Obtain standardised residuals and "Residual v.s. Fitted" plot for "mlm" object from `lm()`
<pre><code>set.seed(0)
## 2 response of 10 observations each
response &lt;- matrix(rnorm(20), 10, 2)
## 3 covariates with 10 observations each
predictors &lt;- matrix(rnorm(30), 10, 3)
fit &lt;- lm(response ~ predictors)
</code></pre>

<p>I have been generating residual plots for the entire model using:</p>

<pre><code>plot(fitted(fit),residuals(fit))
</code></pre>

<p>However, I would like to make individual plots for each predictor covariate. I can do them one at a time by:</p>

<pre><code>f &lt;- fitted(fit)
r &lt;- residual(fit)
plot(f[,1],r[,1])
</code></pre>

<p>The issue with this approach however, is that it needs to be generalizable for data sets with more predictor covariates. Is there a way that I use plot while iterating through each column of (f) and (r)? Or is there a way that <code>plot()</code> can group each co-variate by colour?</p>