1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
|
bgtest <- function(formula, order = 1, order.by = NULL, type = c("Chisq", "F"), data = list())
{
dname <- paste(deparse(substitute(formula)))
if(!inherits(formula, "formula")) {
X <- if(is.matrix(formula$x))
formula$x
else model.matrix(terms(formula), model.frame(formula))
y <- if(is.vector(formula$y))
formula$y
else model.response(model.frame(formula))
} else {
mf <- model.frame(formula, data = data)
y <- model.response(mf)
X <- model.matrix(formula, data = data)
}
if(!is.null(order.by))
{
if(inherits(order.by, "formula")) {
z <- model.matrix(order.by, data = data)
z <- as.vector(z[,ncol(z)])
} else {
z <- order.by
}
X <- as.matrix(X[order(z),])
y <- y[order(z)]
}
n <- nrow(X)
k <- ncol(X)
order <- 1:order
m <- length(order)
resi <- lm.fit(X,y)$residuals
Z <- sapply(order, function(x) c(rep(0, x), resi[1:(n-x)]))
auxfit <- lm.fit(cbind(X,Z), resi)
switch(match.arg(type),
"Chisq" = {
bg <- n * sum(auxfit$fitted^2)/sum(resi^2)
p.val <- pchisq(bg, m, lower.tail = FALSE)
df <- m
names(df) <- "df"
},
"F" = {
uresi <- auxfit$residuals
bg <- ((sum(resi^2) - sum(uresi^2))/m) / (sum(uresi^2) / (n-k-m))
df <- c(m, n-k-m)
names(df) <- c("df1", "df2")
p.val <- pf(bg, df1 = df[1], df2 = df[2], lower.tail = FALSE)
})
names(bg) <- "LM test"
RVAL <- list(statistic = bg, parameter = df,
method = paste("Breusch-Godfrey test for serial correlation of order", max(order)),
p.value = p.val,
data.name = dname)
class(RVAL) <- "htest"
return(RVAL)
}
|
