CRAN Package Check Results for Package allan

Last updated on 2021-02-15 09:47:51 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.01 2.97 35.96 38.93 NOTE
r-devel-linux-x86_64-debian-gcc 1.01 2.23 28.38 30.61 NOTE
r-devel-linux-x86_64-fedora-clang 1.01 56.81 NOTE
r-devel-linux-x86_64-fedora-gcc 1.01 44.50 NOTE
r-devel-windows-ix86+x86_64 1.01 6.00 59.00 65.00 ERROR
r-patched-linux-x86_64 1.01 2.67 34.65 37.32 NOTE
r-patched-solaris-x86 1.01 71.70 NOTE
r-release-linux-x86_64 1.01 2.72 34.50 37.22 NOTE
r-release-macos-x86_64 1.01 NOTE
r-release-windows-ix86+x86_64 1.01 16.00 64.00 80.00 ERROR
r-oldrel-macos-x86_64 1.01 NOTE
r-oldrel-windows-ix86+x86_64 1.01 4.00 44.00 48.00 ERROR

Check Details

Version: 1.01
Check: DESCRIPTION meta-information
Result: NOTE
    Malformed Description field: should contain one or more complete sentences.
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-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.01
Check: R code for possible problems
Result: NOTE
    allanVarSelect: no visible global function definition for 'read.csv'
    fitvbiglm: no visible global function definition for 'read.csv'
    fitvbiglm: no visible global function definition for 'update'
    getbestchunksize: no visible global function definition for 'read.csv'
    getbestchunksize: no visible global function definition for
     'object.size'
    predictvbiglm: no visible global function definition for 'read.csv'
    predictvbiglm: no visible global function definition for 'predict'
    predictvbiglm: no visible global function definition for
     'weighted.mean'
    readinbigdata : <anonymous>: no visible global function definition for
     'read.csv'
    Undefined global functions or variables:
     object.size predict read.csv update weighted.mean
    Consider adding
     importFrom("stats", "predict", "update", "weighted.mean")
     importFrom("utils", "object.size", "read.csv")
    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-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.01
Check: Rd files
Result: NOTE
    prepare_Rd: allan-package.Rd:49-51: Dropping empty section \seealso
    prepare_Rd: allanVarSelect.Rd:59-60: Dropping empty section \details
    prepare_Rd: allanVarSelect.Rd:66-67: Dropping empty section \references
    prepare_Rd: allanVarSelect.Rd:78-79: Dropping empty section \seealso
    prepare_Rd: fitvbiglm.Rd:38-39: Dropping empty section \details
    prepare_Rd: fitvbiglm.Rd:49-50: Dropping empty section \note
    prepare_Rd: fitvbiglm.Rd:43-44: Dropping empty section \references
    prepare_Rd: fitvbiglm.Rd:53-54: Dropping empty section \seealso
    prepare_Rd: getbestchunksize.Rd:35-36: Dropping empty section \details
    prepare_Rd: getbestchunksize.Rd:46-47: Dropping empty section \note
    prepare_Rd: getbestchunksize.Rd:40-41: Dropping empty section \references
    prepare_Rd: getbestchunksize.Rd:50-51: Dropping empty section \seealso
    prepare_Rd: predictvbiglm.Rd:44-45: Dropping empty section \details
    prepare_Rd: predictvbiglm.Rd:56-57: Dropping empty section \note
    prepare_Rd: predictvbiglm.Rd:50-51: Dropping empty section \references
    prepare_Rd: predictvbiglm.Rd:60-61: Dropping empty section \seealso
    prepare_Rd: readinbigdata.Rd:42-43: Dropping empty section \note
    prepare_Rd: readinbigdata.Rd:45-46: Dropping empty section \seealso
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-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.01
Check: Rd line widths
Result: NOTE
    Rd file 'allan-package.Rd':
     \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)
    
    Rd file 'allanVarSelect.Rd':
     \usage lines wider than 90 characters:
     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = "AIC", currentchunksize = -1, si ... [TRUNCATED]
     \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
    
    Rd file 'fitvbiglm.Rd':
     \usage lines wider than 90 characters:
     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)
     \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
    
    Rd file 'getbestchunksize.Rd':
     \usage lines wider than 90 characters:
     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)
     \examples lines wider than 100 characters:
     #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory
     #used. Memory allwed is specified in Gb. The adjfactor is a factor used to adjust memory for overhead
    
    Rd file 'predictvbiglm.Rd':
     \usage lines wider than 90 characters:
     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]
     \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]
     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]
     weightvector<-as.vector(eval(parse(text=paste("CurrentDataSet","$",weightname,sep=""))))
     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)
    
    Rd file 'readinbigdata.Rd':
     \examples lines wider than 100 characters:
     #The return value is either the next chunk of data or NULL if there is no additional data left.
     #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.
    
