Skip to contents

Example 2.B.1 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-53)

Source: R/Exam2.B.1.R
Exam2.B.1.Rd

Exam2.B.1 is used to visualize the effect of lm model statement with Gaussian data and their design matrix

References

  1. Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.

See also

Table1.1

Author

  1. Muhammad Yaseen (myaseen208@gmail.com)

  2. Adeela Munawar (adeela.uaf@gmail.com)

Examples

#-----------------------------------------------------------------------------------
## Linear Model  discussed in Example 2.B.1 using simple regression data of Table1.1
#-----------------------------------------------------------------------------------

data(Table1.1)

Exam2.B.1.lm1 <- lm(formula = y~x, data = Table1.1)
summary(Exam2.B.1.lm1)
#> 
#> Call:
#> lm(formula = y ~ x, data = Table1.1)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -4.0909 -0.9727 -0.2182  0.8364  4.3455 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  -1.2727     1.4066  -0.905 0.389136    
#> x             1.4364     0.2378   6.041 0.000193 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 2.494 on 9 degrees of freedom
#> Multiple R-squared:  0.8022,	Adjusted R-squared:  0.7802 
#> F-statistic:  36.5 on 1 and 9 DF,  p-value: 0.0001925
#> 
library(parameters)
model_parameters(Exam2.B.1.lm1)
#> Parameter   | Coefficient |   SE |        95% CI |  t(9) |      p
#> -----------------------------------------------------------------
#> (Intercept) |       -1.27 | 1.41 | [-4.45, 1.91] | -0.90 | 0.389 
#> x           |        1.44 | 0.24 | [ 0.90, 1.97] |  6.04 | < .001
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald t-distribution approximation.

DesignMatrix.lm1 <- model.matrix (object = Exam2.B.1.lm1)
DesignMatrix.lm1
#>    (Intercept)  x
#> 1            1  0
#> 2            1  1
#> 3            1  2
#> 4            1  3
#> 5            1  4
#> 6            1  5
#> 7            1  6
#> 8            1  7
#> 9            1  8
#> 10           1  9
#> 11           1 10
#> attr(,"assign")
#> [1] 0 1