A few days ago, I had asked this question. Consider this
situation:> x1 <- runif(100); x2 <- runif(100); y <- 2 + 3*x1 - 4*x2 +
rnorm(100)
> m1 <- summary(lm(y ~ x1))
> m2 <- summary(lm(y ~ x2))
> m3 <- summary(lm(y ~ x1 + x2))
You have estimated 3 different "competing" models, and suppose you
want to present the set of models in one table. xtable(m1) is cool,
but that would give us 3 different tables.
What I want is this table:
-----------------------------------------------------------
M1 M2 M3
-----------------------------------------------------------
Intercept 0.0816 3.6292 2.2272
(0.5533) (0.2316)*** (0.2385)***
x1 2.8151 2.7606
(0.5533)*** (0.3193)***
x2 -4.2899 -4.2580
(0.401)*** (0.3031)***
$\sigma_e$ 1.538 1.175 0.8873
$R^2$ 0.2089 0.5385 0.7393
-----------------------------------------------------------
I was given feedback from this mailing list that this is a specialised
display and requires custom code. So I wrote some code. I will be very
happy if you could look at this code, and give me ideas on how to do
it better, and how to generalise it. I am most unhappy with the fact
that right now, I'm tied to the fact that summary.lm() gives you
something which has a $coefficients. Ideally this kind of display
should be general for all kinds of models.
My code produces two results:
> numbers
M1 M2 M3
Intercept 0.0691 3.1110 1.7831
t 0.2471 12.1860 6.8888
x1 2.6595 NA 2.7772
t 5.3837 NA 8.0206
x2 NA -3.2788 -3.3683
t NA -7.6922 -10.1331
Residual std. dev. 1.3567 1.2195 0.9505
$R^2$ 0.2283 0.3765 0.6251
Adjusted $R^2$ 0.2204 0.3701 0.6174
and:
> specialised.latex.generation(numbers)
\hline
& M1 & M2 & M3\\
\hline
Intercept & 0.0691 & 3.111 & 1.7831\\
& (0.2471) & (12.186)$^\mbox{***} &
(6.8888)$^\mbox{***}\\[1mm]
x1 & 2.6595 & & 2.7772\\
& (5.3837)$^\mbox{***} & & (8.0206)$^\mbox{***}\\[1mm]
x2 & & -3.2788 & -3.3683\\
& & (-7.6922)$^\mbox{***} & (-10.1331)$^\mbox{***}\\[1mm]
Residual std. dev. & 1.3567 & 1.2195 & 0.9505\\
$R^2$ & 0.2283 & 0.3765 & 0.6251\\
Adjusted $R^2$ & 0.2204 & 0.3701 & 0.6174\\
\hline
mmp <- function(regressors, bottom.matter, models.names, allmodels) {
numbers <- matrix(NA, nrow=(2*length(regressors))+length(bottom.matter),
ncol=length(models.names))
colnames(numbers) <- models.names
rownames(numbers) <- rep("t", nrow(numbers))
baserow <- 1
for (i in 1:length(regressors)) {
if (regressors[i] == "Intercept") {
regex <- "^\\(Intercept\\)$"
} else {
regex <- paste("^", regressors[i], "$",
sep="")
}
rownames(numbers)[baserow] <- regressors[i]
for (j in 1:length(allmodels)) {
m <- allmodels[[j]]
if (any(locations <- grep(regex, rownames(m$coefficients)))) {
if (length(locations) > 1) {
cat("Regex ", regex, " has multiple matches at model
", j, "\n")
return(NULL)
}
numbers[baserow,j] <- as.numeric(sprintf("%.4f",
m$coefficients[locations,1]))
numbers[baserow+1,j] <- as.numeric(sprintf("%.4f",
m$coefficients[locations,3]))
}
}
baserow <- baserow + 2
}
# Now process the bottom.matter
for (i in 1:length(bottom.matter)) {
if (bottom.matter[i] == "sigma") {
for (j in 1:length(allmodels)) {
m <- allmodels[[j]]
numbers[baserow,j] <- as.numeric(sprintf("%.4f", m$sigma))
}
rownames(numbers)[baserow] <- "Residual std. dev."
baserow <- baserow + 1
}
if (bottom.matter[i] == "r.squared") {
for (j in 1:length(allmodels)) {
m <- allmodels[[j]]
numbers[baserow,j] <- as.numeric(sprintf("%.4f",
m$r.squared))
}
rownames(numbers)[baserow] <- "$R^2$"
baserow <- baserow + 1
}
if (bottom.matter[i] == "adj.r.squared") {
for (j in 1:length(allmodels)) {
m <- allmodels[[j]]
numbers[baserow,j] <- as.numeric(sprintf("%.4f",
m$adj.r.squared))
}
rownames(numbers)[baserow] <- "Adjusted $R^2$"
baserow <- baserow + 1
}
}
numbers
}
# Given a 't' statistic, give me a string with
# '*' or '**' or '***' based on the 90%, 95% and 99%
cutoffs on N(0,1)
stars <- function(t) {
t <- abs(t)
n <- -1 + as.numeric(cut(t,breaks=c(-0.01,-qnorm(c(0.05, 0.025,
0.005)),Inf)))
if (n == 0) {
return("")
} else {
return(paste("$^\\mbox{",
paste(rep("*", n), sep="",
collapse=""),
"}", sep=""))
}
}
specialised.latex.generation <- function(numbers) {
cat("\\hline\n")
for (j in 1:ncol(numbers)) {
cat(" & ", colnames(numbers)[j])
}
cat("\\\\\n\\hline\n")
for (i in 1:nrow(numbers)) {
if (rownames(numbers)[i] == "t") {
for (j in 1:ncol(numbers)) {
if (is.na(numbers[i,j])) {
cat(" & ")
} else {
cat(" & ", sprintf("(%s)%s", numbers[i,j],
stars(numbers[i,j])))
}
}
cat("\\\\[1mm]\n")
} else {
cat(rownames(numbers)[i])
for (j in 1:ncol(numbers)) {
if (is.na(numbers[i,j])) {
cat(" & ")
} else {
cat(" & ", numbers[i, j])
}
}
cat("\\\\\n")
}
}
cat("\\hline\n")
}
x1 <- runif(100); x2 <- runif(100); y <- 2 + 3*x1 - 4*x2 + rnorm(100)
m1 <- summary(lm(y ~ x1))
m2 <- summary(lm(y ~ x2))
m3 <- summary(lm(y ~ x1 + x2))
numbers <- mmp(regressors=c("Intercept", "x1",
"x2"),
bottom.matter=c("sigma", "r.squared",
"adj.r.squared"),
models.names=c("M1", "M2", "M3"),
allmodels=list(m1, m2, m3))
numbers
specialised.latex.generation(numbers)
--
Ajay Shah http://www.mayin.org/ajayshah
ajayshah at mayin.org
http://ajayshahblog.blogspot.com
<*(:-? - wizard who doesn't know the answer.
