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[4s/5s] 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
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• DONE

Status: 1 ERROR, 1 NOTE