rockchalk (version 1.8.0)

model.data: Creates a "raw" (UNTRANSFORMED) data frame equivalent to the input data that would be required to fit the given model.

Description

Unlike model.frame and model.matrix, this does not return transformed variables. It deals with regression formulae that have functions like poly(x, d) in them. It differentiates x from d in those expressions. And it also manages log(x + 10).

Usage

model.data(model, na.action = na.pass)

Arguments

model

A fitted regression model in which the data argument is specified. This function will fail if the model was not fit with the data option.

na.action

Defaults to na.pass, so model as it would appear in user workspace is re-created.

Value

• A data frame

Examples

library(rockchalk)






## first, check if model.data works when there is no data argument
## This used to fail, now OK

x1 <- rnorm(100, m = 100, s = 10)
x2 <- rnorm(100, m = 50, s =20)
y <- rnorm(100, m = 40, s = 3)

m0 <- lm(y ~ log(10+x1) + x2)
m0.data <- model.data(m0)
head(m0.data)


m1 <- lm(log(43 + y) ~ log(10+x1) + x2)
m1.data <- model.data(m1)
head(m1.data)

d <- 3

m2 <- lm(log(d + y) ~ log(10+x1) + x2)
m2.data <- model.data(m2)
head(m2.data)

m3 <- lm(log(y + d) ~ log(10+x1) + x2)
m3.data <- model.data(m3)
head(m3.data)



## check numeric and categorical predictors

x1 <- rpois(100, l=6)
x2 <- rnorm(100, m=50, s=10)
x3 <- rnorm(100)
xcat1 <- gl(2,50, labels=c("M","F"))
xcat2 <- cut(rnorm(100), breaks=c(-Inf, 0, 0.4, 0.9, 1, Inf),
             labels=c("R", "M", "D", "P", "G"))
dat <- data.frame(x1, x2, x3, xcat1, xcat2)
rm(x1, x2, x3, xcat1, xcat2)
dat$xcat1n <- with(dat, contrasts(xcat1)[xcat1, ,drop=FALSE])
dat$xcat2n <- with(dat, contrasts(xcat2)[xcat2, ])

STDE <- 20
dat$y <- with(dat,
              0.03 + 0.8*x1 + 0.1*x2 + 0.7*x3 +
              xcat1n %*% c(2) + xcat2n %*% c(0.1,-2,0.3, 0.1) +
              STDE*rnorm(100))



m1 <- lm(y ~ poly(x1, 2), data=dat)
m1.data <- model.data(m1)
head(m1.data)
attr(m1.data, "varNamesRHS")

## Check to make sure d is not mistaken for a data column
d <- 2
m2 <- lm(y ~ poly(x1, d), data=dat)
m2.data <- model.data(m2)
head(m2.data)
attr(m2.data, "varNamesRHS")


## Check to see how the 10 in log is handled
m3 <- lm(y ~ log(10 + x1) + poly(x1, d) + sin(x2), data=dat)
m3.data <- model.data(m3)
head(m3.data)
attr(m3.data, "varNamesRHS")


m4 <- lm(log(50+y) ~ log(d+10+x1) + poly(x1, 2), data=dat)
m4.data <- model.data(m4)
head(m4.data)
attr(m4.data, "varNamesRHS")


m5 <- lm(y ~ x1*x1, data=dat)
m5.data <- model.data(m5)
head(m5.data)
attr(m5.data, "varNamesRHS")


m6 <- lm(y ~ x1 + I(x1^2), data=dat)
m6.data <- model.data(m6)
head(m6.data)
attr(m6.data, "varNamesRHS")


## Put in some missings.
## poly doesn't work if there are missings, but
## can test with log
dat$x1[sample(100, 5)] <- NA
dat$y[sample(100, 5)] <- NA
dat$x2[sample(100, 5)] <- NA
dat$x3[sample(100,10)] <- NA

m1 <- lm(y ~ log(10 + x1), data=dat)
m1.data <- model.data(m1)
head(m1.data)
summarize(m1.data)
attr(m1.data, "varNamesRHS")



m2 <- lm(y ~ log(x1 + 10), data=dat)
m2.data <- model.data(m2)
head(m2.data)
summarize(m1.data)
attr(m1.data, "varNamesRHS")

d <- 2
m3 <- lm(log(50+y) ~ log(d+10+x1) + x2 + sin(x3), data=dat)
m3.data <- model.data(m3)
head(m3.data)
summarize(m3.data)
attr(m3.data, "varNamesRHS")


m4 <- lm(y ~ I(x1) + I(x1^2) + log(x2), data=dat)
m4.data <- model.data(m4)
summarize(m4.data)
attr(m4.data, "varNamesRHS")


m5 <- lm(y ~ x1 + I(x1^2) + cos(x2), data=dat)
m5.data <- model.data(m5)
head(m5.data)
summarize(m5.data)
attr(m5.data, "varNamesRHS")



## Now try with some variables in the dataframe, some not

x10 <- rnorm(100)
x11 <- rnorm(100)



m6 <- lm(y ~ x1 + I(x1^2) + cos(x2) + log(10 + x10) + sin(x11) +
         x10*x11, data = dat)
m6.data <- model.data(m6)
head(m6.data)
dim(m6.data)
summarize(m5.data)
attr(m6.data, "varNamesRHS")