R Under development (unstable) (2024-08-27 r87063) -- "Unsuffered Consequences"
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> ## For examples skipped in testing because they need recommended packages.
> 
> ## This is skipped entirely on a Unix-alike if recommended packages are,
> ## so for Windows
> if(!require("MASS")) q()
Loading required package: MASS
> 
> pdf("reg-examples-3.pdf", encoding = "ISOLatin1.enc")
> 
> ## From datasets
> if(require("survival")) {
+   model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
+   print(summary(model3))
+   detach("package:survival", unload = TRUE)  # survival (conflicts)
+ }
Loading required package: survival
Call:
coxph(formula = Surv(rep(1, 248L), case) ~ spontaneous + induced + 
    strata(stratum), data = infert, method = "exact")

  n= 248, number of events= 83 

              coef exp(coef) se(coef)     z Pr(>|z|)    
spontaneous 1.9859    7.2854   0.3524 5.635 1.75e-08 ***
induced     1.4090    4.0919   0.3607 3.906 9.38e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

            exp(coef) exp(-coef) lower .95 upper .95
spontaneous     7.285     0.1373     3.651    14.536
induced         4.092     0.2444     2.018     8.298

Concordance= 0.776  (se = 0.044 )
Likelihood ratio test= 53.15  on 2 df,   p=3e-12
Wald test            = 31.84  on 2 df,   p=1e-07
Score (logrank) test = 48.44  on 2 df,   p=3e-11

> 
> 
> ## From grDevices
> x1  <- matrix(rnorm(1e3), ncol = 2)
> x2  <- matrix(rnorm(1e3, mean = 3, sd = 1.5), ncol = 2)
> x   <- rbind(x1, x2)
> 
> dcols <- densCols(x)
> graphics::plot(x, col = dcols, pch = 20, main = "n = 1000")
> 
> 
> ## From graphics:
> ## A largish data set
> set.seed(123)
> n <- 10000
> x1  <- matrix(rnorm(n), ncol = 2)
> x2  <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)
> x   <- rbind(x1, x2)
> 
> oldpar <- par(mfrow = c(2, 2))
> smoothScatter(x, nrpoints = 0)
> smoothScatter(x)
> 
> ## a different color scheme:
> Lab.palette <- colorRampPalette(c("blue", "orange", "red"), space = "Lab")
> smoothScatter(x, colramp = Lab.palette)
> 
> ## somewhat similar, using identical smoothing computations,
> ## but considerably *less* efficient for really large data:
> plot(x, col = densCols(x), pch = 20)
> 
> ## use with pairs:
> par(mfrow = c(1, 1))
> y <- matrix(rnorm(40000), ncol = 4) + 3*rnorm(10000)
> y[, c(2,4)] <-  -y[, c(2,4)]
> pairs(y, panel = function(...) smoothScatter(..., nrpoints = 0, add = TRUE))
> 
> par(oldpar)
> 
> 
> ## From stats
> # alias.Rd
> op <- options(contrasts = c("contr.helmert", "contr.poly"))
> npk.aov <- aov(yield ~ block + N*P*K, npk)
> alias(npk.aov)
Model :
yield ~ block + N * P * K

Complete :
         (Intercept) block1 block2 block3 block4 block5 N1    P1    K1    N1:P1
N1:P1:K1     0           1    1/3    1/6  -3/10   -1/5      0     0     0     0
         N1:K1 P1:K1
N1:P1:K1     0     0

> options(op)  # reset
> 
> # as.hclust.Rd
> if(require("cluster", quietly = TRUE)) {# is a recommended package
+   set.seed(123)
+   x <- matrix(rnorm(30), ncol = 3)
+   hc <- hclust(dist(x), method = "complete")
+   ag <- agnes(x, method = "complete")
+   hcag <- as.hclust(ag)
+   ## The dendrograms order slightly differently:
+   op <- par(mfrow = c(1,2))
+   plot(hc) ;  mtext("hclust", side = 1)
+   plot(hcag); mtext("agnes",  side = 1)
+   detach("package:cluster")
+ }
> 
> # confint.Rd
> counts <- c(18,17,15,20,10,20,25,13,12)
> outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
> glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
> confint(glm.D93)
Waiting for profiling to be done...
                 2.5 %      97.5 %
(Intercept)  2.6958215  3.36655581
outcome2    -0.8577018 -0.06255840
outcome3    -0.6753696  0.08244089
treatment2  -0.3932548  0.39325483
treatment3  -0.3932548  0.39325483
> confint.default(glm.D93)  # based on asymptotic normality}
                 2.5 %      97.5 %
(Intercept)  2.7095672  3.37947764
outcome2    -0.8505027 -0.05800787
outcome3    -0.6707552  0.08478093
treatment2  -0.3919928  0.39199279
treatment3  -0.3919928  0.39199279
> 
> # contrasts.Rd
> utils::example(factor)

