CRAN Package Check Results for Package MAd

Last updated on 2018-05-22 01:46:42 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.8-2 4.30 33.30 37.60 ERROR
r-devel-linux-x86_64-debian-gcc 0.8-2 4.14 27.66 31.80 ERROR
r-devel-linux-x86_64-fedora-clang 0.8-2 53.81 NOTE
r-devel-linux-x86_64-fedora-gcc 0.8-2 52.77 NOTE
r-devel-windows-ix86+x86_64 0.8-2 13.00 46.00 59.00 NOTE
r-patched-linux-x86_64 0.8-2 4.86 34.67 39.53 ERROR
r-patched-solaris-x86 0.8-2 69.40 NOTE
r-release-linux-x86_64 0.8-2 5.01 34.11 39.12 ERROR
r-release-windows-ix86+x86_64 0.8-2 13.00 65.00 78.00 NOTE
r-release-osx-x86_64 0.8-2 NOTE
r-oldrel-windows-ix86+x86_64 0.8-2 7.00 66.00 73.00 NOTE
r-oldrel-osx-x86_64 0.8-2 NOTE

Check Details

Version: 0.8-2
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘R2wd’
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-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.8-2
Check: dependencies in R code
Result: NOTE
    'library' or 'require' calls in package code:
     ‘R2wd’ ‘ggplot2’ ‘metafor’
     Please use :: or requireNamespace() instead.
     See section 'Suggested packages' in the 'Writing R Extensions' manual.
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-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 0.8-2
Check: S3 generic/method consistency
Result: NOTE
    Found the following apparent S3 methods exported but not registered:
     mareg.default print.icclist print.macat print.mareg print.omni
     print.summary.mareg r2.mareg summary.mareg wd.default wd.macat
     wd.mareg wd.omni
    See section ‘Registering S3 methods’ in the ‘Writing R Extensions’
    manual.
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-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64

