CRAN Package Check Results for Package Sleuth3

Last updated on 2020-08-03 14:48:40 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0-3 5.65 266.37 272.02 OK
r-devel-linux-x86_64-debian-gcc 1.0-3 4.44 196.25 200.69 OK
r-devel-linux-x86_64-fedora-clang 1.0-3 330.67 NOTE
r-devel-linux-x86_64-fedora-gcc 1.0-3 312.18 OK
r-devel-windows-ix86+x86_64 1.0-3 25.00 311.00 336.00 ERROR
r-patched-linux-x86_64 1.0-3 6.13 254.32 260.45 OK
r-patched-solaris-x86 1.0-3 398.70 NOTE
r-release-linux-x86_64 1.0-3 5.73 257.05 262.78 OK
r-release-macos-x86_64 1.0-3 NOTE
r-release-windows-ix86+x86_64 1.0-3 19.00 481.00 500.00 NOTE
r-oldrel-macos-x86_64 1.0-3 NOTE
r-oldrel-windows-ix86+x86_64 1.0-3 13.00 338.00 351.00 NOTE

Check Details

Version: 1.0-3
Check: installed package size
Result: NOTE
     installed size is 5.1Mb
     sub-directories of 1Mb or more:
     doc 3.7Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0-3
Check: examples
Result: ERROR
    Running examples in 'Sleuth3-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: case1702
    > ### Title: Love and Marriage
    > ### Aliases: case1702
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > str(case1702)
    'data.frame': 30 obs. of 9 variables:
     $ Couple: int 1 2 3 4 5 6 7 8 9 10 ...
     $ HP : int 2 5 4 4 3 3 3 4 4 4 ...
     $ WP : int 4 4 4 4 4 3 4 3 4 3 ...
     $ HC : int 5 4 5 4 5 4 4 5 5 3 ...
     $ WC : int 5 5 5 5 5 4 5 5 5 4 ...
     $ PW : int 3 5 5 3 3 3 4 4 5 4 ...
     $ PH : int 4 5 4 5 4 3 3 4 4 4 ...
     $ CW : int 5 4 5 4 5 5 4 5 5 3 ...
     $ CH : int 5 5 5 5 5 4 4 5 4 4 ...
    > attach(case1702)
    >
    > ## EXPLORATION
    > x <- cbind(HP,WP,HC,WC) # 4 components of level of love you feel for spouse
    > y <- cbind(PW,PH,CW,CH) # 4 components of level of love you perceive from spouse
    >
    > if(require(CCA)){ # Use the CCA library
    + myCCA <- cc(x,y) # Store canonical correlation computations
    + canCor <- myCCA$cor # Extract the canonical correlations
    + canCor #[1] 0.9506990 0.8665601 0.5571876 0.1106555
    +
    + # Make a function to test the number of canonical correlations (advanced).
    + # Bartlett modification of likelihood ratio test
    + # Reference: Mardia, Kent, and Bibby, 1980, Multivariate Analysis,
    + myTest <- function(xMatrix,yMatrix) {
    + if(require(CCA)){ # Use the CCA library
    + myCCA <- cc(xMatrix,yMatrix) # Store CCA computations
    + canCor <- myCCA$cor # extract the canonical correlations
    + n <- dim(x)[1] # number of rows of x,= sample size
    + p <- dim(y)[2] # number of component variables in y
    + q <- dim(x)[2] # number of component variables in x
    + k <- min(p,q) # the maximum number of canonical pairs
    + testStat <- rep(0,k) # store the test statistics; initially set to 0
    + degFr <- rep(0,k) # store the associated degrees of freedom
    + canCor2 <- canCor[k:1] # Reverse order for the following calculations
    + productTerm <- 1
    + for (i in 1:k) {
    + productTerm <- productTerm*(1 - canCor2[i]^2)
    + degFr[i] <- (p + 1 - i)*(q + 1 - i)
    + testStat[i] <- -(n -(p+q+3)/2)*log(productTerm)
    + }
    + pair <- 1:k
    + testStat <- round(testStat[k:1],2) # Revert to original order; round
    + pValue <- round(1 - pchisq(testStat,degFr),4) # p-value to 4 digits
    + canCor <- round(canCor,4) # Round to 4 digits
    + cbind(pair,canCor, testStat,degFr, pValue) # Show the results;
    + } }
    + myTest(x,y)
    +
    + # Explore possible meaningful linear combination suggested by first pair of CCs
    + round(myCCA$xcoef,1)
    + round(myCCA$ycoef,1)
    + # The 1st column of xcoef is almost entirely HC; 1st column of ycoef is CW
    + ccX1 <- myCCA$scores$xscores[,1]
    + plot(ccX1 ~ jitter(HC)) # See if HC is a good substitute for 1st X canonical var
    + cor(ccX1,HC) #[1] 0.9719947
    +
    + ccY1 <- myCCA$scores$yscores[,1]
    + plot(ccY1 ~ jitter(CW)) # See if CW is a good substitute for 1st y canonical var
    + cor(ccY1, CW) #[1] 0.9975468
    +
    + # Analyze the correlation of the meaningful substitute variables
    + cor(HC,CW) # [1] 0.9280323
    + myLm1 <- lm(HC ~ CW)
    + summary(myLm1) # p-value < 0.0001 (test for slope= 0 equiv to test that corr = 0)
    +
    +
    + # Explore possible meaningful linear combination suggested by 2nd pair of CCs
    + # Suggested substitutes from 2nd columns above are WC and CH
    + ccX2 <- myCCA$scores$xscores[,2]
    + WCres <- lm(WC ~ HC)$res # WC with effect of HC removed
    + plot(ccX2 ~ WCres)
    + cor(ccX2,WCres) #[1] 0.9045225
    +
    + ccY2 <- myCCA$scores$yscores[,2]
    + CHres <- lm(CH ~ CW)$res # CH with effect of CW removed
    + plot(ccY2 ~ CHres)
    + cor(ccY2,CHres) # [1] 0.9280248
    +
    + cor(WC,CH) #[1] 0.8134213
    + myLm2 <- lm(WC ~ CH)
    + summary(myLm2) # p-value < 0.0001 for test that correlation = 0
    +
    + # Explore cannonical correlations from other groupings of variables
    + x <- cbind(HP, HC, PW, CW) # husband's responses
    + y <- cbind(WP, WC, PH, CH) # wife's responses
    + myTest(x,y) # No evidence that husbands' responses are correlated with wifes'
    + x <- cbind(HP,PW,WP,PH) # passionate responses
    + y <- cbind(HC,CW,WC,CH) # compassionate responses
    + myTest(x,y) #No evidence that passionate anc compassionate responses are correlated
    +
    +
    + ## GRAPHICAL DISPLAYS FOR PRESENTATION
    + jFactor <- 0.3 # Jittering factor (try different values to see what works)
    + jHC <- jitter(HC,factor=jFactor)
    + jCW <- jitter(CW,factor=jFactor)
    + jCH <- jitter(CH,factor=jFactor)
    + jWC <- jitter(WC,factor=jFactor)
    + opar <- par(no.readonly=TRUE) # Store current graphical parameter settings
    + par(mfrow=c(2,2)) # Prepare to make a 2x2 panel of graphs
    + par(mar=c(1.1,4.1,1.1,1.1) ) # Adjust margins
    + plot(jHC ~ jCW, ylab="Husband's Compassionate Love For His Wife (Jittered)",
    + xlab="", ylim=c(3,5.1), xlim=c(3,5.1), col="black", pch=21, lwd=2,
    + bg="green", cex=2 )
    + text(3,5.1,"correlation = 0.93",adj=0)
    + text(3,5.0,"p-value < 0.0001",adj=0)
    + abline(myLm1)
    +
    + par(mar=c(1.1,1.1,1.1,4.1))
    + plot(jHC ~ jCH, xlab="", ylab="", ylim=c(3,5.1), xlim=c(3,5.1),
    + col="black", pch=21, lwd=2, bg="green", cex=2 )
    + cor(HC,CH) #[1] 0.274204
    + myLm3 <- lm(HC ~ CH)
    + summary(myLm3)# p-value = 0.143
    + text(3,5.1,"correlation = 0.27",adj=0)
    + text(3,5.0,"p-value = 0.14",adj=0)
    + abline(myLm3)
    +
    + par(mar=c(4.1,4.1,1.1,1.1))
    + plot(jWC ~ jCW,
    + xlab="Husband's Perceived Compassionate Love From His Wife (Jittered)",
    + ylab="Wife's Compassionate Love For Her Husband (Jittered)",
    + ylim=c(3,5.1), xlim=c(3,5.1), col="black", pch=21, lwd=2, bg="green", cex=2 )
    + cor(WC,CW) #[1] 0.04171195
    + myLm4 <- lm(WC ~ CW)
    + summary(myLm4) # p-value = 0.827
    + text(3,3.1,"correlation = 0.04",adj=0)
    + text(3,3,"p-value = 0.8",adj=0)
    + abline(myLm4)
    +
    + par(mar=c(4.1,1.1,1.1,4.1))
    + plot(jWC ~ jCH, ylab="",
    + xlab="Wife's Perceived Compassionate Love From Her Husband (Jittered)",
    + ylim=c(3,5.1), xlim=c(3,5.1), col="black", pch=21, lwd=2, bg="green", cex=2 )
    + text(3,3.1,"correlation = 0.81",adj=0)
    + text(3,3,"p-value < 0.0001",adj=0)
    + abline(myLm2)
    +
    + par(opar) # Restore previous graphics parameter settings
    + }
    Loading required package: CCA
    Loading required package: fda
    Loading required package: Matrix
    
