Running examples in ‘uwIntroStats-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: regress
> ### Title: General Regression for an Arbitrary Functional
> ### Aliases: regress fitted.uRegress print.augCoefficients print.uRegress
> ###   uLRtest uWaldtest termTraverse explode indentNames getLevels testList
> ###   pasteTwo processTerm addArgs pasteOn pasteOnSpline pastePair
> ###   movingSum myNext reFormatReg createCols checkNesting splitOnParen
> ###   reFormat equal
> ### Keywords: ~kwd1 ~kwd2
> 
> ### ** Examples
> 
> # Loading required libraries
> library(survival)
> library(sandwich)
> 
> # Reading in a dataset
> mri <- read.table("http://www.emersonstatistics.com/datasets/mri.txt",header=TRUE)
> 
> # Creating a Surv object to reflect time to death
> mri$ttodth <- Surv(mri$obstime,mri$death)
> 
> # Attaching the mri dataset
> attach(mri)
> 
> # Linear regression of atrophy on age
> regress("mean", atrophy~age, data=mri)

Call:
regress(fnctl = "mean", formula = atrophy ~ age, data = mri)

Residuals:
    Min      1Q  Median      3Q     Max 
-36.870  -8.589  -0.870   7.666  51.203 

Coefficients:
                 Estimate  Naive SE  Robust SE    95%L      95%H     
[1] Intercept     -16.06     6.256     6.701       -29.22    -2.907  
[2] age            0.6980   0.08368   0.09002       0.5213    0.8747 
                    F stat    df Pr(>F)   
[1] Intercept            5.75 1    0.0168 
[2] age                 60.12 1  < 0.00005

Residual standard error: 12.36 on 733 degrees of freedom
Multiple R-squared:  0.08669,	Adjusted R-squared:  0.08545 
F-statistic: 60.12 on 1 and 733 DF,  p-value: 2.988e-14

> 
> ## Linear regression of atrophy on male and race and their interaction, 
> ## with a multiple-partial F-test on the race-age interaction
> regress("mean", atrophy~ male + U(ra=~race*age), data=mri)

Call:
regress(fnctl = "mean", formula = atrophy ~ male + U(ra = ~race * 
    age), data = mri)

Residuals:
    Min      1Q  Median      3Q     Max 
-33.438  -8.368  -0.462   7.372  53.253 

Coefficients:
                  Estimate  Naive SE  Robust SE    95%L      95%H     
[1] Intercept       2.199     13.24     14.53       -26.33     30.73  
[2] male            6.003     0.8850    0.8815       4.273     7.734  
    ra                                                                
[3]   race         -15.30     8.902     9.660       -34.26     3.664  
[4]   age           0.4255    0.1770    0.1943      0.04399    0.8070 
[5]   race:age      0.1954    0.1186    0.1287     -0.05726    0.4481 
                     F stat    df Pr(>F)   
[1] Intercept             0.02 1    0.8798 
[2] male                 46.37 1  < 0.00005
    ra                   21.51 3  < 0.00005
[3]   race                2.51 1    0.1137 
[4]   age                 4.79 1    0.0289 
[5]   race:age            2.31 1    0.1293 

Residual standard error: 11.99 on 730 degrees of freedom
Multiple R-squared:  0.1443,	Adjusted R-squared:  0.1397 
F-statistic: 27.23 on 4 and 730 DF,  p-value: < 2.2e-16

> 
> ## Linear regression of atrophy on age, male, race (as a dummy variable), chf,
> ## and diabetes. There are two multiple partial F-tests and both are named
> regress("mean", atrophy~age+male+U(rc=~dummy(race)+chf)+U(md=~male+diabetes), data=mri)

Call:
regress(fnctl = "mean", formula = atrophy ~ age + male + U(rc = ~dummy(race) + 
    chf) + U(md = ~male + diabetes), data = mri)

Residuals:
    Min      1Q  Median      3Q     Max 
-34.003  -8.611  -0.572   7.279  54.064 

Coefficients:
                      Estimate  Naive SE  Robust SE    95%L      95%H     
[1]  Intercept         -17.95     6.088     6.593       -30.90    -5.009  
[2]  age                0.6862   0.08142   0.08840       0.5127    0.8598 
[3]  male               5.879     0.8952    0.8942       4.123     7.635  
     rc                                                                   
       dummy(race)                                                        
[4]       race.2       -2.147     1.285     1.255       -4.611     0.3176 
[5]       race.3        0.1829    1.826     1.796       -3.343     3.709  
[6]       race.4       -2.734     3.508     3.798       -10.19     4.722  
[7]    chf              1.919     1.943     2.179       -2.358     6.197  
     md                                                                   
       male             5.879     0.8952    0.8942       4.123     7.635  
[8]    diabetes         0.6524    1.450     1.267       -1.836     3.141  
                         F stat    df Pr(>F)   
[1]  Intercept                7.41 1    0.0066 
[2]  age                     60.25 1  < 0.00005
[3]  male                    43.23 1  < 0.00005
     rc                       1.09 4    0.3598 
       dummy(race)            1.14 3    0.3336 
[4]       race.2              2.92 1    0.0877 
[5]       race.3              0.01 1    0.9189 
[6]       race.4              0.52 1    0.4718 
[7]    chf                    0.78 1    0.3787 
     md                      22.84 2  < 0.00005
       male                  43.23 1  < 0.00005
[8]    diabetes               0.26 1    0.6069 

 Dummy terms calculated from race, reference = 1 

Residual standard error: 12 on 727 degrees of freedom
Multiple R-squared:  0.1453,	Adjusted R-squared:  0.1371 
F-statistic: 15.31 on 7 and 727 DF,  p-value: < 2.2e-16

> 
> ## Proportional hazards regression clustered on id 
> ## (here it makes no difference because the ids are unique)
> regress("hazard", ttodth~ldl, id=ptid, data=mri)
Warning in coxph(formula, data = data, ...) :
  a variable appears on both the left and right sides of the formula
Error in eval(predvars, data, env) : 
  invalid 'envir' argument of type 'closure'
Calls: regress ... eval -> eval -> <Anonymous> -> model.frame.default -> eval
Execution halted