# Create a data frame suitable for estimating a model This is the default method. Works for lm and glm fits. ```r ## Default S3 method: model.data(model, na.action = na.omit, ...) ``` ## 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 ## Returns A data frame ## Examples ```r 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") ``` ## Author(s) Paul E. Johnson pauljohn@ku.edu

model.data.default function