Last updated on 2024-12-23 03:49:37 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.2.3 | 1.88 | 25.86 | 27.74 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.2.3 | 1.67 | 20.64 | 22.31 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.2.3 | 48.17 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.2.3 | 45.36 | ERROR | |||
| r-devel-windows-x86_64 | 0.2.3 | 4.00 | 57.00 | 61.00 | NOTE | |
| r-patched-linux-x86_64 | 0.2.3 | 2.53 | 24.77 | 27.30 | ERROR | |
| r-release-linux-x86_64 | 0.2.3 | 1.94 | 25.19 | 27.13 | ERROR | |
| r-release-macos-arm64 | 0.2.3 | 16.00 | NOTE | |||
| r-release-macos-x86_64 | 0.2.3 | 30.00 | NOTE | |||
| r-release-windows-x86_64 | 0.2.3 | 3.00 | 53.00 | 56.00 | NOTE | |
| r-oldrel-macos-arm64 | 0.2.3 | 21.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.2.3 | 46.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.2.3 | 5.00 | 53.00 | 58.00 | ERROR |
Version: 0.2.3
Check: Rd files
Result: NOTE
checkRd: (-1) correct_sign.Rd:23: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) correct_sign.Rd:24: Lost braces in \itemize; \value handles \item{}{} directly
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64
Version: 0.2.3
Check: examples
Result: ERROR
Running examples in ‘rineq-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: contribution
> ### Title: Function to decompose the Relative Concentration Index into its
> ### components
> ### Aliases: contribution
>
> ### ** Examples
>
> data(housing)
>
> ## Linear regression direct decomposition
> fit.lm <- lm(bmi ~ sex + tenure + place + age,data = housing)
>
> # decompose relative concentration index
> contrib.lm <- contribution(fit.lm, housing$income)
> summary(contrib.lm)
Overall CI: 0.121004
95% confidence interval: 0.1181791 0.123829
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 38.2448886 0.0462779 0.0000000
sexmale 70.3033532 0.0850699 0.2241433
tenureirregular -16.8438839 -0.0203818 0.0307882
tenureown_apartment 0.0751962 0.0000910 0.0018771
tenureown_house 1.3489119 0.0016322 0.0136284
tenurerent 6.6988511 0.0081059 0.0517000
placeurban 0.0671404 0.0000812 -0.0123887
age 0.1055426 0.0001277 0.0324067
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.3795336 0.3691540 0.3899131 no
tenureirregular -0.6619993 -0.6980505 -0.6259481 no
tenureown_apartment 0.0484737 0.0260028 0.0709447 no
tenureown_house 0.1197676 0.0926766 0.1468586 no
tenurerent 0.1567869 0.1440879 0.1694859 no
placeurban -0.0065578 -0.0188208 0.0057052 no
age 0.0039409 -0.0000583 0.0079401 no
> plot(contrib.lm, decreasing = FALSE, horiz = TRUE)
>
>
> # GLM: Decomposition based on predicted outcome
> fit.logit <-glm(high.bmi ~ sex + tenure + place + age, data = housing)
>
> contrib.logit <- contribution(fit.logit, housing$income)
> summary(contrib.logit)
Overall CI: 0.2502025
95% confidence interval: 0.2429066 0.2574983
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 0.0000000 0.0000000 0.0000000
sexmale 108.4563232 0.2713604 0.7149839
tenureirregular -17.2872968 -0.0432532 0.0653373
tenureown_apartment 0.1140275 0.0002853 0.0058857
tenureown_house 1.4916426 0.0037321 0.0311614
tenurerent 7.0608518 0.0176664 0.1126780
placeurban 0.0573415 0.0001435 -0.0218778
age 0.1071102 0.0002680 0.0680031
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.3795336 0.3691540 0.3899131 no
tenureirregular -0.6619993 -0.6980505 -0.6259481 no
tenureown_apartment 0.0484737 0.0260028 0.0709447 no
tenureown_house 0.1197676 0.0926766 0.1468586 no
tenurerent 0.1567869 0.1440879 0.1694859 no
placeurban -0.0065578 -0.0188208 0.0057052 no
age 0.0039409 -0.0000583 0.0079401 no
> plot(contrib.logit, decreasing = FALSE,horiz = TRUE)
>
>
> # GLM probit: Decomposition based on predicted outcome
> fit.probit <-glm(high.bmi ~ sex + tenure + place + age, data = housing,
+ family = binomial(link = probit))
>
> # binary, set type to 'CIw'
> contrib.probit <- contribution(fit.probit, housing$income, type = "CIw")
> summary(contrib.probit)
Overall CI: -0.26355
95% confidence interval: -0.2718336 -0.2552664
(based on a corrected value)
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 272.0517695 -0.7169924 0.0000000
sexmale -179.3760257 0.4727455 0.6287769
tenureirregular 25.5830437 -0.0674241 0.0866431