factor> (ff <- factor(substring("statistics", 1:10, 1:10), levels = letters))
 [1] s t a t i s t i c s
Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z

factor> as.integer(ff)      # the internal codes
 [1] 19 20  1 20  9 19 20  9  3 19

factor> (f. <- factor(ff))  # drops the levels that do not occur
 [1] s t a t i s t i c s
Levels: a c i s t

factor> ff[, drop = TRUE]   # the same, more transparently
 [1] s t a t i s t i c s
Levels: a c i s t

factor> factor(letters[1:20], labels = "letter")
 [1] letter1  letter2  letter3  letter4  letter5  letter6  letter7  letter8 
 [9] letter9  letter10 letter11 letter12 letter13 letter14 letter15 letter16
[17] letter17 letter18 letter19 letter20
20 Levels: letter1 letter2 letter3 letter4 letter5 letter6 letter7 ... letter20

factor> class(ordered(4:1)) # "ordered", inheriting from "factor"
[1] "ordered" "factor" 

factor> z <- factor(LETTERS[3:1], ordered = TRUE)

factor> ## and "relational" methods work:
factor> stopifnot(sort(z)[c(1,3)] == range(z), min(z) < max(z))

factor> ## Don't show: 
factor> of <- ordered(ff)

factor> stopifnot(identical(range(of, rev(of)), of[3:2]),
factor+ 	  identical(max(of), of[2]))

factor> ## End(Don't show)
factor> 
factor> ## suppose you want "NA" as a level, and to allow missing values.
factor> (x <- factor(c(1, 2, NA), exclude = NULL))
[1] 1    2    <NA>
Levels: 1 2 <NA>

factor> is.na(x)[2] <- TRUE

factor> x  # [1] 1    <NA> <NA>
[1] 1    <NA> <NA>
Levels: 1 2 <NA>

factor> is.na(x)
[1] FALSE  TRUE FALSE

factor> # [1] FALSE  TRUE FALSE
factor> 
factor> ## More rational, since R 3.4.0 :
factor> factor(c(1:2, NA), exclude =  "" ) # keeps <NA> , as
[1] 1    2    <NA>
Levels: 1 2 <NA>

factor> factor(c(1:2, NA), exclude = NULL) # always did
[1] 1    2    <NA>
Levels: 1 2 <NA>

factor> ## exclude = <character>
factor> z # ordered levels 'A < B < C'
[1] C B A
Levels: A < B < C

factor> factor(z, exclude = "C") # does exclude
[1] <NA> B    A   
Levels: A < B

factor> factor(z, exclude = "B") # ditto
[1] C    <NA> A   
Levels: A < C

factor> ## Now, labels maybe duplicated:
factor> ## factor() with duplicated labels allowing to "merge levels"
factor> x <- c("Man", "Male", "Man", "Lady", "Female")

factor> ## Map from 4 different values to only two levels:
factor> (xf <- factor(x, levels = c("Male", "Man" , "Lady",   "Female"),
factor+                  labels = c("Male", "Male", "Female", "Female")))
[1] Male   Male   Male   Female Female
Levels: Male Female

factor> #> [1] Male   Male   Male   Female Female
factor> #> Levels: Male Female
factor> 
factor> ## Using addNA()
factor> Month <- airquality$Month

factor> table(addNA(Month))

   5    6    7    8    9 <NA> 
  31   30   31   31   30    0 

factor> table(addNA(Month, ifany = TRUE))

