ex0920)
 drop1(m, test="F")
 
-
-## -----------------------------------------------------------------------------
 mT  <- lm(Speed ~ Track,            data = Sleuth3::ex0920)
 mS  <- lm(Speed ~         Starters, data = Sleuth3::ex0920)
 mST <- lm(Speed ~ Track + Starters, data = Sleuth3::ex0920)
 anova(mS,mST) # Consider adding Track into the model that already has Starters
 anova(mT,mST) # Consider adding Starters into the model that already has Track
 
-
-## -----------------------------------------------------------------------------
 m <- lm(Ingestion ~ Weight + Organic, data = Sleuth3::ex0921)
 drop1(m, test='F')
 
 
 
-
-## -----------------------------------------------------------------------------
 m <- lm(Ingestion ~ Weight * Organic, data = Sleuth3::ex0921)
 drop1(m, test = 'F')
 
-
-## -----------------------------------------------------------------------------
 m0 <- lm(Ingestion ~ Weight + Organic, data = Sleuth3::ex0921)
-anova(m0,m)
-
+anova(m0, m)

courses/stat5870Eng/labs/lab10/lab10.Rmd

@@ -238,13 +238,14 @@ it will only attempt to drop the interaction.
 m <- lm(Ingestion ~ Weight * Organic, data = Sleuth3::ex0921)
 drop1(m, test = 'F')
 ```
+
 It is not obvious from this output that you are comparing the model that
-contains the main effects with the model that additional adds the interaction.
+contains the main effects with the model that additionally adds the interaction.
 You can check this with the `anova` function.
 
 ```{r}
 m0 <- lm(Ingestion ~ Weight + Organic, data = Sleuth3::ex0921)
-anova(m0,m)
+anova(m0, m)
 ```
 
 Notice that the statistics are all the same as the `drop1` results.