CODEWITHSUNDEEP
rplotstatisticsdataframeregression
Mona Jalal
Mona Jalal

Reputation: 38135

How to create a plot consisting of multiple residuals?

How can I make a residual plot according to the following (what are y_hat and e here)?

enter image description here

Is this a form of residual plot as well?

beeflm=lm(PBE ~ CBE + PPO + CPO + PFO +DINC + CFO+RDINC+RFP+YEAR, data = beef)
summary(beeflm)
qqnorm(residuals(beeflm))
#plot(beeflm) #in manuals I have seen they use this but it gives me multiple plot

or is this one correct?

plot(beeflm$residuals,beeflm$fitted.values)

I know through the comments that plot(beeflm,which=1) is correct but according to the stated question I should use matplot but I receive the following error:

matplot(beeflm,which=1,
+         main = "Beef: residual plot",
+         ylab = expression(e[i]), # only 1st is taken
+         xlab = expression(hat(y[i])))

Error in xy.coords(x, y, xlabel, ylabel, log = log) : 
  (list) object cannot be coerced to type 'double'

And when I use plot I receive the following error:

plot(beeflm,which=1,main="Beef: residual plot",ylab = expression(e[i]),xlab = expression(hat(y[i])))
Error in plot.default(yh, r, xlab = l.fit, ylab = "Residuals", main = main,  : 
  formal argument "xlab" matched by multiple actual arguments

Also do you know what does the following mean? Any example for illustrating this (or external link)? enter image description here

Beef data is like the following: enter image description here

Here's the beef data.frame:

   YEAR  PBE  CBE  PPO  CPO  PFO DINC  CFO RDINC RFP
1  1925 59.7 58.6 60.5 65.8 65.8 51.4 90.9  68.5 877
2  1926 59.7 59.4 63.3 63.3 68.0 52.6 92.1  69.6 899
3  1927 63.0 53.7 59.9 66.8 65.5 52.1 90.9  70.2 883
4  1928 71.0 48.1 56.3 69.9 64.8 52.7 90.9  71.9 884
5  1929 71.0 49.0 55.0 68.7 65.6 55.1 91.1  75.2 895
6  1930 74.2 48.2 59.6 66.1 62.4 48.8 90.7  68.3 874
7  1931 72.1 47.9 57.0 67.4 51.4 41.5 90.0  64.0 791
8  1932 79.0 46.0 49.5 69.7 42.8 31.4 87.8  53.9 733
9  1933 73.1 50.8 47.3 68.7 41.6 29.4 88.0  53.2 752
10 1934 70.2 55.2 56.6 62.2 46.4 33.2 89.1  58.0 811
11 1935 82.2 52.2 73.9 47.7 49.7 37.0 87.3  63.2 847
12 1936 68.4 57.3 64.4 54.4 50.1 41.8 90.5  70.5 845
13 1937 73.0 54.4 62.2 55.0 52.1 44.5 90.4  72.5 849
14 1938 70.2 53.6 59.9 57.4 48.4 40.8 90.6  67.8 803
15 1939 67.8 53.9 51.0 63.9 47.1 43.5 93.8  73.2 793
16 1940 63.4 54.2 41.5 72.4 47.8 46.5 95.5  77.6 798
17 1941 56.0 60.0 43.9 67.4 52.2 56.3 97.5  89.5 830

Upvotes: 1

Views: 1887

Answers (2)

lukeA
lukeA

Reputation: 54237

An example illustrating this using the mtcars data:

fit <- lm(mpg ~ ., data=mtcars)
plot(x=fitted(fit), y=residuals(fit))

and

par(mfrow=c(3,4)) # or 'layout(matrix(1:12, nrow=3, byrow=TRUE))'
for (coeff in colnames(mtcars)[-1])
  plot(x=mtcars[, coeff], residuals(fit),  xlab=coeff, ylab=expression(e[i]))

Upvotes: 1

MYaseen208
MYaseen208

Reputation: 23898

Use plot(beeflm, which=1) to get the plot between residuals and fitted values.

require(graphics)

## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
plot(lm.D9, which=1)

Edited

You can use matplot as given below:

matplot(
  x = lm.D9$fitted.values
, y = lm.D9$resid
)

Upvotes: 2

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