 5  6  7  8  9 
31 30 31 31 30 
> fff <- ff[, drop = TRUE]  # reduce to 5 levels.
> contrasts(fff) <- contr.sum(5)[, 1:2]; contrasts(fff)
  [,1] [,2]       [,3]       [,4]
a    1    0 -0.2471257  0.2688164
c    0    1 -0.2471257  0.2688164
i    0    0 -0.1498721 -0.8817814
s    0    0  0.8912491  0.0753323
t   -1   -1 -0.2471257  0.2688164
> 
> ## using sparse contrasts: % useful, once model.matrix() works with these :
> ffs <- fff
> if(requireNamespace("Matrix", lib.loc=.Library)) withAutoprint({
+ contrasts(ffs) <- contr.sum(5, sparse = TRUE)[, 1:2]; contrasts(ffs)
+ stopifnot(all.equal(ffs, fff))
+ contrasts(ffs) <- contr.sum(5, sparse = TRUE); contrasts(ffs)
+ })
> contrasts(ffs) <- contr.sum(5, sparse = TRUE)[, 1:2]
> contrasts(ffs)
  [,1] [,2]       [,3]       [,4]
a    1    0 -0.2471257  0.2688164
c    0    1 -0.2471257  0.2688164
i    0    0 -0.1498721 -0.8817814
s    0    0  0.8912491  0.0753323
t   -1   -1 -0.2471257  0.2688164
> stopifnot(all.equal(ffs, fff))
> contrasts(ffs) <- contr.sum(5, sparse = TRUE)
> contrasts(ffs)
5 x 4 sparse Matrix of class "dgCMatrix"
             
a  1  .  .  .
c  .  1  .  .
i  .  .  1  .
s  .  .  .  1
t -1 -1 -1 -1
> 
> # glm.Rd
> utils::data(anorexia, package = "MASS")
> 
> anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
+                 family = gaussian, data = anorexia)
> summary(anorex.1)

Call:
glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, 
    data = anorexia)

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  49.7711    13.3910   3.717 0.000410 ***
Prewt        -0.5655     0.1612  -3.509 0.000803 ***
TreatCont    -4.0971     1.8935  -2.164 0.033999 *  
TreatFT       4.5631     2.1333   2.139 0.036035 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 48.69504)

    Null deviance: 4525.4  on 71  degrees of freedom
Residual deviance: 3311.3  on 68  degrees of freedom
AIC: 489.97

Number of Fisher Scoring iterations: 2

> 
> # logLik.Rd
> utils::data(Orthodont, package = "nlme")
> fm1 <- lm(distance ~ Sex * age, Orthodont)
> logLik(fm1)
'log Lik.' -239.1209 (df=5)
> logLik(fm1, REML = TRUE)
'log Lik.' -241.7796 (df=5)
> 
> # nls.Rd
> od <- options(digits=5)
> ## The muscle dataset in MASS is from an experiment on muscle
> ## contraction on 21 animals.  The observed variables are Strip
> ## (identifier of muscle), Conc (Cacl concentration) and Length
> ## (resulting length of muscle section).
> utils::data(muscle, package = "MASS")
> 
> ## The non linear model considered is
> ##       Length = alpha + beta*exp(-Conc/theta) + error
> ## where theta is constant but alpha and beta may vary with Strip.
> 
> with(muscle, table(Strip)) # 2, 3 or 4 obs per strip
Strip
S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 
  4   4   4   3   3   3   2   2   2   2   3   2   2   2   2   4   4   3   3   3 
S21 
  3 
> 
> ## We first use the plinear algorithm to fit an overall model,
> ## ignoring that alpha and beta might vary with Strip.
> 
> musc.1 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle,
+               start = list(th = 1), algorithm = "plinear")
> ## IGNORE_RDIFF_BEGIN
> summary(musc.1)

Formula: Length ~ cbind(1, exp(-Conc/th))

Parameters:
      Estimate Std. Error t value Pr(>|t|)    
th       0.608      0.115    5.31  1.9e-06 ***
.lin1   28.963      1.230   23.55  < 2e-16 ***
.lin2  -34.227      3.793   -9.02  1.4e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.67 on 57 degrees of freedom

Number of iterations to convergence: 5 
Achieved convergence tolerance: 9.32e-07

> ## IGNORE_RDIFF_END
> 
> ## Then we use nls' indexing feature for parameters in non-linear
> ## models to use the conventional algorithm to fit a model in which
> ## alpha and beta vary with Strip.  The starting values are provided
> ## by the previously fitted model.
> ## Note that with indexed parameters, the starting values must be
> ## given in a list (with names):
> b <- coef(musc.1)
> musc.2 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), muscle,
+               start = list(a = rep(b[2], 21), b = rep(b[3], 21), th = b[1]))
> ## IGNORE_RDIFF_BEGIN
> summary(musc.2)