    Attaching package: 'fda'
    
    The following object is masked from 'package:graphics':
    
     matplot
    
    Loading required package: fields
    Loading required package: spam
    Loading required package: dotCall64
    Loading required package: grid
    Spam version 2.5-1 (2019-12-12) is loaded.
    Type 'help( Spam)' or 'demo( spam)' for a short introduction
    and overview of this package.
    Help for individual functions is also obtained by adding the
    suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
    
    Attaching package: 'spam'
    
    The following object is masked from 'package:Matrix':
    
     det
    
    The following objects are masked from 'package:base':
    
     backsolve, forwardsolve
    
    Loading required package: maps
    See https://github.com/NCAR/Fields for
     an extensive vignette, other supplements and source code
    Error in svd(x, nu, nv, LINPACK) :
     the LINPACK argument has been defunct since R 3.1.0
    Warning in svd2(Dmat) :
     svd failed using LINPACK = FALSE with n = 4 and p = 4; x stored in 'd:/Rcompile/CRANpkg/local/4.1/Sleuth3.Rcheck\svd.LINPACK.error.matrix131a07fd4449e.rda'
    Error in svd(x, nu, nv, !LINPACK) :
     the LINPACK argument has been defunct since R 3.1.0
    Error in svd2(Dmat) :
     svd also failed using LINPACK = TRUE; x stored in 'd:/Rcompile/CRANpkg/local/4.1/Sleuth3.Rcheck\svd.LINPACK.error.matrix131a07fd4449e.rda'
    Calls: cc -> rcc -> geigen -> svd2
    Execution halted
Flavor: r-devel-windows-ix86+x86_64