Last updated on 2025-04-06 01:51:21 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.23 | 30.92 | 147.63 | 178.55 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.23 | 20.80 | 93.66 | 114.46 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.23 | 319.41 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.23 | 280.67 | ERROR | |||
| r-devel-macos-arm64 | 1.23 | 73.00 | OK | |||
| r-devel-macos-x86_64 | 1.23 | 167.00 | OK | |||
| r-devel-windows-x86_64 | 1.23 | 30.00 | 167.00 | 197.00 | OK | |
| r-patched-linux-x86_64 | 1.23 | 29.55 | 158.02 | 187.57 | OK | |
| r-release-linux-x86_64 | 1.23 | 32.15 | 155.32 | 187.47 | NOTE | |
| r-release-macos-arm64 | 1.23 | 74.00 | NOTE | |||
| r-release-macos-x86_64 | 1.23 | 183.00 | NOTE | |||
| r-release-windows-x86_64 | 1.23 | 30.00 | 160.00 | 190.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.23 | 70.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.23 | 193.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.23 | 39.00 | 197.00 | 236.00 | NOTE |
Version: 1.23
Check: examples
Result: ERROR
Running examples in ‘cgam-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: cgam
> ### Title: Constrained Generalized Additive Model Fitting
> ### Aliases: cgam
> ### Keywords: cgam routine
>
> ### ** Examples
>
> # Example 1.
> data(cubic)
> # extract x
> x <- cubic$x
>
> # extract y
> y <- cubic$y
>
> # regress y on x with no restriction with lm()
> fit.lm <- lm(y ~ x + I(x^2) + I(x^3))
>
> # regress y on x under the restriction: "increasing and convex"
> fit.cgam <- cgam(y ~ incr.conv(x))
>
> # make a plot to compare the two fits
> par(mar = c(4, 4, 1, 1))
> plot(x, y, cex = .7, xlab = "x", ylab = "y")
> lines(x, fit.cgam$muhat, col = 2, lty = 2)
> lines(x, fitted(fit.lm), col = 1, lty = 1)
> legend("topleft", bty = "n", c("constrained cgam fit", "unconstrained lm fit"),
+ lty = c(2, 1), col = c(2, 1))
>
> # Example 2.
> ## Not run:
> ##D library(gam)
> ##D data(kyphosis)
> ##D
> ##D # regress Kyphosis on Age, Number, and Start under the restrictions:
> ##D # "concave", "increasing and concave", and "decreasing and concave"
> ##D fit <- cgam(Kyphosis ~ conc(Age) + incr.conc(Number) + decr.conc(Start),
> ##D family = binomial(), data = kyphosis)
> ## End(Not run)
>
> # Example 3.
> library(MASS)
> data(Rubber)
>
> # regress loss on hard and tens under the restrictions:
> # "decreasing" and "decreasing"
> fit.cgam <- cgam(loss ~ decr(hard) + decr(tens), data = Rubber)
> # "smooth and decreasing" and "smooth and decreasing"
> fit.cgam.s <- cgam(loss ~ s.decr(hard) + s.decr(tens), data = Rubber)
> summary(fit.cgam.s)
Call:
cgam(formula = loss ~ s.decr(hard) + s.decr(tens), data = Rubber)
Coefficients:
Estimate StdErr t.value p.value
(Intercept) 175.4333 5.9624 29.423 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 606.8062)
Null deviance: 225011.4 on 29 degrees of freedom
Residual deviance: 17597.38 on 16.5 observed degrees of freedom
Approximate significance of constrained components:
edf mixture.of.Beta p.value
s.decr(hard) 7.5 0.8772 < 2.2e-16 ***
s.decr(tens) 4.5 0.8166 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
CIC: 10.0851> anova(fit.cgam.s)
Family: gaussian
Link function: identity
Formula:
cgam(formula = loss ~ s.decr(hard) + s.decr(tens), data = Rubber)
Approximate significance of smooth terms:
edf mixture.of.Beta p.value
s.decr(hard) 7.5 0.8772 < 2.2e-16 ***
s.decr(tens) 4.5 0.8166 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> # make a 3D plot based on fit.cgam and fit.cgam.s
> plotpersp(fit.cgam, th = 120, main = "3D Plot of a Cgam Fit")
> plotpersp(fit.cgam.s, tens, hard, data = Rubber, th = 120, main = "3D Plot of a Smooth Cgam Fit")
>
> # Example 4. monotonic variance estimation
> n <- 400
> x <- runif(n)
> sig <- .1 + exp(15*x-8)/(1+exp(15*x-8))
> e <- rnorm(n)
> mu <- 10*x^2
> y <- mu + sig*e
>
> fit <- cgam(y ~ s.incr.conv(x), var.est = s.incr(x))
> est.var <- fit$vh
> muhat <- fit$muhat
>
> par(mfrow = c(1, 2))
> plot(x, y)
> points(sort(x), muhat[order(x)], type = "l", lwd = 2, col = 2)
> lines(sort(x), (mu)[order(x)], col = 4)
>
> plot(sort(x), est.var[order(x)], col=2, lwd=2, type="l",
+ lty=2, ylab="Variance", ylim=c(0, max(c(est.var, sig^2))))
> points(sort(x), (sig^2)[order(x)], col=1, lwd=2, type="l")
>
> # Example 5. monotonic variance estimation with the lidar data set in SemiPar
> library(SemiPar)
> data(lidar)
>
> fit <- cgam(logratio ~ s.decr(range), var.est=s.incr(range), data=lidar)
Error in attr(x, "nm") <- deparse(pars$x) :
cannot set attribute on a 'builtin'
Calls: cgam -> cgam.fit -> s.incr
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 1.23
Check: examples
Result: ERROR
Running examples in ‘cgam-Ex.R’ failed
The error most likely occurred in:
> ### Name: cgam
> ### Title: Constrained Generalized Additive Model Fitting
> ### Aliases: cgam
> ### Keywords: cgam routine
>
> ### ** Examples
>
> # Example 1.
