I am trying to expand on this function:
vcov2sls <- function(s1, s2, data, type=2) {
## turn factor variables into dummies
DATA <- as.data.frame(model.matrix(phantom ~ ., transform(data, phantom=0)))
## list variable names
vn <- lapply(list(s1=s1, s2=s2), function(s)
c(all.vars(s$call)[1], colnames(model.matrix(s))[-1]))
## auxilliary model matrix
X <- cbind(`(Intercept)`=1, DATA[, c(vn$s1[1], vn$s2[-(1:2)]), F])
## get y
y <- DATA[, vn$s2[1]]
## betas second stage
b <- s2$coefficients
## calculate corrected sums of squares
sse <- sum((y - b %*% t(X))^2)
rmse <- sqrt(mean(s2$residuals^2)) ## RMSE 2nd stage
V0 <- vcov(s2) ## biased vcov 2nd stage
dof <- s2$df.residual ## degrees of freedom 2nd stage
## calculate corrected RMSE
rmse.c <- sqrt(sse/dof)
## calculate corrected vcov
V <- (rmse.c/rmse)^2 * V0
return(V)
}
The following part turns the factors into dummies:
DATA <- as.data.frame(model.matrix(phantom ~ ., transform(data, phantom=0)))
But I don't want to convert the factors beforehand. Instead I would prefer to do in the function, turning the first three lines into:
factornames <<- stringr::str_match_all(as.character(s1$terms)[3], 'as.factor\(([A-Za-z]+)\)')[[1]][,-1]
data <- as.data.frame(data)
data[factornames] <- lapply(data[factornames], factor)
But I do not understand to adapt the DATA <- as.data.frame(model.matrix(phantom ~ ., transform(data, phantom=0))) part. For example I do not understand what phantom does and how I should replace it.
I tried:
form_all <- all.vars(as.character(s1$terms)[3])
DATA <<- as.data.frame(model.matrix(form_all, data = data))
But it does not work properly.
This works (in DF_2 factors are converted beforehand):
s1.tobit <- AER::tobit(taxrate ~ votewon + industry + size + urbanisation + vote,
left=12, right=33, data=DF_2)
yhat <- fitted(s1.tobit)
s2.tobit <- lm(sales ~ yhat + industry + size + urbanisation + vote, data=DF_2)
lmtest::coeftest(s2.tobit, vcov.=vcov2sls(s1.tobit, s2.tobit, DF_2))
This is what I want to work (NOTE THE ADDED INTERACTION TERMS):
s1.tobit <- AER::tobit(taxrate ~ votewon + as.factor(industry):as.factor(size) + urbanisation + vote,
left=12, right=33, data=DF)
yhat <- fitted(s1.tobit)
s2.tobit <- lm(sales ~ yhat + industry + size + urbanisation + vote, data=DF)
lmtest::coeftest(s2.tobit, vcov.=vcov2sls(s1.tobit, s2.tobit, DF))
DATA
DF <- structure(list(country = c("C", "C", "C", "C", "J", "J", "B",
"B", "F", "F", "E", "E", "D", "D", "F", "F", "I", "I", "J", "J",
"E", "E", "C", "C", "I", "I", "I", "I", "I", "I", "C", "C", "H",
"H", "J", "J", "G", "G", "J", "J", "I", "I", "C", "C", "D", "D",
"A", "A", "G", "G", "E", "E", "J", "J", "G", "G", "I", "I", "I",
"I", "J", "J", "G", "G", "E", "E", "G", "G", "E", "E", "F", "F",
"I", "I", "B", "B", "E", "E", "H", "H", "B", "B", "A", "A", "I",
"I", "I", "I", "F", "F", "E", "E", "I", "I", "J", "J", "D", "D",
"F", "F"), year = c(2005, 2010, 2010, 2005, 2005, 2010, 2010,
2005, 2010, 2005, 2005, 2010, 2010, 2005, 2005, 2010, 2005, 2010,
2005, 2010, 2010, 2005, 2010, 2005, 2005, 2010, 2005, 2010, 2010,
2005, 2010, 2005, 2005, 2010, 2010, 2005, 2005, 2010, 2005, 2010,
2005, 2010, 2005, 2010, 2010, 2005, 2005, 2010, 2010, 2005, 2010,
2005, 2010, 2005, 2010, 2005, 2010, 2005, 2010, 2005, 2010, 2005,
2010, 2005, 2010, 2005, 2010, 2005, 2005, 2010, 2005, 2010, 2005,
