CRAN Package Check Results for Package betareg

Last updated on 2018-04-24 13:46:19 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.1-0 11.54 101.16 112.70 ERROR
r-devel-linux-x86_64-debian-gcc 3.1-0 8.55 74.33 82.88 ERROR
r-devel-linux-x86_64-fedora-clang 3.1-0 207.92 OK
r-devel-linux-x86_64-fedora-gcc 3.1-0 199.67 OK
r-devel-windows-ix86+x86_64 3.1-0 19.00 162.00 181.00 OK
r-patched-linux-x86_64 3.1-0 10.68 94.37 105.05 ERROR
r-patched-solaris-x86 3.1-0 246.90 OK
r-release-linux-x86_64 3.1-0 10.49 96.01 106.50 ERROR
r-release-windows-ix86+x86_64 3.1-0 19.00 162.00 181.00 OK
r-release-osx-x86_64 3.1-0 OK
r-oldrel-windows-ix86+x86_64 3.1-0 6.00 244.00 250.00 OK
r-oldrel-osx-x86_64 3.1-0 OK

Check Details

Version: 3.1-0
Check: examples
Result: ERROR
    Running examples in ‘betareg-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: betatree
    > ### Title: Beta Regression Trees
    > ### Aliases: betatree plot.betatree print.betatree predict.betatree
    > ### sctest.betatree
    > ### Keywords: tree
    >
    > ### ** Examples
    >
    > options(digits = 4)
    >
    > ## data with two groups of dyslexic and non-dyslexic children
    > data("ReadingSkills", package = "betareg")
    > ## additional random noise (not associated with reading scores)
    > set.seed(1071)
    > ReadingSkills$x1 <- rnorm(nrow(ReadingSkills))
    > ReadingSkills$x2 <- runif(nrow(ReadingSkills))
    > ReadingSkills$x3 <- factor(rnorm(nrow(ReadingSkills)) > 0)
    >
    > ## fit beta regression tree: in each node
    > ## - accurcay's mean and precision depends on iq
    > ## - partitioning is done by dyslexia and the noise variables x1, x2, x3
    > ## only dyslexia is correctly selected for splitting
    > bt <- betatree(accuracy ~ iq | iq, ~ dyslexia + x1 + x2 + x3,
    + data = ReadingSkills, minsize = 10)
    > plot(bt)
    >
    > ## inspect result
    > coef(bt)
     (Intercept) iq (phi)_(Intercept) (phi)_iq
    2 1.6565 1.46571 1.273 2.048
    3 0.3809 -0.08623 4.808 0.826
    > summary(bt, node = 2)
    
    Call:
    betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
    Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
    -1.821 -0.521 0.061 0.849 1.063
    
    Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 1.657 0.286 5.78 7.3e-09 ***
    iq 1.466 0.248 5.92 3.2e-09 ***
    
    Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 1.273 0.307 4.15 3.4e-05 ***
    iq 2.048 0.331 6.19 5.9e-10 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Type of estimator: ML (maximum likelihood)
    Log-likelihood: 39.4 on 4 Df
    Pseudo R-squared: 0.149
    Number of iterations: 17 (BFGS) + 1 (Fisher scoring)
    > summary(bt, node = 3)
    
    Call:
    betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
    Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
    -2.455 -0.659 -0.079 0.813 1.610
    
    Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 0.3809 0.0486 7.83 4.8e-15 ***
    iq -0.0862 0.0549 -1.57 0.12
    
    Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 4.808 0.414 11.61 <2e-16 ***
    iq 0.826 0.395 2.09 0.036 *
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Type of estimator: ML (maximum likelihood)
    Log-likelihood: 27.3 on 4 Df
    Pseudo R-squared: 0.0391
    Number of iterations: 16 (BFGS) + 2 (Fisher scoring)
    > library("strucchange")
    Loading required package: zoo
    
    Attaching package: ‘zoo’
    
    The following objects are masked from ‘package:base’:
    