Formula: Length ~ a[Strip] + b[Strip] * exp(-Conc/th)

Parameters:
    Estimate Std. Error t value Pr(>|t|)    
a1    23.454      0.796   29.46  5.0e-16 ***
a2    28.302      0.793   35.70  < 2e-16 ***
a3    30.801      1.716   17.95  1.7e-12 ***
a4    25.921      3.016    8.60  1.4e-07 ***
a5    23.201      2.891    8.02  3.5e-07 ***
a6    20.120      2.435    8.26  2.3e-07 ***
a7    33.595      1.682   19.98  3.0e-13 ***
a8    39.053      3.753   10.41  8.6e-09 ***
a9    32.137      3.318    9.69  2.5e-08 ***
a10   40.005      3.336   11.99  1.0e-09 ***
a11   36.190      3.109   11.64  1.6e-09 ***
a12   36.911      1.839   20.07  2.8e-13 ***
a13   30.635      1.700   18.02  1.6e-12 ***
a14   34.312      3.495    9.82  2.0e-08 ***
a15   38.395      3.375   11.38  2.3e-09 ***
a16   31.226      0.886   35.26  < 2e-16 ***
a17   31.230      0.821   38.02  < 2e-16 ***
a18   19.998      1.011   19.78  3.6e-13 ***
a19   37.095      1.071   34.65  < 2e-16 ***
a20   32.594      1.121   29.07  6.2e-16 ***
a21   30.376      1.057   28.74  7.5e-16 ***
b1   -27.300      6.873   -3.97  0.00099 ***
b2   -26.270      6.754   -3.89  0.00118 ** 
b3   -30.901      2.270  -13.61  1.4e-10 ***
b4   -32.238      3.810   -8.46  1.7e-07 ***
b5   -29.941      3.773   -7.94  4.1e-07 ***
b6   -20.622      3.647   -5.65  2.9e-05 ***
b7   -19.625      8.085   -2.43  0.02661 *  
b8   -45.780      4.113  -11.13  3.2e-09 ***
b9   -31.345      6.352   -4.93  0.00013 ***
b10  -38.599      3.955   -9.76  2.2e-08 ***
b11  -33.921      3.839   -8.84  9.2e-08 ***
b12  -38.268      8.992   -4.26  0.00053 ***
b13  -22.568      8.194   -2.75  0.01355 *  
b14  -36.167      6.358   -5.69  2.7e-05 ***
b15  -32.952      6.354   -5.19  7.4e-05 ***
b16  -47.207      9.540   -4.95  0.00012 ***
b17  -33.875      7.688   -4.41  0.00039 ***
b18  -15.896      6.222   -2.55  0.02051 *  
b19  -28.969      7.235   -4.00  0.00092 ***
b20  -36.917      8.033   -4.60  0.00026 ***
b21  -26.508      7.012   -3.78  0.00149 ** 
th     0.797      0.127    6.30  8.0e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.11 on 17 degrees of freedom

Number of iterations to convergence: 8 
Achieved convergence tolerance: 2.17e-06

> ## IGNORE_RDIFF_END
> options(od)
> 
> # princomp.Rd
> ## Robust:
> (pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))
Call:
princomp(x = stackloss, covmat = MASS::cov.rob(stackloss))

Standard deviations:
   Comp.1    Comp.2    Comp.3    Comp.4 
7.8322873 4.0077676 1.9114016 0.7624211 

