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Fix weighted.mean(w =) argument's denominator #2

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Fix weighted.mean(w =) argument's denominator #2

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16 changes: 8 additions & 8 deletions Rmarkdown/Cross-ValidationInClassHO.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -191,7 +191,7 @@ The left side of Figure 5.2 on page 178 of @james_introduction_2013 shows the va
```{r}
library(ISLR)
n <- nrow(Auto)
plot(1:10, type ="n", xlab = "Degree of Polynomial", ylim = c(15, 30),
plot(1:10, type = "n", xlab = "Degree of Polynomial", ylim = c(15, 30),
ylab = "Mean Squared Prediction Error")
IND <- sample(1:n, size = floor(n/2), replace = FALSE)
train <- Auto[IND, ]
Expand Down Expand Up @@ -277,11 +277,11 @@ table(folds)
sum(xtabs(~folds))
for(j in 1:k){
modq <- lm(y ~ poly(x, 2, raw = TRUE), data = DF[folds != j, ])
pred <- predict(modq, newdata = DF[folds ==j, ])
pred <- predict(modq, newdata = DF[folds == j, ])
MSPE[j] <- mean((DF[folds == j, ]$y - pred)^2)
}
MSPE
weighted.mean(MSPE, table(folds)/sum(folds))
weighted.mean(MSPE, w = table(folds)/sum(xtabs(~folds)))
```

### Using `caret`
Expand Down Expand Up @@ -382,7 +382,7 @@ The right side of Figure 5.4 on page 180 of @james_introduction_2013 shows the 1

```{r}
# Your code here
plot(1:10, type ="n", xlab = "Degree of Polynomial", ylim = c(16, 26),
plot(1:10, type = "n", xlab = "Degree of Polynomial", ylim = c(16, 26),
ylab = "Mean Squared Prediction Error", main = "10-fold CV")
k <- 10 # number of folds
MSPE <- numeric(k)
Expand All @@ -406,7 +406,7 @@ cv <- numeric(k)
```{r}
# Your Code Here
set.seed(123)
plot(1:10, type ="n", xlab = "Degree of Polynomial", ylim = c(16, 26),
plot(1:10, type = "n", xlab = "Degree of Polynomial", ylim = c(16, 26),
ylab = "Mean Squared Prediction Error", main = "10-fold CV")
k <- 10 # number of folds
MSPE <- numeric(k)
Expand Down Expand Up @@ -455,7 +455,7 @@ MSPE <- numeric(k)

```{r}
# Your Code Here
plot(1:10, type ="n", xlab = "Degree of Polynomial", ylim = c(16, 26),
plot(1:10, type = "n", xlab = "Degree of Polynomial", ylim = c(16, 26),
ylab = "Mean Squared Prediction Error", main = "10-fold CV")
#
#
Expand Down Expand Up @@ -577,7 +577,7 @@ glm.fit <- glm(hwfat ~ abs + triceps, data = HSWRESTLER)

```{r}
# Your Code Here
plot(1:10, type ="n", xlab = "Degree of Polynomial", ylim = c(16, 26),
plot(1:10, type = "n", xlab = "Degree of Polynomial", ylim = c(16, 26),
ylab = "Mean Squared Prediction Error")
k <- nrow(Auto) # number of folds
MSPE <- numeric(k)
Expand All @@ -600,7 +600,7 @@ Using the short cut formula:

```{r}
# Your Code Here
plot(1:10, type ="n", xlab = "Degree of Polynomial", ylim = c(16, 26),
plot(1:10, type = "n", xlab = "Degree of Polynomial", ylim = c(16, 26),
ylab = "Mean Squared Prediction Error")
cv <- numeric(10)
#
Expand Down