ZzKr - 11 months ago 143

R Question

I have the following regression model with transformations:

`expo <- 3`

fit <- lm( I(NewValue^(1/expo)) ~ I(CurrentValue^(1/expo)) + Age + Type -1,

data=dataReg)

plot(fit)

But plot gives me the following error:

`Error: $ operator is invalid for atomic vectors`

It might have to do with plot calling

`$`

`dataReg`

`summary`

`predict`

`resid`

Answer Source

This is actually quite a interesting observation. In fact, among all 6 plots supported by `plot.lm`

, only the Q-Q plot fails in this case. Consider the following reproducible example:

```
x <- runif(20)
y <- runif(20)
fit <- lm(I(y ^ (1/3)) ~ I(x ^ (1/3)))
## only `which = 2L` (QQ plot) fails; `which = 1, 3, 4, 5, 6` all work
stats:::plot.lm(fit, which = 2L)
```

Inside `plot.lm`

, the Q-Q plot is simply produced as follow:

```
rs <- rstandard(fit) ## standardised residuals
qqnorm(rs) ## fine
## inside `qqline(rs)`
yy <- quantile(rs, c(0.25, 0.75))
xx <- qnorm(c(0.25, 0.75))
slope <- diff(yy)/diff(xx)
int <- yy[1L] - slope * xx[1L]
abline(int, slope) ## this fails!!!
```

Error: $ operator is invalid for atomic vectors

**So this is purely a problem of abline function!** Note:

```
is.object(int)
# [1] TRUE
is.object(slope)
# [1] TRUE
```

i.e., both `int`

and `slope`

has class attribute (*read ?is.object; it is a very efficient way to check whether an object has class attribute*). What class?

```
class(int)
# [1] AsIs
class(slope)
# [1] AsIs
```

**This is the result of using I(). Precisely, they inherits such class from rs and further from the response variable. That is, if we use I() on response, the RHS of the model formula, we get this behaviour.**

You can do a few experiment here:

```
abline(as.numeric(int), as.numeric(slope)) ## OK
abline(as.numeric(int), slope) ## OK
abline(int, as.numeric(slope)) ## fails!!
abline(int, slope) ## fails!!
```

**So abline(a, b) is very sensitive to whether the first argument a has class attribute or not.**

Why? Because `abline`

can accept a linear model object with "lm" class. Inside `abline`

:

```
if (is.object(a) || is.list(a)) {
p <- length(coefa <- as.vector(coef(a)))
```

**If a has a class, abline is assuming it as a model object (regardless whether it is really is!!!), then try to use coef to obtain coefficients. The check being done here is fairly not robust; we can make abline fail rather easily:**

```
plot(0:1, 0:1)
a <- 0 ## plain numeric
abline(a, 1) ## OK
class(a) <- "whatever" ## add a class
abline(a, 1) ## oops, fails!!!
```

Error: $ operator is invalid for atomic vectors

**So here is the conclusion: avoid using I() on your response variable in the model formula.** It is OK to have

`I()`

on covariates, but not on response. `lm`

and most generic functions won't have trouble dealing with this, but `plot.lm`

will.