Exam7.6.2.1: Example 7.6.2.1 from Generalized Linear Mixed Models: Modern...

In StroupGLMM: R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup

Example 7.6.2.1 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup (p-231)

Description

Exam7.6.2.1 Nonlinear Mean Models ( Quantitative by quantitative models)

Author(s)

1. Muhammad Yaseen ([e-mail])

2. Adeela Munawar ([e-mail])

References

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

@seealso DataSet7.6

Examples

library(scatterplot3d)
data(DataSet7.6)

library(dplyr)
library(magrittr)

DataSet7.6 <-
   DataSet7.6 %>%
   mutate(
     logx1 = ifelse(test = x1 == 0, yes = log(x1 + 0.1), no = log(x1))
   , logx2 = ifelse(test = x2 == 0, yes = log(x2 + 0.1), no = log(x2))
   )
DataSet7.6
Exam7.6.2.1.lm <- lm(formula = response ~ x1*x2 + logx1*logx2 , data = DataSet7.6)
summary(Exam7.6.2.1.lm)
library(parameters)
model_parameters(Exam7.6.2.1.lm)

##---3D Scatter plot ( page#232)
attach(DataSet7.6)
(
  ScatterPlot1 <-
   scatterplot3d(
             x           = x1
           , y           = x2
           , z           = response
           , color      = response
           , main        = " 3D Scatter plot of response")
  )

##--- scatter plot with regression plane by using Hoerl function ( page#233)
grid.lines <-  5
x1.pred <- seq(min(x1), max(x1), length.out = grid.lines)
x2.pred <- seq(min(x2), max(x2), length.out = grid.lines)
x1x2    <- expand.grid( x = x1.pred, y = x2.pred)

z.pred  <- matrix(data = predict(Exam7.6.2.1.lm, newdata = x1x2),
                  nrow = grid.lines
                , ncol = grid.lines)
(ScatterPlot2 <-
   scatterplot3d(
             x           = x1
           , y           = x2
           , z           = response
           , pch         = 20
           , phi         = 25
           , theta       = 30
           , ticktype   = "detailed"
           , xlab       =  "x1"
           , ylab       =  "x2"
           , zlab       = "response"
           , add         = FALSE
           , surf        = list(x      = x1.pred ,
                                y      = x2.pred ,
                                z      = z.pred  ,
                                facets = NA
                                )
           , plot        = TRUE
           , main        = "Fitted Response Surface by Hoerl Function"
           )
           )

StroupGLMM documentation built on Oct. 2, 2024, 1:07 a.m.