2010, 2005, 2010, 2005, 2010, 2005, 2010, 2010, 2005, 2005, 2010,
2005, 2010, 2010, 2005, 2010, 2005, 2010, 2005, 2005, 2010, 2005,
2010, 2010, 2005, 2010, 2005), sales = c(15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72,
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9), industry = c("D",
"D", "E", "E", "F", "F", "F", "F", "D", "D", "E", "E", "D", "D",
"E", "E", "F", "F", "F", "F", "D", "D", "F", "F", "E", "E", "D",
"D", "D", "D", "E", "E", "F", "F", "D", "D", "E", "E", "E", "E",
"D", "D", "E", "E", "D", "D", "D", "D", "E", "E", "D", "D", "F",
"F", "D", "D", "D", "D", "E", "E", "D", "D", "E", "E", "D", "D",
"D", "D", "D", "D", "F", "F", "F", "F", "E", "E", "D", "D", "E",
"E", "F", "F", "E", "E", "F", "F", "E", "E", "F", "F", "D", "D",
"D", "D", "D", "D", "D", "D", "F", "F"), urbanisation = c("B",
"B", "A", "A", "B", "B", "A", "A", "C", "C", "C", "C", "A", "A",
"B", "B", "C", "C", "A", "A", "C", "C", "B", "B", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "C", "C", "B", "B", "B", "B",
"B", "B", "C", "C", "A", "A", "B", "B", "B", "B", "A", "A", "B",
"B", "A", "A", "A", "A", "B", "B", "C", "C", "A", "A", "C", "C",
"A", "A", "B", "B", "A", "A", "B", "B", "B", "B", "B", "B", "C",
"C", "A", "A", "A", "A", "A", "A", "A", "A", "C", "C", "A", "A",
"B", "B", "A", "A", "B", "B", "B", "B"), size = c(1, 1, 5, 5,
5, 5, 1, 1, 1, 1, 5, 5, 5, 5, 2, 2, 2, 2, 5, 5, 1, 1, 1, 1, 5,
5, 5, 5, 4, 4, 5, 5, 5, 5, 4, 4, 2, 2, 5, 5, 1, 1, 1, 1, 2, 2,
1, 1, 2, 2, 5, 5, 1, 1, 3, 3, 2, 2, 2, 2, 5, 5, 4, 4, 1, 1, 5,
5, 2, 2, 5, 5, 2, 2, 2, 2, 4, 4, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3,
5, 5, 3, 3, 2, 2, 3, 3, 1, 1, 5, 5), base_rate = c(14L, 14L,
14L, 14L, 19L, 19L, 30L, 30L, 20L, 20L, 29L, 29L, 20L, 20L, 20L,
20L, 24L, 24L, 19L, 19L, 29L, 29L, 14L, 14L, 24L, 24L, 24L, 24L,
24L, 24L, 14L, 14L, 17L, 17L, 19L, 19L, 33L, 33L, 19L, 19L, 24L,
24L, 14L, 14L, 20L, 20L, 23L, 23L, 33L, 33L, 29L, 29L, 19L, 19L,
33L, 33L, 24L, 24L, 24L, 24L, 19L, 19L, 33L, 33L, 29L, 29L, 33L,
33L, 29L, 29L, 20L, 20L, 24L, 24L, 30L, 30L, 29L, 29L, 17L, 17L,
30L, 30L, 23L, 23L, 24L, 24L, 24L, 24L, 20L, 20L, 29L, 29L, 24L,
24L, 19L, 19L, 20L, 20L, 20L, 20L), taxrate = c(12L, 14L, 14L,
12L, 21L, 18L, 30L, 30L, 20L, 20L, 29L, 30L, 20L, 20L, 20L, 20L,
24L, 24L, 21L, 18L, 30L, 29L, 14L, 12L, 24L, 24L, 24L, 24L, 24L,
24L, 14L, 12L, 18L, 19L, 18L, 21L, 33L, 32L, 21L, 18L, 24L, 24L,
12L, 14L, 20L, 20L, 22L, 25L, 32L, 33L, 30L, 29L, 18L, 21L, 32L,
33L, 24L, 24L, 24L, 24L, 18L, 21L, 32L, 33L, 30L, 29L, 32L, 33L,
29L, 30L, 20L, 20L, 24L, 24L, 30L, 30L, 29L, 30L, 18L, 19L, 30L,
30L, 22L, 25L, 24L, 24L, 24L, 24L, 20L, 20L, 30L, 29L, 24L, 24L,
21L, 18L, 20L, 20L, 20L, 20L), vote = c(0, 0, 0, 0, 1, 1, 1,
0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1,
1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,
1, 0, 1, 1, 1, 1, 0, 1, 1), votewon = c(0, 0, 0, 0, 1, 0, 1,
0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0,
0, 0, 1, 1, 0, 1, 0, 1, 1)), class = "data.frame", row.names = c(NA,
-100L))
DF_2 <- copy(DF)
## convert variables to factors beforehand
DF_2[c(1, 2, 4, 5, 6, 9, 10)] <- lapply(DF_2[c(1, 2, 4, 5, 6, 9, 10)], factor)
question from:
https://stackoverflow.com/questions/65651392/model-matrix-turning-factors-into-dummies 