     as.Date, as.Date.numeric
    
    Loading required package: sandwich
    > sctest(bt)
    Error in UseMethod("estfun") :
     no applicable method for 'estfun' applied to an object of class "c('betatree', 'modelparty', 'party')"
    Calls: sctest -> sctest.default -> gefp -> scores
    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: 3.1-0
Check: tests
Result: ERROR
     Running ‘betamix.R’ [10s/12s]
     Comparing ‘betamix.Rout’ to ‘betamix.Rout.save’ ...27c27
    < convergence after 17 iterations
    ---
    > convergence after 43 iterations
    32c32
    < (Intercept) 1.403 0.263 5.33 9.9e-08 ***
    ---
    > (Intercept) 1.403 0.263 5.33 9.8e-08 ***
    54c54
    < (Intercept) 4.251 0.747 5.69 1.3e-08 ***
    ---
    > (Intercept) 4.252 0.747 5.69 1.3e-08 ***
    70c70
    < [2,] 0.8720 0.1280 0.0000
    ---
    > [2,] 0.8670 0.1330 0.0000
    72c72
    < [4,] 0.1521 0.0000 0.8479
    ---
    > [4,] 0.1524 0.0000 0.8476
    74,75c74,75
    < [6,] 0.4674 0.5326 0.0000
    < [7,] 0.9276 0.0724 0.0000
    ---
    > [6,] 0.4654 0.5346 0.0000
    > [7,] 0.9255 0.0745 0.0000
    78,82c78,82
    < [10,] 0.0938 0.0000 0.9062
    < [11,] 0.0305 0.0000 0.9695
    < [12,] 0.1621 0.0000 0.8379
    < [13,] 0.0399 0.0000 0.9601
    < [14,] 0.2680 0.0000 0.7320
    ---
    > [10,] 0.0943 0.0000 0.9057
    > [11,] 0.0307 0.0000 0.9693
    > [12,] 0.1626 0.0000 0.8374
    > [13,] 0.0402 0.0000 0.9598
    > [14,] 0.2679 0.0000 0.7321
    84,85c84,85
    < [16,] 0.0399 0.0000 0.9601
    < [17,] 0.3641 0.6359 0.0000
    ---
    > [16,] 0.0402 0.0000 0.9598
    > [17,] 0.3605 0.6395 0.0000
    87,88c87,88
    < [19,] 0.0806 0.9194 0.0000
    < [20,] 0.2377 0.0000 0.7623
    ---
    > [19,] 0.0796 0.9204 0.0000
    > [20,] 0.2378 0.0000 0.7622
    91,92c91,92
    < [23,] 0.6760 0.3240 0.0000
    < [24,] 0.0415 0.9585 0.0000
    ---
    > [23,] 0.6718 0.3282 0.0000
    > [24,] 0.0410 0.9590 0.0000
    94,100c94,100
    < [26,] 0.0827 0.9173 0.0000
    < [27,] 0.1375 0.8625 0.0000
    < [28,] 0.8438 0.1562 0.0000
    < [29,] 0.1856 0.8144 0.0000
    < [30,] 0.2568 0.7432 0.0000
    < [31,] 0.0397 0.9603 0.0000
    < [32,] 0.0101 0.9899 0.0000
    ---
    > [26,] 0.0820 0.9180 0.0000
    > [27,] 0.1366 0.8634 0.0000
    > [28,] 0.8388 0.1612 0.0000
    > [29,] 0.1845 0.8155 0.0000
    > [30,] 0.2558 0.7442 0.0000
    > [31,] 0.0394 0.9606 0.0000
    > [32,] 0.0100 0.9900 0.0000
    102,112c102,112
    < [34,] 0.3365 0.6635 0.0000
    < [35,] 0.1013 0.8987 0.0000
    < [36,] 0.5090 0.4910 0.0000
    < [37,] 0.3690 0.6310 0.0000
    < [38,] 0.1338 0.8662 0.0000
    < [39,] 0.5589 0.4411 0.0000
    < [40,] 0.3227 0.6773 0.0000
    < [41,] 0.3087 0.6913 0.0000
    < [42,] 0.2423 0.7577 0.0000
    < [43,] 0.2825 0.7175 0.0000
    < [44,] 0.2558 0.7442 0.0000
    ---
    > [34,] 0.3357 0.6643 0.0000
    > [35,] 0.1004 0.8996 0.0000
    > [36,] 0.5048 0.4952 0.0000
    > [37,] 0.3669 0.6331 0.0000
    > [38,] 0.1337 0.8663 0.0000
    > [39,] 0.5545 0.4455 0.0000
    > [40,] 0.3209 0.6791 0.0000
    > [41,] 0.3069 0.6931 0.0000
    > [42,] 0.2416 0.7584 0.0000
    > [43,] 0.2812 0.7188 0.0000
    > [44,] 0.2555 0.7445 0.0000
     Running ‘betatree.R’ [3s/5s]
    Running the tests in ‘tests/betatree.R’ failed.
    Complete output:
     > options(digits = 4)
     >
     > ## package and data
     > library("betareg")
     > data("ReadingSkills", package = "betareg")
     >
     > ## augment with random noise
     > set.seed(1071)
     > n <- nrow(ReadingSkills)
     > ReadingSkills$x1 <- rnorm(n)
     > ReadingSkills$x2 <- runif(n)
     > ReadingSkills$x3 <- factor(sample(0:1, n, replace = TRUE))
     >
     > ## fit beta regression tree
     > rs_tree <- betatree(accuracy ~ iq | iq, ~ dyslexia + x1 + x2 + x3,
     + data = ReadingSkills, minsize = 10)
     >
     > ## methods
     > print(rs_tree)
     Beta regression tree
    
