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Using knitR (R + LATEX) in R Studio: A Demo

Pairach Piboonrungroj1,2,

1. Logistics Systems Dynamics Group, Cardiff Business School, Cardiff University, United Kingdom

2. Chiang Mai School of Economics, Chiang Mai University, ThailandEmail: me@pairach.com

1. Show only R source code

1 + 1

2. Show only output

## [1] 2

3. Show both source code and output

1 + 1

## [1] 2

4. Show source code in grey shade but the output

1 + 1

[1] 2

5. Now, testing a linear model

# generating value for x variable from 1 to 100

x

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0 20 40 60 80 100

30000

10000

0

10000

30000

x

y

7. Lets build a linear model by regressing y on x# creating a linear model by regressing y on x as 'lm1' object

lm1 |t|)

## (Intercept) -3457.7 2027.2 -1.71 0.09124 .

## x 132.8 34.9 3.81 0.00024 ***

## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##

## Residual standard error: 10100 on 98 degrees of freedom

## Multiple R-squared: 0.129,Adjusted R-squared: 0.12## F-statistic: 14.5 on 1 and 98 DF, p-value: 0.000242

##

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8. Now we can create a post-hoc plots to check assumptions of regression

# Creating post-hoc plot for lm1

par(mfrow = c(2, 2))

plot(lm1)

2000 4000 8000

30000

0

20000

Fitted values

Residuals

Residuals vs Fitted

2

98

46

2 1 0 1 2

3

1

1

2

3

Theoretical Quantiles

Standardizedresiduals

Normal QQ

2

98

46

2000 4000 80000

.0

0.5

1.0

1.5

Fitted values

S

tandardizedresiduals ScaleLocation

29846

0.00 0.02 0.04

3

1

1

3

Leverage

S

tandardizedresiduals

Cook's distance

Residuals vs Leverage

2

987