> data(cubic)
> # extract x
> x <- cubic$x
>
> # extract y
> y <- cubic$y
>
> # regress y on x with no restriction with lm()
> fit.lm <- lm(y ~ x + I(x^2) + I(x^3))
>
> # regress y on x under the restriction: "increasing and convex"
> fit.cgam <- cgam(y ~ incr.conv(x))
>
> # make a plot to compare the two fits
> par(mar = c(4, 4, 1, 1))
> plot(x, y, cex = .7, xlab = "x", ylab = "y")
> lines(x, fit.cgam$muhat, col = 2, lty = 2)
> lines(x, fitted(fit.lm), col = 1, lty = 1)
> legend("topleft", bty = "n", c("constrained cgam fit", "unconstrained lm fit"),
+ lty = c(2, 1), col = c(2, 1))
>
> # Example 2.
> ## Not run:
> ##D library(gam)
> ##D data(kyphosis)
> ##D
> ##D # regress Kyphosis on Age, Number, and Start under the restrictions:
> ##D # "concave", "increasing and concave", and "decreasing and concave"
> ##D fit <- cgam(Kyphosis ~ conc(Age) + incr.conc(Number) + decr.conc(Start),
> ##D family = binomial(), data = kyphosis)
> ## End(Not run)
>
> # Example 3.
> library(MASS)
> data(Rubber)
>
> # regress loss on hard and tens under the restrictions:
> # "decreasing" and "decreasing"
> fit.cgam <- cgam(loss ~ decr(hard) + decr(tens), data = Rubber)
> # "smooth and decreasing" and "smooth and decreasing"
> fit.cgam.s <- cgam(loss ~ s.decr(hard) + s.decr(tens), data = Rubber)
> summary(fit.cgam.s)
Call:
cgam(formula = loss ~ s.decr(hard) + s.decr(tens), data = Rubber)
Coefficients:
Estimate StdErr t.value p.value
(Intercept) 175.4333 5.9624 29.423 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 606.8062)
Null deviance: 225011.4 on 29 degrees of freedom
Residual deviance: 17597.38 on 16.5 observed degrees of freedom
Approximate significance of constrained components:
edf mixture.of.Beta p.value
s.decr(hard) 7.5 0.8772 < 2.2e-16 ***
s.decr(tens) 4.5 0.8166 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
CIC: 10.0851> anova(fit.cgam.s)
Family: gaussian
Link function: identity
Formula:
cgam(formula = loss ~ s.decr(hard) + s.decr(tens), data = Rubber)
Approximate significance of smooth terms:
edf mixture.of.Beta p.value
s.decr(hard) 7.5 0.8772 < 2.2e-16 ***
s.decr(tens) 4.5 0.8166 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> # make a 3D plot based on fit.cgam and fit.cgam.s
> plotpersp(fit.cgam, th = 120, main = "3D Plot of a Cgam Fit")
> plotpersp(fit.cgam.s, tens, hard, data = Rubber, th = 120, main = "3D Plot of a Smooth Cgam Fit")
>
> # Example 4. monotonic variance estimation
> n <- 400
> x <- runif(n)
> sig <- .1 + exp(15*x-8)/(1+exp(15*x-8))
> e <- rnorm(n)
> mu <- 10*x^2
> y <- mu + sig*e
>
> fit <- cgam(y ~ s.incr.conv(x), var.est = s.incr(x))
> est.var <- fit$vh
> muhat <- fit$muhat
>
> par(mfrow = c(1, 2))
> plot(x, y)
> points(sort(x), muhat[order(x)], type = "l", lwd = 2, col = 2)
> lines(sort(x), (mu)[order(x)], col = 4)
>
> plot(sort(x), est.var[order(x)], col=2, lwd=2, type="l",
+ lty=2, ylab="Variance", ylim=c(0, max(c(est.var, sig^2))))
> points(sort(x), (sig^2)[order(x)], col=1, lwd=2, type="l")
>
> # Example 5. monotonic variance estimation with the lidar data set in SemiPar
> library(SemiPar)
> data(lidar)
>
> fit <- cgam(logratio ~ s.decr(range), var.est=s.incr(range), data=lidar)
Error in attr(x, "nm") <- deparse(pars$x) :
cannot set attribute on a 'builtin'
Calls: cgam -> cgam.fit -> s.incr
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.23
Check: package dependencies
Result: NOTE
Depends: includes the non-default packages:
'coneproj', 'svDialogs', 'statmod', 'lme4', 'Matrix', 'splines2'
Adding so many packages to the search path is excessive and importing
selectively is preferable.
Flavors: r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64