Here's what `mypre`

does, given `dat`

as an argument. In practice other data, in the same form as `dat`

would be used.

```
dat <- data.frame(
Triceps =
c(19.5, 24.7, 30.7, 29.8, 19.1, 25.6, 31.4, 27.9, 22.1, 25.5, 31.1, 30.4, 18.7, 19.7, 14.6, 29.5, 27.7, 30.2, 22.7, 25.2),
Thigh =
c(43.1, 49.8, 51.9, 54.3, 42.2, 53.9, 58.5, 52.1, 49.9, 53.5, 56.6, 56.7, 46.5, 44.2, 42.7, 54.4, 55.3, 58.6, 48.2, 51),
Midarm =
c(29.1, 28.2, 37, 31.1, 30.9, 23.7, 27.6, 30.6, 23.2, 24.8, 30, 28.3, 23, 28.6, 21.3, 30.1, 25.7, 24.6, 27.1, 27.5),
Bodyfat =
c(11.9, 22.8, 18.7, 20.1, 12.9, 21.7, 27.1, 25.4, 21.3, 19.3, 25.4, 27.2, 11.7, 17.8, 12.8, 23.9, 22.6, 25.4, 14.8, 21.1)
)
fit1 <- lm(Bodyfat ~ Triceps + Thigh + Midarm, data = dat)
mypre <- function(data, mod) {
varnames <- as.character(formula(mod))[3]
varnames <- trimws(unlist(stringr::str_split(varnames, "\\+")))
coef <- coef(mod)
newdata <- data[, varnames]
len <- length(newdata[[1]])
y <- vector(length = len)
for (i in 1:len) {
y[i] <- sum(coef[-1] * unlist(newdata[i, ])) + coef[1]
}
return(y)
}
mypre(dat, fit1)
#> [1] 14.85499 20.21884 20.98668 23.12732 11.75761 22.24372 25.71432 22.27064
#> [9] 19.59482 20.54838 24.59556 24.99231 15.00940 13.67231 11.81195 23.72747
#> [17] 22.97360 26.78590 18.52628 20.48791
```

`mypre`

is a laborious way of accomplishing the same thing with

```
predict(fit1,newdata)
```

Most of `mypre`

is housekeeping. The lifting is done by

```
y[i] <- sum(coef[-1] * unlist(newdata[i,])) + coef[1]
```

which simply applies the `fit1`

coefficients to the new data seriatim.