R Under development (unstable) (2022-03-19 r81942) -- "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
> 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.
>
> # termplot.R
> library(MASS)
> hills.lm <- lm(log(time) ~ log(climb)+log(dist), data = hills)
> termplot(hills.lm, partial.resid = TRUE, smooth = panel.smooth,
+ terms = "log(dist)", main = "Original")
> termplot(hills.lm, transform.x = TRUE,
+ partial.resid = TRUE, smooth = panel.smooth,
+ terms = "log(dist)", main = "Transformed")
>
> # xtabs.Rd
> if(require("Matrix")) {
+ ## 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__>
>