     Model formula:
     accuracy ~ iq + iq | dyslexia + x1 + x2 + x3
    
     Fitted party:
     [1] root
     | [2] dyslexia in no: n = 25
     | (Intercept) iq (phi)_(Intercept) (phi)_iq
     | 1.657 1.466 1.273 2.048
     | [3] dyslexia in yes: n = 19
     | (Intercept) iq (phi)_(Intercept) (phi)_iq
     | 0.38093 -0.08623 4.80766 0.82603
    
     Number of inner nodes: 1
     Number of terminal nodes: 2
     Number of parameters per node: 4
     Objective function (negative log-likelihood): 66.73
     > summary(rs_tree)
     $`2`
    
     Call:
     betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
     Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
     -1.821 -0.521 0.061 0.849 1.063
    
     Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 1.657 0.286 5.78 7.3e-09 ***
     iq 1.466 0.248 5.92 3.2e-09 ***
    
     Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 1.273 0.307 4.15 3.4e-05 ***
     iq 2.048 0.331 6.19 5.9e-10 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Type of estimator: ML (maximum likelihood)
     Log-likelihood: 39.4 on 4 Df
     Pseudo R-squared: 0.149
     Number of iterations: 17 (BFGS) + 1 (Fisher scoring)
    
     $`3`
    
     Call:
     betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
     Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
     -2.455 -0.659 -0.079 0.813 1.610
    
     Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 0.3809 0.0486 7.83 4.8e-15 ***
     iq -0.0862 0.0549 -1.57 0.12
    
     Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 4.808 0.414 11.61 <2e-16 ***
     iq 0.826 0.395 2.09 0.036 *
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Type of estimator: ML (maximum likelihood)
     Log-likelihood: 27.3 on 4 Df
     Pseudo R-squared: 0.0391
     Number of iterations: 16 (BFGS) + 2 (Fisher scoring)
    
     > coef(rs_tree)
     (Intercept) iq (phi)_(Intercept) (phi)_iq
     2 1.6565 1.46571 1.273 2.048
     3 0.3809 -0.08623 4.808 0.826
     > library("strucchange")
     Loading required package: zoo
    
     Attaching package: 'zoo'
    
     The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
     Loading required package: sandwich
     > sctest(rs_tree)
     Error in UseMethod("estfun") :
     no applicable method for 'estfun' applied to an object of class "c('betatree', 'modelparty', 'party')"
     Calls: sctest -> sctest.default -> gefp -> scores
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 3.1-0
Check: running R code from vignettes
Result: ERROR
    Errors in running code in vignettes:
    when running code in ‘betareg-ext.Rnw’
     ...
    
    > options(width = 70, prompt = "R> ", continue = "+ ",
    + useFancyQuotes = FALSE)
    
    > library("betareg")
    
    > combine <- function(x, sep, width) {
    + cs <- cumsum(nchar(x))
    + remaining <- if (any(cs[-1] > width))
    + combine(x[c(FALSE, cs[-1] > .... [TRUNCATED]
    
    > prettyPrint <- function(x, sep = " ", linebreak = "\n\t",
    + width = getOption("width")) {
    + x <- strsplit(x, sep)[[1]]
    + paste(combine( .... [TRUNCATED]
    
    > cache <- FALSE
    
    > enumerate <- function(x) paste(paste(x[-length(x)],
    + collapse = ", "), x[length(x)], sep = " and ")
    
    > betamix_methods <- enumerate(paste("\\\\fct{", gsub("\\.betamix",
    + "", as.character(methods(class = "betamix"))), "}", sep = ""))
    
    > cat(prettyPrint(prompt(extraComponent, filename = NA)$usage[[2]],
    + sep = ", ", linebreak = paste("\n", paste(rep(" ", 2), collapse = ""),
    + .... [TRUNCATED]
    extraComponent(type = c("uniform", "betareg"), coef, delta,
     link = "logit", link.phi = "log")
    > data("ReadingSkills", package = "betareg")
    
    > mean_accuracy <- format(round(with(ReadingSkills,
    + tapply(accuracy, dyslexia, mean)), digits = 3), nsmall = 3)
    
    > mean_iq <- format(round(with(ReadingSkills, tapply(iq,
    + dyslexia, mean)), digits = 3), nsmall = 3)
    
    > data("ReadingSkills", package = "betareg")
    
    > rs_ols <- lm(qlogis(accuracy) ~ dyslexia * iq, data = ReadingSkills)
    
    > rs_beta <- betareg(accuracy ~ dyslexia * iq | dyslexia +
    + iq, data = ReadingSkills, hessian = TRUE)
    
    > cl1 <- hcl(c(260, 0), 90, 40)
    