tenureown_apartment -0.1800413 0.0004745 0.0077997
tenureown_house -2.1688445 0.0057160 0.0404237
tenurerent -15.7773222 0.0415811 0.1471374
placeurban -0.1352142 0.0003564 -0.0293442
age 0.0026347 -0.0000069 0.0893297
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.7518494 0.7312877 0.7724110 no
tenureirregular -0.7781818 -0.8205601 -0.7358035 no
tenureown_apartment 0.0608355 0.0326340 0.0890370 no
tenureown_house 0.1414021 0.1094175 0.1733867 no
tenurerent 0.2826007 0.2597114 0.3054901 no
placeurban -0.0121440 -0.0348533 0.0105652 no
age -0.0000777 -0.0001566 0.0000012 no
> plot(contrib.probit, decreasing = FALSE,horiz = TRUE)
>
>
> # Marginal effects probit using package 'mfx': Decomposition based on predicted outcome
> fit.mfx <-mfx::probitmfx(high.bmi ~ sex + tenure + place + age, data = housing)
>
> contrib.mfx <- contribution(fit.mfx, housing$income, type = "CIw")
> summary(contrib.mfx, type="CIw")
Overall CI: 0.6906082
95% confidence interval: 0.6694697 0.7117467
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 22.2833787 0.1538908 0.0000000
sexmale 80.4392036 0.5555197 0.7388710
tenureirregular -12.5529543 -0.0866917 0.1114029
tenureown_apartment 0.0984600 0.0006800 0.0111772
tenureown_house 1.1582469 0.0079989 0.0565688
tenurerent 8.5011533 0.0587097 0.2077477
placeurban 0.0739535 0.0005107 -0.0420559
age -0.0014417 -0.0000100 0.1280922
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.7518494 0.7312877 0.7724110 no
tenureirregular -0.7781818 -0.8205601 -0.7358035 no
tenureown_apartment 0.0608355 0.0326340 0.0890370 no
tenureown_house 0.1414021 0.1094175 0.1733867 no
tenurerent 0.2826007 0.2597114 0.3054901 no
placeurban -0.0121440 -0.0348533 0.0105652 no
age -0.0000777 -0.0001566 0.0000012 no
> plot(contrib.mfx, decreasing = FALSE, horiz = TRUE)
>
>
> # package survey svy
> des = survey::svydesign(~1, data= housing, weights = rep(1, NROW(housing)))
> fit.svy = survey::svyglm(bmi ~ tenure+height+weight, design = des)
> contrib.svy = contribution(fit.svy, housing$income)
>
>
> # adopted from the `coxph` example in survival package
> testcph <- data.frame(time = c(4,3,1,1,2,2,3),
+ status = c(1,1,1,0,1,1,0),
+ x = c(0,2,1,1,1,0,0),
+ sex = c(0,0,0,0,1,1,1),
+ income = c(100,50, 20, 20, 50, 60,100))
>
> # Fit a stratified model
> fit.coxph = survival::coxph(survival::Surv(time, status) ~ x + survival::strata(sex), testcph)
> contrib.coxph = contribution(fit.coxph, testcph$income)
Error in model.matrix(object)[, names(object$coefficients)][, -1, drop = F] :
incorrect number of dimensions
Calls: contribution -> contribution.coxph
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 0.2.3
Check: examples
Result: ERROR
Running examples in ‘rineq-Ex.R’ failed
The error most likely occurred in:
> ### Name: contribution
> ### Title: Function to decompose the Relative Concentration Index into its
> ### components
> ### Aliases: contribution
>
> ### ** Examples
>
> data(housing)
>
> ## Linear regression direct decomposition
> fit.lm <- lm(bmi ~ sex + tenure + place + age,data = housing)
>
> # decompose relative concentration index
> contrib.lm <- contribution(fit.lm, housing$income)
> summary(contrib.lm)
Overall CI: 0.121004
95% confidence interval: 0.1181791 0.123829
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 38.2448886 0.0462779 0.0000000
sexmale 70.3033532 0.0850699 0.2241433
tenureirregular -16.8438839 -0.0203818 0.0307882
tenureown_apartment 0.0751962 0.0000910 0.0018771
tenureown_house 1.3489119 0.0016322 0.0136284
tenurerent 6.6988511 0.0081059 0.0517000
placeurban 0.0671404 0.0000812 -0.0123887
age 0.1055426 0.0001277 0.0324067
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.3795336 0.3691540 0.3899131 no
tenureirregular -0.6619993 -0.6980505 -0.6259481 no
tenureown_apartment 0.0484737 0.0260028 0.0709447 no
tenureown_house 0.1197676 0.0926766 0.1468586 no
tenurerent 0.1567869 0.1440879 0.1694859 no
placeurban -0.0065578 -0.0188208 0.0057052 no
age 0.0039409 -0.0000583 0.0079401 no
> plot(contrib.lm, decreasing = FALSE, horiz = TRUE)
>
>
> # GLM: Decomposition based on predicted outcome
> fit.logit <-glm(high.bmi ~ sex + tenure + place + age, data = housing)
>