 4  variables and  21 observations.
> 
> # xtabs.Rd
> if(require("Matrix", .Library)) {
+  ## similar to "nlme"s  'ergoStool' :
+  d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)),
+                       Subj = gl(9, 4, 36*4))
+  print(xtabs(~ Type + Subj, data = d.ergo)) # 4 replicates each
+  set.seed(15) # a subset of cases:
+  print(xtabs(~ Type + Subj, data = d.ergo[sample(36, 10), ], sparse = TRUE))
+ 
+  ## Hypothetical two-level setup:
+  inner <- factor(sample(letters[1:25], 100, replace = TRUE))
+  inout <- factor(sample(LETTERS[1:5], 25, replace = TRUE))
+  fr <- data.frame(inner = inner, outer = inout[as.integer(inner)])
+  print(xtabs(~ inner + outer, fr, sparse = TRUE))
+ }
Loading required package: Matrix
    Subj
Type 1 2 3 4 5 6 7 8 9
  T1 4 4 4 4 4 4 4 4 4
  T2 4 4 4 4 4 4 4 4 4
  T3 4 4 4 4 4 4 4 4 4
  T4 4 4 4 4 4 4 4 4 4
4 x 9 sparse Matrix of class "dgCMatrix"
    Subj
Type 1 2 3 4 5 6 7 8 9
  T1 . 1 . . . 1 . . .
  T2 . . . . . . . 1 1
  T3 . . 1 1 1 . 1 1 .
  T4 . 1 . . . . . . .
25 x 4 sparse Matrix of class "dgCMatrix"
     outer
inner A B C E
    a . . . 3
    b . . . 6
    c . . 5 .
    d . 1 . .
    e . . 3 .
    f . . 5 .
    g . . 3 .
    h 6 . . .
    i 8 . . .
    j . 1 . .
    k . . . 6
    l . 2 . .
    m . . . 7
    n . . 3 .
    o . 3 . .
    p . 3 . .
    q . . . 7
    r . . . 2
    s . 6 . .
    t . . . 5
    u 4 . . .
    v . . 3 .
    w . 3 . .
    x . . 3 .
    y 2 . . .
> 
> 
> ## From splines
> library(splines)
> Matrix::drop0(zapsmall(6*splineDesign(knots = 1:40, x = 4:37, sparse = TRUE)))
34 x 36 sparse Matrix of class "dgCMatrix"
                                                                             
 [1,] 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [2,] . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [3,] . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [4,] . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [5,] . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [6,] . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [7,] . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [8,] . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . .
 [9,] . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . .
[10,] . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . .
[11,] . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . .
[12,] . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . .
[13,] . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . .
[14,] . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . .
[15,] . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . .
[16,] . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . .
[17,] . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . .
[18,] . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . .
[19,] . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . .
[20,] . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . .
[21,] . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . .
[22,] . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . .
[23,] . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . .
[24,] . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . .
[25,] . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . .
[26,] . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . .
[27,] . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . .
[28,] . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . .
[29,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . .
[30,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . .
[31,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . .
[32,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . .
[33,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 .
[34,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1
> 
> 
> ## From tools
> library(tools)
> example(standard_package_names, run.donttest = TRUE)

stnd__> str(stPkgs <- standard_package_names())
List of 2
 $ base       : chr [1:14] "base" "tools" "utils" "grDevices" ...
 $ recommended: chr [1:15] "MASS" "lattice" "Matrix" "nlme" ...

stnd__> ## No test: 
stnd__> ## consistency of packageDescription and standard_package_names :
stnd__> (pNms <- unlist(stPkgs, FALSE))
        base1         base2         base3         base4         base5 
       "base"       "tools"       "utils"   "grDevices"    "graphics" 
        base6         base7         base8         base9        base10 
      "stats"    "datasets"     "methods"        "grid"     "splines" 
       base11        base12        base13        base14  recommended1 
     "stats4"       "tcltk"    "compiler"    "parallel"        "MASS" 
 recommended2  recommended3  recommended4  recommended5  recommended6 
    "lattice"      "Matrix"        "nlme"    "survival"        "boot" 
 recommended7  recommended8  recommended9 recommended10 recommended11 
    "cluster"   "codetools"     "foreign"  "KernSmooth"       "rpart" 
recommended12 recommended13 recommended14 recommended15 
      "class"        "nnet"     "spatial"        "mgcv" 

stnd__> (prio <- sapply(as.vector(pNms), packageDescription, fields = "Priority"))
         base         tools         utils     grDevices      graphics 
       "base"        "base"        "base"        "base"        "base" 
        stats      datasets       methods          grid       splines 
       "base"        "base"        "base"        "base"        "base" 
       stats4         tcltk      compiler      parallel          MASS 
       "base"        "base"        "base"        "base" "recommended" 
      lattice        Matrix          nlme      survival          boot 
"recommended" "recommended" "recommended" "recommended" "recommended" 
      cluster     codetools       foreign    KernSmooth         rpart 
"recommended" "recommended" "recommended" "recommended" "recommended" 
        class          nnet       spatial          mgcv 
"recommended" "recommended" "recommended" "recommended" 

stnd__> stopifnot(identical(unname(prio),
stnd__+                     sub("[0-9]+$", '', names(pNms))))

stnd__> ## End(No test)
stnd__> 
stnd__> 
stnd__> 
>