    > cl2 <- hcl(c(260, 0), 10, 95)
    
    > plot(accuracy ~ iq, data = ReadingSkills, col = cl2[as.numeric(dyslexia)],
    + main = "Reading skills data", xlab = "IQ score", ylab = "Reading a ..." ... [TRUNCATED]
    
    > points(accuracy ~ iq, data = ReadingSkills, cex = 1.5,
    + pch = (1:2)[as.numeric(dyslexia)], col = cl1[as.numeric(dyslexia)])
    
    > nd <- data.frame(dyslexia = "no", iq = -30:30/10)
    
    > lines(nd$iq, predict(rs_beta, nd), col = cl1[1], lwd = 2)
    
    > lines(nd$iq, plogis(predict(rs_ols, nd)), col = cl1[1],
    + lty = 2, lwd = 2)
    
    > nd <- data.frame(dyslexia = "yes", iq = -30:30/10)
    
    > lines(nd$iq, predict(rs_beta, nd), col = cl1[2], lwd = 2)
    
    > lines(nd$iq, plogis(predict(rs_ols, nd)), col = cl1[2],
    + lty = 2, lwd = 2)
    
    > legend("topleft", c("control", "dyslexic", "betareg",
    + "lm"), lty = c(NA, NA, 1:2), pch = c(19, 17, NA, NA), lwd = 2,
    + col = c(cl2, 1, 1 .... [TRUNCATED]
    
    > legend("topleft", c("control", "dyslexic", "betareg",
    + "lm"), lty = c(NA, NA, 1:2), pch = c(1, 2, NA, NA), col = c(cl1,
    + NA, NA), bty = .... [TRUNCATED]
    
    > data("ReadingSkills", package = "betareg")
    
    > rs_f <- accuracy ~ dyslexia * iq | dyslexia * iq
    
    > rs_ml <- betareg(rs_f, data = ReadingSkills, type = "ML")
    
    > rs_bc <- betareg(rs_f, data = ReadingSkills, type = "BC")
    
    > rs_br <- betareg(rs_f, data = ReadingSkills, type = "BR")
    
    > rs_list <- list(rs_ml, rs_bc, rs_br)
    
    > cf <- paste("$", format(round(sapply(rs_list, coef),
    + digits = 3), nsmall = 3), "$\\phantom{)}", sep = "")
    
    > se <- paste("(", format(round(sapply(rs_list, function(x) sqrt(diag(vcov(x)))),
    + digits = 3), nsmall = 3), ")", sep = "")
    
    > ll <- paste("$", format(round(sapply(rs_list, logLik),
    + digits = 3), nsmall = 3), "$\\phantom{)}", sep = "")
    
    > cfse <- matrix(as.vector(rbind(cf, se)), ncol = 3)
    
    > cfse <- cbind(c("Mean", rep("", 7), "Precision", rep("",
    + 7)), rep(as.vector(rbind(c("(Intercept)", "\\code{dyslexia}",
    + "\\code{iq}", " ..." ... [TRUNCATED]
    
    > cfse <- rbind(cfse, c("Log-likelihood", "", ll[1:2],
    + paste(ll[3], "\\\\ \\hline")))
    
    > writeLines(apply(cfse, 1, paste, collapse = " & "))
    Mean & (Intercept) & $ 1.019$\phantom{)} & $ 0.990$\phantom{)} & $ 0.985$\phantom{)} \\
     & & (0.145) & (0.150) & (0.150) \\
     & \code{dyslexia} & $-0.638$\phantom{)} & $-0.610$\phantom{)} & $-0.603$\phantom{)} \\
     & & (0.145) & (0.150) & (0.150) \\
     & \code{iq} & $ 0.690$\phantom{)} & $ 0.700$\phantom{)} & $ 0.707$\phantom{)} \\
     & & (0.127) & (0.133) & (0.133) \\
     & \code{dyslexia:iq} & $-0.776$\phantom{)} & $-0.786$\phantom{)} & $-0.784$\phantom{)} \\
     & & (0.127) & (0.133) & (0.133) \\ \hline
    Precision & (Intercept) & $ 3.040$\phantom{)} & $ 2.811$\phantom{)} & $ 2.721$\phantom{)} \\
     & & (0.258) & (0.257) & (0.256) \\
     & \code{dyslexia} & $ 1.768$\phantom{)} & $ 1.705$\phantom{)} & $ 1.634$\phantom{)} \\
     & & (0.258) & (0.257) & (0.256) \\
     & \code{iq} & $ 1.437$\phantom{)} & $ 1.370$\phantom{)} & $ 1.281$\phantom{)} \\
     & & (0.257) & (0.257) & (0.257) \\
     & \code{dyslexia:iq} & $-0.611$\phantom{)} & $-0.668$\phantom{)} & $-0.759$\phantom{)} \\
     & & (0.257) & (0.257) & (0.257) \\ \hline
    Log-likelihood & & $66.734$\phantom{)} & $66.334$\phantom{)} & $66.134$\phantom{)} \\ \hline
    
