```
abline(a = NULL, b = NULL, h = NULL, v = NULL, reg = NULL,
coef = NULL, untf = FALSE, ...)
```

a, b

the intercept and slope, single values.

untf

logical asking whether to *untransform*. See
‘Details’.

h

the y-value(s) for horizontal line(s).

v

the x-value(s) for vertical line(s).

coef

a vector of length two giving the intercept and slope.

reg

an object with a

`coef`

method. See ‘Details’.…

graphical parameters such as

`col`

, `lty`

and `lwd`

(possibly as vectors: see
‘Details’) and `xpd`

and the line characteristics
`lend`

, `ljoin`

and `lmitre`

.abline(a, b, untf = FALSE, \dots) abline(h =, untf = FALSE, \dots) abline(v =, untf = FALSE, \dots) abline(coef =, untf = FALSE, \dots) abline(reg =, untf = FALSE, \dots)The first form specifies the line in intercept/slope form (alternatively

`a`

can be specified on its own and is taken to
contain the slope and intercept in vector form). The `h=`

and `v=`

forms draw horizontal and vertical lines
at the specified coordinates. The `coef`

form specifies the line by a vector containing the
slope and intercept. `reg`

is a regression object with a `coef`

method.
If this returns a vector of length 1 then the value is taken to be the
slope of a line through the origin, otherwise, the first 2 values are
taken to be the intercept and slope. If `untf`

is true, and one or both axes are log-transformed, then
a curve is drawn corresponding to a line in original coordinates,
otherwise a line is drawn in the transformed coordinate system. The
`h`

and `v`

parameters always refer to original coordinates. The graphical parameters `col`

, `lty`

and `lwd`

can be specified; see `par`

for details. For the
`h=`

and `v=`

usages they can be vectors of length greater
than one, recycled as necessary. Specifying an `xpd`

argument for clipping overrides
the global `par("xpd")`

setting used otherwise.`lines`

and `segments`

for connected and
arbitrary lines given by their `par`

.## Setup up coordinate system (with x == y aspect ratio): plot(c(-2,3), c(-1,5), type = "n", xlab = "x", ylab = "y", asp = 1) ## the x- and y-axis, and an integer grid abline(h = 0, v = 0, col = "gray60") text(1,0, "abline( h = 0 )", col = "gray60", adj = c(0, -.1)) abline(h = -1:5, v = -2:3, col = "lightgray", lty = 3) abline(a = 1, b = 2, col = 2) text(1,3, "abline( 1, 2 )", col = 2, adj = c(-.1, -.1)) ## Simple Regression Lines: require(stats) sale5 <- c(6, 4, 9, 7, 6, 12, 8, 10, 9, 13) plot(sale5) abline(lsfit(1:10, sale5)) abline(lsfit(1:10, sale5, intercept = FALSE), col = 4) # less fitting z <- lm(dist ~ speed, data = cars) plot(cars) abline(z) # equivalent to abline(reg = z) or abline(coef = coef(z)) ## trivial intercept model abline(mC <- lm(dist ~ 1, data = cars)) ## the same as abline(a = coef(mC), b = 0, col = "blue")

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