Fit linear model in R for various values - r

Question

In this experiment, four different diets were tried on animals. Then researchers measured their effects on blood coagulation time.
## Data :
coag diet
1 62 A
2 60 A
3 63 A
4 59 A
5 63 B
6 67 B
7 71 B
8 64 B
9 65 B
10 66 B
11 68 C
12 66 C
13 71 C
14 67 C
15 68 C
16 68 C
17 56 D
18 62 D
19 60 D
20 61 D
21 63 D
22 64 D
23 63 D
24 59 D
I am trying to fit a linear model for coag~diet by using the function lm in R
Results should look like the following:
> modelSummary$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.100000e+01 1.183216 5.155441e+01 9.547815e-23
dietB 5.000000e+00 1.527525 3.273268e+00 3.802505e-03
dietC 7.000000e+00 1.527525 4.582576e+00 1.805132e-04
dietD -1.071287e-14 1.449138 -7.392579e-15 1.000000e+00
My code thus far does not look like results:
coagulation$x1 <- 1*(coagulation$diet=="B")
coagulation$x2 <- 1*(coagulation$diet=="C")
coagulation$x3 <- 1*(coagulation$diet=="D")
modelSummary <- lm(coag~1+x1+x2+x3, data=coagulation)

Answer 1

"diet" is a character variable and is treated as a factor. So you may leave out the dummy coding and just do:
summary(lm(coag ~ diet, data=coagulation))$coefficients
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 6.100000e+01 1.183216 5.155441e+01 9.547815e-23
# dietB 5.000000e+00 1.527525 3.273268e+00 3.802505e-03
# dietC 7.000000e+00 1.527525 4.582576e+00 1.805132e-04
# dietD 2.991428e-15 1.449138 2.064281e-15 1.000000e+00
Even if "diet" were a numeric variable and you want R to treat it as a categorical rather than a continuous variable no dummy coding is needed, you would just add it as + factor(diet) into the formula.
As you see, also 1 + is redundant since lm calculates the (Intercept) by default. To omit the intercept, you may do 0 + (or - 1).

Answer 2

That presentation is a property of summary(modelSummary) (class summary.lm), not modelSummary (class lm).
summary(modelSummary)$coefficients
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 6.100000e+01 1.183216 5.155441e+01 9.547815e-23
# x1 5.000000e+00 1.527525 3.273268e+00 3.802505e-03
# x2 7.000000e+00 1.527525 4.582576e+00 1.805132e-04
# x3 2.991428e-15 1.449138 2.064281e-15 1.000000e+00

Answer 3

You may also consider coding diet in this manner
coagulation$diet <- factor(coagulation$diet)
modelSummary<-lm(coag~diet,coagulation)
summary(modelSummary)
Call:
lm(formula = coag ~ diet, data = coagulation)
Residuals:
Min 1Q Median 3Q Max
-5.00 -1.25 0.00 1.25 5.00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.100e+01 1.183e+00 51.554 < 2e-16 ***
dietB 5.000e+00 1.528e+00 3.273 0.003803 **
dietC 7.000e+00 1.528e+00 4.583 0.000181 ***
dietD 2.991e-15 1.449e+00 0.000 1.000000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1