    > pr_phi <- sapply(list(`Maximum likelihood` = rs_ml,
    + `Bias correction` = rs_bc, `Bias reduction` = rs_br), predict,
    + type = "precision")
    
    > pairs(log(pr_phi), panel = function(x, y, ...) {
    + panel.smooth(x, y, ...)
    + abline(0, 1, lty = 2)
    + })
    
    > set.seed(1071)
    
    > n <- nrow(ReadingSkills)
    
    > ReadingSkills$x1 <- rnorm(n)
    
    > ReadingSkills$x2 <- runif(n)
    
    > ReadingSkills$x3 <- factor(sample(0:1, n, replace = TRUE))
    
    > if (cache & file.exists("betareg-ext-betatree.rda")) {
    + load("betareg-ext-betatree.rda")
    + } else {
    + rs_tree <- betatree(accuracy ~ iq | i .... [TRUNCATED]
    
    > plot(rs_tree)
    
    > plot(rs_tree)
    
    > coef(rs_tree)
     (Intercept) iq (phi)_(Intercept) (phi)_iq
    2 1.6565251 1.46570750 1.272597 2.0478577
    3 0.3809322 -0.08622808 4.807662 0.8260329
    
    > rs_tree
    Beta regression tree
    
    Model formula:
    accuracy ~ iq + iq | dyslexia + x1 + x2 + x3
    
    Fitted party:
    [1] root
    | [2] dyslexia in no: n = 25
    | (Intercept) iq (phi)_(Intercept)
    | 1.656525 1.465708 1.272597
    | (phi)_iq
    | 2.047858
    | [3] dyslexia in yes: n = 19
    | (Intercept) iq (phi)_(Intercept)
    | 0.38093218 -0.08622808 4.80766206
    | (phi)_iq
    | 0.82603290
    
    Number of inner nodes: 1
    Number of terminal nodes: 2
    Number of parameters per node: 4
    Objective function (negative log-likelihood): 66.73409
    
    > library("strucchange")
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    Loading required package: sandwich
    
    > sctest(rs_tree)
    
     When sourcing 'betareg-ext.R':
    Error: no applicable method for ‘estfun’ applied to an object of class "c('betatree', 'modelparty', 'party')"
    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: 3.1-0
Check: re-building of vignette outputs
Result: WARN
    Error in re-building vignettes:
     ...
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    Loading required package: sandwich
    