> contrib.logit <- contribution(fit.logit, housing$income)
> summary(contrib.logit)
Overall CI: 0.2502025
95% confidence interval: 0.2429066 0.2574983
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 0.0000000 0.0000000 0.0000000
sexmale 108.4563232 0.2713604 0.7149839
tenureirregular -17.2872968 -0.0432532 0.0653373
tenureown_apartment 0.1140275 0.0002853 0.0058857
tenureown_house 1.4916426 0.0037321 0.0311614
tenurerent 7.0608518 0.0176664 0.1126780
placeurban 0.0573415 0.0001435 -0.0218778
age 0.1071102 0.0002680 0.0680031
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.3795336 0.3691540 0.3899131 no
tenureirregular -0.6619993 -0.6980505 -0.6259481 no
tenureown_apartment 0.0484737 0.0260028 0.0709447 no
tenureown_house 0.1197676 0.0926766 0.1468586 no
tenurerent 0.1567869 0.1440879 0.1694859 no
placeurban -0.0065578 -0.0188208 0.0057052 no
age 0.0039409 -0.0000583 0.0079401 no
> plot(contrib.logit, decreasing = FALSE,horiz = TRUE)
>
>
> # GLM probit: Decomposition based on predicted outcome
> fit.probit <-glm(high.bmi ~ sex + tenure + place + age, data = housing,
+ family = binomial(link = probit))
>
> # binary, set type to 'CIw'
> contrib.probit <- contribution(fit.probit, housing$income, type = "CIw")
> summary(contrib.probit)
Overall CI: -0.26355
95% confidence interval: -0.2718336 -0.2552664
(based on a corrected value)
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 272.0517695 -0.7169924 0.0000000
sexmale -179.3760257 0.4727455 0.6287769
tenureirregular 25.5830437 -0.0674241 0.0866431
tenureown_apartment -0.1800413 0.0004745 0.0077997
tenureown_house -2.1688445 0.0057160 0.0404237
tenurerent -15.7773222 0.0415811 0.1471374
placeurban -0.1352142 0.0003564 -0.0293442
age 0.0026347 -0.0000069 0.0893297
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.7518494 0.7312877 0.7724110 no
tenureirregular -0.7781818 -0.8205601 -0.7358035 no
tenureown_apartment 0.0608355 0.0326340 0.0890370 no
tenureown_house 0.1414021 0.1094175 0.1733867 no
tenurerent 0.2826007 0.2597114 0.3054901 no
placeurban -0.0121440 -0.0348533 0.0105652 no
age -0.0000777 -0.0001566 0.0000012 no
> plot(contrib.probit, decreasing = FALSE,horiz = TRUE)
>
>
> # Marginal effects probit using package 'mfx': Decomposition based on predicted outcome
> fit.mfx <-mfx::probitmfx(high.bmi ~ sex + tenure + place + age, data = housing)
>
> contrib.mfx <- contribution(fit.mfx, housing$income, type = "CIw")
> summary(contrib.mfx, type="CIw")
Overall CI: 0.6906082
95% confidence interval: 0.6694697 0.7117467
Decomposition:
Contribution (%) Contribution (Abs) Elasticity
residual 22.2833787 0.1538908 0.0000000
sexmale 80.4392036 0.5555197 0.7388710
tenureirregular -12.5529543 -0.0866917 0.1114029
tenureown_apartment 0.0984600 0.0006800 0.0111772
tenureown_house 1.1582469 0.0079989 0.0565688
tenurerent 8.5011533 0.0587097 0.2077477
placeurban 0.0739535 0.0005107 -0.0420559
age -0.0014417 -0.0000100 0.1280922
Concentration Index lower 5% upper 5% Corrected
residual NA NA NA no
sexmale 0.7518494 0.7312877 0.7724110 no
tenureirregular -0.7781818 -0.8205601 -0.7358035 no
tenureown_apartment 0.0608355 0.0326340 0.0890370 no
tenureown_house 0.1414021 0.1094175 0.1733867 no
tenurerent 0.2826007 0.2597114 0.3054901 no
placeurban -0.0121440 -0.0348533 0.0105652 no
age -0.0000777 -0.0001566 0.0000012 no
> plot(contrib.mfx, decreasing = FALSE, horiz = TRUE)
>
>
> # package survey svy
> des = survey::svydesign(~1, data= housing, weights = rep(1, NROW(housing)))
> fit.svy = survey::svyglm(bmi ~ tenure+height+weight, design = des)
> contrib.svy = contribution(fit.svy, housing$income)
>
>
> # adopted from the `coxph` example in survival package
> testcph <- data.frame(time = c(4,3,1,1,2,2,3),
+ status = c(1,1,1,0,1,1,0),
+ x = c(0,2,1,1,1,0,0),
+ sex = c(0,0,0,0,1,1,1),
+ income = c(100,50, 20, 20, 50, 60,100))
>
> # Fit a stratified model
> fit.coxph = survival::coxph(survival::Surv(time, status) ~ x + survival::strata(sex), testcph)
> contrib.coxph = contribution(fit.coxph, testcph$income)
Error in model.matrix(object)[, names(object$coefficients)][, -1, drop = F] :
incorrect number of dimensions
Calls: contribution -> contribution.coxph
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-oldrel-windows-x86_64