Recovery Time Data Set — recovery • multcomp

Recovery time after surgery.

Usage

data("recovery")

Format

This data frame contains the following variables

blanket: blanket type, a factor at four levels: b0, b1, b2, and b3.
minutes: response variable: recovery time after a surgical procedure.

Details

A company developed specialized heating blankets designed to help the body heat following a surgical procedure. Four types of blankets were tried on surgical patients with the aim of comparing the recovery time of patients. One of the blanket was a standard blanket that had been in use already in various hospitals. Data were published by multcomp::westfall1999, page 66.

References

Examples


  ### set up one-way ANOVA
  amod <- aov(minutes ~ blanket, data = recovery)

  ### set up multiple comparisons: one-sided Dunnett contrasts
  rht <- glht(amod, linfct = mcp(blanket = "Dunnett"), 
              alternative = "less")

  ### cf. Westfall et al. (1999, p. 80)
  confint(rht, level = 0.9)
#> 
#> 	 Simultaneous Confidence Intervals
#> 
#> Multiple Comparisons of Means: Dunnett Contrasts
#> 
#> 
#> Fit: aov(formula = minutes ~ blanket, data = recovery)
#> 
#> Quantile = 1.8434
#> 90% family-wise confidence level
#>  
#> 
#> Linear Hypotheses:
#>              Estimate lwr     upr    
#> b1 - b0 >= 0 -2.1333     -Inf  0.8230
#> b2 - b0 >= 0 -7.4667     -Inf -4.5103
#> b3 - b0 >= 0 -1.6667     -Inf -0.0357
#> 

  ### the same
  rht <- glht(amod, linfct = mcp(blanket = c("b1 - b0 >= 0", 
                                             "b2 - b0 >= 0", 
                                             "b3 - b0 >= 0")))
  confint(rht, level = 0.9)
#> 
#> 	 Simultaneous Confidence Intervals
#> 
#> Multiple Comparisons of Means: User-defined Contrasts
#> 
#> 
#> Fit: aov(formula = minutes ~ blanket, data = recovery)
#> 
#> Quantile = 1.843
#> 90% family-wise confidence level
#>  
#> 
#> Linear Hypotheses:
#>              Estimate lwr      upr     
#> b1 - b0 >= 0 -2.13333     -Inf  0.82247
#> b2 - b0 >= 0 -7.46667     -Inf -4.51086
#> b3 - b0 >= 0 -1.66667     -Inf -0.03602
#>