    Error: processing vignette ‘betareg-ext.Rnw’ failed with diagnostics:
     chunk 16 (label = ReadingSkills-tree-sctest)
    Error in UseMethod("estfun") :
     no applicable method for ‘estfun’ applied to an object of class "c('betatree', 'modelparty', 'party')"
    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: 3.1-0
Check: tests
Result: ERROR
     Running ‘betamix.R’ [7s/9s]
     Comparing ‘betamix.Rout’ to ‘betamix.Rout.save’ ...27c27
    < convergence after 17 iterations
    ---
    > convergence after 43 iterations
    32c32
    < (Intercept) 1.403 0.263 5.33 9.9e-08 ***
    ---
    > (Intercept) 1.403 0.263 5.33 9.8e-08 ***
    54c54
    < (Intercept) 4.251 0.747 5.69 1.3e-08 ***
    ---
    > (Intercept) 4.252 0.747 5.69 1.3e-08 ***
    70c70
    < [2,] 0.8720 0.1280 0.0000
    ---
    > [2,] 0.8670 0.1330 0.0000
    72c72
    < [4,] 0.1521 0.0000 0.8479
    ---
    > [4,] 0.1524 0.0000 0.8476
    74,75c74,75
    < [6,] 0.4674 0.5326 0.0000
    < [7,] 0.9276 0.0724 0.0000
    ---
    > [6,] 0.4654 0.5346 0.0000
    > [7,] 0.9255 0.0745 0.0000
    78,82c78,82
    < [10,] 0.0938 0.0000 0.9062
    < [11,] 0.0305 0.0000 0.9695
    < [12,] 0.1621 0.0000 0.8379
    < [13,] 0.0399 0.0000 0.9601
    < [14,] 0.2680 0.0000 0.7320
    ---
    > [10,] 0.0943 0.0000 0.9057
    > [11,] 0.0307 0.0000 0.9693
    > [12,] 0.1626 0.0000 0.8374
    > [13,] 0.0402 0.0000 0.9598
    > [14,] 0.2679 0.0000 0.7321
    84,85c84,85
    < [16,] 0.0399 0.0000 0.9601
    < [17,] 0.3641 0.6359 0.0000
    ---
    > [16,] 0.0402 0.0000 0.9598
    > [17,] 0.3605 0.6395 0.0000
    87,88c87,88
    < [19,] 0.0806 0.9194 0.0000
    < [20,] 0.2377 0.0000 0.7623
    ---
    > [19,] 0.0796 0.9204 0.0000
    > [20,] 0.2378 0.0000 0.7622
    91,92c91,92
    < [23,] 0.6760 0.3240 0.0000
    < [24,] 0.0415 0.9585 0.0000
    ---
    > [23,] 0.6718 0.3282 0.0000
    > [24,] 0.0410 0.9590 0.0000
    94,100c94,100
    < [26,] 0.0827 0.9173 0.0000
    < [27,] 0.1375 0.8625 0.0000
    < [28,] 0.8438 0.1562 0.0000
    < [29,] 0.1856 0.8144 0.0000
    < [30,] 0.2568 0.7432 0.0000
    < [31,] 0.0397 0.9603 0.0000
    < [32,] 0.0101 0.9899 0.0000
    ---
    > [26,] 0.0820 0.9180 0.0000
    > [27,] 0.1366 0.8634 0.0000
    > [28,] 0.8388 0.1612 0.0000
    > [29,] 0.1845 0.8155 0.0000
    > [30,] 0.2558 0.7442 0.0000
    > [31,] 0.0394 0.9606 0.0000
    > [32,] 0.0100 0.9900 0.0000
    102,112c102,112
    < [34,] 0.3365 0.6635 0.0000
    < [35,] 0.1013 0.8987 0.0000
    < [36,] 0.5090 0.4910 0.0000
    < [37,] 0.3690 0.6310 0.0000
    < [38,] 0.1338 0.8662 0.0000
    < [39,] 0.5589 0.4411 0.0000
    < [40,] 0.3227 0.6773 0.0000
    < [41,] 0.3087 0.6913 0.0000
    < [42,] 0.2423 0.7577 0.0000
    < [43,] 0.2825 0.7175 0.0000
    < [44,] 0.2558 0.7442 0.0000
    ---
    > [34,] 0.3357 0.6643 0.0000
    > [35,] 0.1004 0.8996 0.0000
    > [36,] 0.5048 0.4952 0.0000
    > [37,] 0.3669 0.6331 0.0000
    > [38,] 0.1337 0.8663 0.0000
    > [39,] 0.5545 0.4455 0.0000
    > [40,] 0.3209 0.6791 0.0000
    > [41,] 0.3069 0.6931 0.0000
    > [42,] 0.2416 0.7584 0.0000
    > [43,] 0.2812 0.7188 0.0000
    > [44,] 0.2555 0.7445 0.0000
     Running ‘betatree.R’ [2s/3s]
    Running the tests in ‘tests/betatree.R’ failed.
    Complete output:
     > options(digits = 4)
     >
     > ## package and data
     > library("betareg")
     > data("ReadingSkills", package = "betareg")
     >
     > ## augment with random noise
     > set.seed(1071)
     > n <- nrow(ReadingSkills)
     > ReadingSkills$x1 <- rnorm(n)
     > ReadingSkills$x2 <- runif(n)
     > ReadingSkills$x3 <- factor(sample(0:1, n, replace = TRUE))
     >
     > ## fit beta regression tree
     > rs_tree <- betatree(accuracy ~ iq | iq, ~ dyslexia + x1 + x2 + x3,
     + data = ReadingSkills, minsize = 10)
     >
     > ## methods
     > print(rs_tree)
     Beta regression tree
    
     Model formula:
     accuracy ~ iq + iq | dyslexia + x1 + x2 + x3
    
     Fitted party:
     [1] root
     | [2] dyslexia in no: n = 25
     | (Intercept) iq (phi)_(Intercept) (phi)_iq
     | 1.657 1.466 1.273 2.048
     | [3] dyslexia in yes: n = 19
     | (Intercept) iq (phi)_(Intercept) (phi)_iq
     | 0.38093 -0.08623 4.80766 0.82603
    
     Number of inner nodes: 1
     Number of terminal nodes: 2
     Number of parameters per node: 4
     Objective function (negative log-likelihood): 66.73
     > summary(rs_tree)
     $`2`
    
     Call:
     betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
     Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
     -1.821 -0.521 0.061 0.849 1.063
    
     Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 1.657 0.286 5.78 7.3e-09 ***
     iq 1.466 0.248 5.92 3.2e-09 ***
    
     Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 1.273 0.307 4.15 3.4e-05 ***
     iq 2.048 0.331 6.19 5.9e-10 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Type of estimator: ML (maximum likelihood)
     Log-likelihood: 39.4 on 4 Df
     Pseudo R-squared: 0.149
     Number of iterations: 17 (BFGS) + 1 (Fisher scoring)
    
