AvgDailyGain: Average daily weight gain of steers on different diets

In SASmixed: Data sets from "SAS System for Mixed Models"

Description

The AvgDailyGain data frame has 32 rows and 6 columns.

Format

This data frame contains the following columns:

Id

the animal number

Block

an ordered factor indicating the barn in which the steer was housed.

Treatment

an ordered factor with levels 0 < 10 < 20 < 30 indicating the amount of medicated feed additive added to the base ration.

adg

a numeric vector of average daily weight gains over a period of 160 days.

InitWt

a numeric vector giving the initial weight of the animal

Trt

the Treatment as a numeric variable

Source

Littel, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. (1996), SAS System for Mixed Models, SAS Institute (Data Set 5.3).

Examples

str(AvgDailyGain)
if (require("lattice", quietly = TRUE, character = TRUE)) {
  ## plot of adg versus Treatment by Block
  xyplot(adg ~ Treatment | Block, AvgDailyGain, type = c("g", "p", "r"),
         xlab = "Treatment (amount of feed additive)",
         ylab = "Average daily weight gain (lb.)", aspect = "xy",
         index.cond = function(x, y) coef(lm(y ~ x))[1])
}
if (require("lme4", quietly = TRUE, character = TRUE)) {
  options(contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
  ## compare with output 5.1, p. 178
  print(fm1Adg <- lmer(adg ~ InitWt * Treatment - 1 + (1 | Block),
                         AvgDailyGain))
  print(anova(fm1Adg))   # checking significance of terms
  print(fm2Adg <- lmer(adg ~ InitWt + Treatment + (1 | Block),
                         AvgDailyGain))
  print(anova(fm2Adg))
  print(lmer(adg ~ InitWt + Treatment - 1 + (1 | Block), AvgDailyGain))
}

Example output

'data.frame':	32 obs. of  6 variables:
 $ Id       : num  1 2 3 4 5 6 7 8 9 10 ...
 $ Block    : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 2 2 2 2 3 3 ...
 $ Treatment: Factor w/ 4 levels "0","10","20",..: 1 2 3 4 1 2 3 4 1 2 ...
 $ adg      : num  1.03 1.54 1.82 1.86 1.31 2.16 2.13 2.23 1.59 2.53 ...
 $ InitWt   : num  338 477 444 370 403 451 450 393 394 499 ...
 $ Trt      : num  0 10 20 30 0 10 20 30 0 10 ...
 - attr(*, "ginfo")=List of 7
  ..$ formula     :Class 'formula'  language adg ~ Trt | Block
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  ..$ order.groups: logi TRUE
  ..$ FUN         :function (x)  
  ..$ outer       : NULL
  ..$ inner       : NULL
  ..$ labels      :List of 2
  .. ..$ Trt: chr "Level of medicated feed additive in diet"
  .. ..$ adg: chr "Average Daily Gain of steers fed for 160 days"
  ..$ units       : list()
Linear mixed model fit by REML ['lmerMod']
Formula: adg ~ InitWt * Treatment - 1 + (1 | Block)
   Data: AvgDailyGain
REML criterion at convergence: 65.3268
Random effects:
 Groups   Name        Std.Dev.
 Block    (Intercept) 0.5092  
 Residual             0.2223  
Number of obs: 32, groups:  Block, 8
Fixed Effects:
            InitWt          Treatment0         Treatment10         Treatment20  
          0.004448            0.439137            1.426119            0.479629  
       Treatment30   InitWt:Treatment0  InitWt:Treatment10  InitWt:Treatment20  
          0.200107           -0.002154           -0.003365           -0.001082  
Analysis of Variance Table
                 Df Sum Sq Mean Sq F value
InitWt            1 4.5318  4.5318 91.6823
Treatment         4 1.7425  0.4356  8.8131
InitWt:Treatment  3 0.1381  0.0460  0.9312
Linear mixed model fit by REML ['lmerMod']
Formula: adg ~ InitWt + Treatment + (1 | Block)
   Data: AvgDailyGain
REML criterion at convergence: 36.3373
Random effects:
 Groups   Name        Std.Dev.
 Block    (Intercept) 0.4908  
 Residual             0.2238  
Number of obs: 32, groups:  Block, 8
Fixed Effects:
(Intercept)       InitWt   Treatment0  Treatment10  Treatment20  
    0.80111      0.00278     -0.55207     -0.06857     -0.08813  
Analysis of Variance Table
          Df  Sum Sq Mean Sq F value
InitWt     1 0.51455 0.51455  10.275
Treatment  3 1.52670 0.50890  10.162
Linear mixed model fit by REML ['lmerMod']
Formula: adg ~ InitWt + Treatment - 1 + (1 | Block)
   Data: AvgDailyGain
REML criterion at convergence: 36.3373
Random effects:
 Groups   Name        Std.Dev.
 Block    (Intercept) 0.4908  
 Residual             0.2238  
Number of obs: 32, groups:  Block, 8
Fixed Effects:
     InitWt   Treatment0  Treatment10  Treatment20  Treatment30  
    0.00278      0.24903      0.73254      0.71298      0.80111  

SASmixed documentation built on May 2, 2019, 4:47 p.m.