rockchalk (version 1.8.157)

model.data.default: Create a data frame suitable for estimating a model

Description

This is the default method. Works for lm and glm fits.

Usage

# S3 method for default
model.data(model, na.action = na.omit, ...)

Value

A data frame

Arguments

model

A fitted model

na.action

Defaults to na.omit, so model as it would appear in user workspace is re-created, except that rows with missing values are deleted. Changing this argument to na.pass will provide the data as it was in the workspace.

...

Place holder for other arguments, not used at present

Author

Paul E. Johnson

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")