     $`3`
    
     Call:
     betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
     Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
     -2.455 -0.659 -0.079 0.813 1.610
    
     Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 0.3809 0.0486 7.83 4.8e-15 ***
     iq -0.0862 0.0549 -1.57 0.12
    
     Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 4.808 0.414 11.61 <2e-16 ***
     iq 0.826 0.395 2.09 0.036 *
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Type of estimator: ML (maximum likelihood)
     Log-likelihood: 27.3 on 4 Df
     Pseudo R-squared: 0.0391
     Number of iterations: 16 (BFGS) + 2 (Fisher scoring)
    
     > coef(rs_tree)
     (Intercept) iq (phi)_(Intercept) (phi)_iq
     2 1.6565 1.46571 1.273 2.048
     3 0.3809 -0.08623 4.808 0.826
     > library("strucchange")
     Loading required package: zoo
    
     Attaching package: 'zoo'
    
     The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
     Loading required package: sandwich
     > sctest(rs_tree)
     Error in UseMethod("estfun") :
     no applicable method for 'estfun' applied to an object of class "c('betatree', 'modelparty', 'party')"
     Calls: sctest -> sctest.default -> gefp -> scores
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 3.1-0
Check: tests
Result: ERROR
     Running ‘betamix.R’ [9s/11s]
     Comparing ‘betamix.Rout’ to ‘betamix.Rout.save’ ...27c27
    < convergence after 17 iterations
    ---
    > convergence after 43 iterations
    32c32
    < (Intercept) 1.403 0.263 5.33 9.9e-08 ***
    ---
    > (Intercept) 1.403 0.263 5.33 9.8e-08 ***
    54c54
    < (Intercept) 4.251 0.747 5.69 1.3e-08 ***
    ---
    > (Intercept) 4.252 0.747 5.69 1.3e-08 ***
    70c70
    < [2,] 0.8720 0.1280 0.0000
    ---
    > [2,] 0.8670 0.1330 0.0000
    72c72
    < [4,] 0.1521 0.0000 0.8479
    ---
    > [4,] 0.1524 0.0000 0.8476
    74,75c74,75
    < [6,] 0.4674 0.5326 0.0000
    < [7,] 0.9276 0.0724 0.0000
    ---
    > [6,] 0.4654 0.5346 0.0000
    > [7,] 0.9255 0.0745 0.0000
    78,82c78,82
    < [10,] 0.0938 0.0000 0.9062
    < [11,] 0.0305 0.0000 0.9695
    < [12,] 0.1621 0.0000 0.8379
    < [13,] 0.0399 0.0000 0.9601
    < [14,] 0.2680 0.0000 0.7320
    ---
    > [10,] 0.0943 0.0000 0.9057
    > [11,] 0.0307 0.0000 0.9693
    > [12,] 0.1626 0.0000 0.8374
    > [13,] 0.0402 0.0000 0.9598
    > [14,] 0.2679 0.0000 0.7321
    84,85c84,85
    < [16,] 0.0399 0.0000 0.9601
    < [17,] 0.3641 0.6359 0.0000
    ---
    > [16,] 0.0402 0.0000 0.9598
    > [17,] 0.3605 0.6395 0.0000
    87,88c87,88
    < [19,] 0.0806 0.9194 0.0000
    < [20,] 0.2377 0.0000 0.7623
    ---
    > [19,] 0.0796 0.9204 0.0000
    > [20,] 0.2378 0.0000 0.7622
    91,92c91,92
    < [23,] 0.6760 0.3240 0.0000
    < [24,] 0.0415 0.9585 0.0000
    ---
    > [23,] 0.6718 0.3282 0.0000
    > [24,] 0.0410 0.9590 0.0000
    94,100c94,100
    < [26,] 0.0827 0.9173 0.0000
    < [27,] 0.1375 0.8625 0.0000
    < [28,] 0.8438 0.1562 0.0000
    < [29,] 0.1856 0.8144 0.0000
    < [30,] 0.2568 0.7432 0.0000
    < [31,] 0.0397 0.9603 0.0000
    < [32,] 0.0101 0.9899 0.0000
    ---
    > [26,] 0.0820 0.9180 0.0000
    > [27,] 0.1366 0.8634 0.0000
    > [28,] 0.8388 0.1612 0.0000
    > [29,] 0.1845 0.8155 0.0000
    > [30,] 0.2558 0.7442 0.0000
    > [31,] 0.0394 0.9606 0.0000
    > [32,] 0.0100 0.9900 0.0000
    102,112c102,112
    < [34,] 0.3365 0.6635 0.0000
    < [35,] 0.1013 0.8987 0.0000
    < [36,] 0.5090 0.4910 0.0000
    < [37,] 0.3690 0.6310 0.0000
    < [38,] 0.1338 0.8662 0.0000
    < [39,] 0.5589 0.4411 0.0000
    < [40,] 0.3227 0.6773 0.0000
    < [41,] 0.3087 0.6913 0.0000
    < [42,] 0.2423 0.7577 0.0000
    < [43,] 0.2825 0.7175 0.0000
    < [44,] 0.2558 0.7442 0.0000
    ---
    > [34,] 0.3357 0.6643 0.0000
    > [35,] 0.1004 0.8996 0.0000
    > [36,] 0.5048 0.4952 0.0000
    > [37,] 0.3669 0.6331 0.0000
    > [38,] 0.1337 0.8663 0.0000
    > [39,] 0.5545 0.4455 0.0000
    > [40,] 0.3209 0.6791 0.0000
    > [41,] 0.3069 0.6931 0.0000
    > [42,] 0.2416 0.7584 0.0000
    > [43,] 0.2812 0.7188 0.0000
    > [44,] 0.2555 0.7445 0.0000
     Running ‘betatree.R’ [3s/4s]
    Running the tests in ‘tests/betatree.R’ failed.
    Complete output:
     > options(digits = 4)
     >
     > ## package and data
     > library("betareg")
     > data("ReadingSkills", package = "betareg")
     >
     > ## augment with random noise
     > set.seed(1071)
     > n <- nrow(ReadingSkills)
     > ReadingSkills$x1 <- rnorm(n)
     > ReadingSkills$x2 <- runif(n)
     > ReadingSkills$x3 <- factor(sample(0:1, n, replace = TRUE))
     >
     > ## fit beta regression tree
     > rs_tree <- betatree(accuracy ~ iq | iq, ~ dyslexia + x1 + x2 + x3,
     + data = ReadingSkills, minsize = 10)
     >
     > ## methods
     > print(rs_tree)
     Beta regression tree
    