Version: 0.8-2
Check: R code for possible problems
Result: NOTE
    CatCompf: no visible global function definition for ‘aov’
    CatCompf: no visible global function definition for ‘TukeyHSD’
    CatCompf: no visible global function definition for ‘pchisq’
    CatCompr: no visible global function definition for ‘aov’
    CatCompr: no visible global function definition for ‘TukeyHSD’
    CatCompr: no visible global function definition for ‘pchisq’
    CatModf: no visible global function definition for ‘aggregate’
    CatModf: no visible global function definition for ‘pnorm’
    CatModf: no visible global function definition for ‘pchisq’
    CatModfQ: no visible global function definition for ‘pchisq’
    CatModr: no visible global function definition for ‘aggregate’
    CatModr: no visible global function definition for ‘pnorm’
    CatModr: no visible global function definition for ‘pchisq’
    CatModrQ: no visible global function definition for ‘pchisq’
    MRfit: no visible binding for global variable ‘anova’
    MetaG: no visible global function definition for ‘pt’
    OmnibusES: no visible global function definition for ‘pnorm’
    OmnibusES: no visible global function definition for ‘pchisq’
    icc: no visible global function definition for ‘na.omit’
    icc: no visible global function definition for ‘var’
    icc: no visible binding for global variable ‘var’
    icc: no visible global function definition for ‘pf’
    icc: no visible global function definition for ‘qf’
    macat: no visible global function definition for ‘aggregate’
    macat: no visible global function definition for ‘pnorm’
    macat: no visible global function definition for ‘pchisq’
    macatC: no visible global function definition for ‘pchisq’
    mareg.default: no visible global function definition for
     ‘model.response’
    mareg.default: no visible global function definition for
     ‘model.extract’
    mareg.default: no visible global function definition for ‘model.matrix’
    mareg.default: no visible binding for global variable ‘contrasts’
    omni: no visible global function definition for ‘pnorm’
    omni: no visible global function definition for ‘pchisq’
    p.ancova_to_d1: no visible global function definition for ‘qt’
    p.ancova_to_d2: no visible global function definition for ‘qt’
    p_to_d1: no visible global function definition for ‘qt’
    p_to_d2: no visible global function definition for ‘qt’
    print.summary.mareg: no visible global function definition for
     ‘printCoefmat’
    robustSE: no visible global function definition for ‘residuals’
    robustSE: no visible global function definition for ‘pt’
    robustSE: no visible global function definition for ‘qt’
    Undefined global functions or variables:
     TukeyHSD aggregate anova aov contrasts model.extract model.matrix
     model.response na.omit pchisq pf pnorm printCoefmat pt qf qt
     residuals var
    Consider adding
     importFrom("stats", "TukeyHSD", "aggregate", "anova", "aov",
     "contrasts", "model.extract", "model.matrix",
     "model.response", "na.omit", "pchisq", "pf", "pnorm",
     "printCoefmat", "pt", "qf", "qt", "residuals", "var")
    to your NAMESPACE file.
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-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 0.8-2
Check: examples
Result: ERROR
    Running examples in ‘MAd-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: MAd-package
    > ### Title: Meta-Analysis with Mean Differences
    > ### Aliases: MAd-package MAd
    > ### Keywords: package
    >
    > ### ** Examples
    >
    > ## EXAMPLES FOR EACH BROAD AREA
    >
    > # SAMPLE DATA:
    > MA <- data.frame(id=factor(rep(1:5, 3)),
    + measure=c(rep("dep",5), rep("anx",5), rep("shy",5)),
    + es=c(rnorm(5, 0.8, .2), rnorm(5, 0.5, .1), rnorm(5, 0.4, .1)),
    + var.es=abs(rnorm(5*3,0.05, .03)),
    + nT=round(rnorm(5*3, 30, 5),0),
    + nC=round(rnorm(5*3, 30, 5),0),
    + mod1=factor(rep(c("a","b","c","d","e"),3)),
    + mod2=rep(seq(20, 60, 10), 3))
    >
    > # 1. COMPUTE MEAN DIFFERENCE STATISTIC FROM
    > # REPORTED STATS (GENERALLY FROM A PRIMARY STUDY):
    >
    > # suppose the primary study reported an log odds ratio for different
    > # proportions between 2 groups. Then, running:
    >
    > lor_to_d(.9070,.0676)
     d var.d
    [1,] 0.5000553 0.02054794
    >
    > # reported log odds ratio (lor = .9070) and variance (.0676) will output the
    > # standardized mean difference (d) and its variance (var.d) that can be used in
    > # a meta-analysis.
    >
    > ## 2. ACCOUNT FOR DEPENDENCIES: WITHIN-STUDY EFFECT SIZES (ES):
    >
    > ## 2 EXAMPLES:
    > # EXAMPLE 1: AGGREGATING EFFECT SIZES FOR A DATA FRAME
    > # (MULTIPLE STUDIES AT LEAST SOME OF WHICH HAVE MULTIPLE DEPENDENT EFFECT SIZES)
    > # EXAMPLE 2: AGGREGATING EFFECT SIZES FOR SINGLE STUDY WITH THREE OR MORE
    > # EFFECT SIZES WHEN PAIRS OF DVS HAVE DIFFERENT CORRELATIONS
    >
    > ## EXAMPLE 1: MA IS A DATA FRAME CONTAINING MULTIPLE STUDIES (id),
    > ## EACH WITH MULTIPLE EFFECT SIZES (CORRELATIONS BETWEEN ALL PAIRS OF DVS ARE r=.5.)
    >
    >
    > # AGGREGATION PROCEDURE:
    > # method="GO1"; GLESER AND OLKIN (1994) PROCEDURE WHEN d IS COMPUTED
    > # USING POOLED SD IN THE DENOMINATOR
    >
    > MA1 <- agg(id=id, es=es, var=var.es, n.1=nT, n.2=nC, cor = .5, method="GO1", data=MA)
    >
    > MA1
     id es1 var1 n.1 n.2
    1 1 0.5447640 0.04286886 31 33
    2 2 0.5990264 0.04787094 29 29
    3 3 0.5105737 0.04411957 32 30
    4 4 0.5719716 0.04512555 33 29
    5 5 0.6055718 0.04537863 28 33
    >
    > ## EXAMPLE 2: STUDY 1 COMPARES A TREATMENT AND CONTROL GROUP ON
    > ## THREE OUTCOME MEASURES (DEPRESSION, ANXIETY, and SHYNESS).
    > # THE CORRELATION AMONG THE THREE PAIRS OF DVS ARE r12=.5, r13=.2, and r23=.3.
    >
    > study1 <- data.frame( id=factor(rep(1, 3)),
    + measure=c("dep", "anx", "shy"),
    + es=c(0.8, 0.5, 0.4),
    + var.es=c(0.01, 0.02, 0.1),
    + nT=rep(30, 3),
    + nC=rep(30, 3))
    >
    > # ONE WOULD CONSTRUCT THE CORRELATION MATRIX AS FOLLOWS:
    >
    > cors <- matrix(c(1,.5,.2,
    + .5,1,.3,
    + .2,.3,1), nrow=3)
    >
    > # AGGREGATION PROCEDURE:
    > # method="GO1"; GLESER AND OLKIN (1994) PROCEDURE WHEN d
    > # IS COMPUTED USING POOLED SD IN THE DENOMINATOR
    >
    > agg(id=id, es=es, var=var.es, n.1=nT, n.2=nC, cor=cors, method="GO1", mod = NULL, data=study1)
     id es1 var1 n.1 n.2
    1 1 0.5545657 0.0377071 30 30
    >
    >
    > # where MA = data.frame with columns for id, es (standardized
    > # mean difference), var.es (variance of es), n.1 (sample size of group
    > # one), and n.2 (sample size of comparison group) with multiple rows per
    > # study. Outputs an aggregated data.frame with 1 effect size per study.
    >
    > ## 3.OMNIBUS ANALYSIS
    >
    > # FIRST ADD MODERATORS TO THE AGGREGATED DATASET:
    >
    > MODS <- data.frame(id=1:5,
    + mod1=factor(c("a","b","a","b","b")),
    + mod2=as.numeric(c(20, 30, 25, 35, 40)))
    >
    > MA2 <- merge(MA1, MODS, by='id')
    >
    > # Random Effects
    > m0 <- mareg(es1~ 1, var = var1, method = "REML", data = MA2)
    Error in UseMethod("mareg") :
     no applicable method for 'mareg' applied to an object of class "formula"
    Calls: mareg
    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