     Model formula:
     accuracy ~ iq + iq | dyslexia + x1 + x2 + x3
    
     Fitted party:
     [1] root
     | [2] dyslexia in no: n = 25
     | (Intercept) iq (phi)_(Intercept) (phi)_iq
     | 1.657 1.466 1.273 2.048
     | [3] dyslexia in yes: n = 19
     | (Intercept) iq (phi)_(Intercept) (phi)_iq
     | 0.38093 -0.08623 4.80766 0.82603
    
     Number of inner nodes: 1
     Number of terminal nodes: 2
     Number of parameters per node: 4
     Objective function (negative log-likelihood): 66.73
     > summary(rs_tree)
     $`2`
    
     Call:
     betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
     Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
     -1.821 -0.521 0.061 0.849 1.063
    
     Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 1.657 0.286 5.78 7.3e-09 ***
     iq 1.466 0.248 5.92 3.2e-09 ***
    
     Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 1.273 0.307 4.15 3.4e-05 ***
     iq 2.048 0.331 6.19 5.9e-10 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Type of estimator: ML (maximum likelihood)
     Log-likelihood: 39.4 on 4 Df
     Pseudo R-squared: 0.149
     Number of iterations: 17 (BFGS) + 1 (Fisher scoring)
    
     $`3`
    
     Call:
     betatree(formula = accuracy ~ iq | iq, data = ReadingSkills)
    
     Standardized weighted residuals 2:
     Min 1Q Median 3Q Max
     -2.455 -0.659 -0.079 0.813 1.610
    
     Coefficients (mean model with logit link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 0.3809 0.0486 7.83 4.8e-15 ***
     iq -0.0862 0.0549 -1.57 0.12
    
     Phi coefficients (precision model with log link):
     Estimate Std. Error z value Pr(>|z|)
     (Intercept) 4.808 0.414 11.61 <2e-16 ***
     iq 0.826 0.395 2.09 0.036 *
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Type of estimator: ML (maximum likelihood)
     Log-likelihood: 27.3 on 4 Df
     Pseudo R-squared: 0.0391
     Number of iterations: 16 (BFGS) + 2 (Fisher scoring)
    
     > coef(rs_tree)
     (Intercept) iq (phi)_(Intercept) (phi)_iq
     2 1.6565 1.46571 1.273 2.048
     3 0.3809 -0.08623 4.808 0.826
     > library("strucchange")
     Loading required package: zoo
    
     Attaching package: 'zoo'
    
     The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
     Loading required package: sandwich
     > sctest(rs_tree)
     Error in UseMethod("estfun") :
     no applicable method for 'estfun' applied to an object of class "c('betatree', 'modelparty', 'party')"
     Calls: sctest -> sctest.default -> gefp -> scores
     Execution halted
Flavors: r-patched-linux-x86_64, r-release-linux-x86_64