    These lines will be truncated in the PDF 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-release-linux-x86_64

Version: 1.01
Check: examples
Result: ERROR
    Running examples in 'allan-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: allanVarSelect
    > ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear
    > ### Models
    > ### Aliases: allanVarSelect
    > ### Keywords: stepwise linear regression memory
    >
    > ### ** Examples
    >
    > #Get external data. For your own data skip this next line and replace all
    > #instance of SampleData with "YourFile.csv".
    > SampleData=system.file("extdata","SampleDataFile.csv", package = "allan")
    >
    > #fit smaller data to biglm object
    > columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))
    > datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)
    > datafeed(TRUE)
    > firstchunk<-datafeed(FALSE)
    >
    > #create a biglm model from the small chunk with all variables that will be consdered
    > #for variable selection.
    > bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
    >
    > #now run variable selection
    > FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria="MSE",silent=FALSE)
    [1] "Total memory usage for 1000 lines:"
    110560 bytes
    [1] "Chunksize for dataframe after adjustment factor:"
    [1] 429630
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Criteria:MSE"
    [1] "Iteration Result"
     AICValue BICValue MSEValue
     "1529253.65122942" "1529265.34987705" "1.29997019294336e+129"
    
     "cont3" "1"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Criteria:MSE"
    [1] "Iteration Result"
     AICValue BICValue MSEValue
     "1529255.65122942" "1529273.19920087" "1.29902541076004e+129"
    
     "cont1" "2"
    [1] "Final Results of Variable Selection:"
     AICValue BICValue MSEValue
    IterationSummary "1529253.65122942" "1529265.34987705" "1.29997019294336e+129"
    IterationSummary "1529255.65122942" "1529273.19920087" "1.29902541076004e+129"
    
    IterationSummary "cont3" "1"
    IterationSummary "cont1" "2"
    [1] "Results Stored in $SelectionSummary"
    >
    >
    >
    >
    >
    >
    >
    > cleanEx()
    Warning in .Internal(gc(verbose, reset, full)) :
     closing unused connection 3 (D:/temp/RtmpukHfZE/RLIBS_c44c28153910/allan/extdata/SampleDataFile.csv)
    Error: connections left open:
     D:/temp/RtmpukHfZE/RLIBS_c44c28153910/allan/extdata/SampleDataFile.csv (file)
    Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 1.01
Check: examples
Result: ERROR
    Running examples in 'allan-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: allanVarSelect
    > ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear
    > ### Models
    > ### Aliases: allanVarSelect
    > ### Keywords: stepwise linear regression memory
    >
    > ### ** Examples
    >
    > #Get external data. For your own data skip this next line and replace all
    > #instance of SampleData with "YourFile.csv".
    > SampleData=system.file("extdata","SampleDataFile.csv", package = "allan")
    >
    > #fit smaller data to biglm object
    > columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))
    > datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)
    > datafeed(TRUE)
    > firstchunk<-datafeed(FALSE)
    >
    > #create a biglm model from the small chunk with all variables that will be consdered
    > #for variable selection.
    > bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
    >
    > #now run variable selection
    > FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria="MSE",silent=FALSE)
    [1] "Total memory usage for 1000 lines:"
    110560 bytes
    [1] "Chunksize for dataframe after adjustment factor:"
    [1] 429630
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Criteria:MSE"
    [1] "Iteration Result"
     AICValue BICValue MSEValue
     "1529253.65122942" "1529265.34987705" "1.29997019294336e+129"
    
     "cont3" "1"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Criteria:MSE"
    [1] "Iteration Result"
     AICValue BICValue MSEValue
     "1529255.65122942" "1529273.19920087" "1.29902541076004e+129"
    
     "cont1" "2"
    [1] "Final Results of Variable Selection:"
     AICValue BICValue MSEValue
    IterationSummary "1529253.65122942" "1529265.34987705" "1.29997019294336e+129"
    IterationSummary "1529255.65122942" "1529273.19920087" "1.29902541076004e+129"
    
    IterationSummary "cont3" "1"
    IterationSummary "cont1" "2"
    [1] "Results Stored in $SelectionSummary"
    >
    >
    >
    >
    >
    >
    >
    > cleanEx()
    Warning in .Internal(gc(verbose, reset, full)) :
     closing unused connection 3 (D:/temp/RtmpEHUBR4/RLIBS_12fec2e8ba2c/allan/extdata/SampleDataFile.csv)
    Error: connections left open:
     D:/temp/RtmpEHUBR4/RLIBS_12fec2e8ba2c/allan/extdata/SampleDataFile.csv (file)
    Execution halted
Flavor: r-release-windows-ix86+x86_64

Version: 1.01
Check: examples
Result: ERROR
    Running examples in 'allan-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: allanVarSelect
    > ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear
    > ### Models
    > ### Aliases: allanVarSelect
    > ### Keywords: stepwise linear regression memory
    >
    > ### ** Examples
    >
    > #Get external data. For your own data skip this next line and replace all
    > #instance of SampleData with "YourFile.csv".
    > SampleData=system.file("extdata","SampleDataFile.csv", package = "allan")
    >
    > #fit smaller data to biglm object
    > columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))
    > datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)
    > datafeed(TRUE)
    > firstchunk<-datafeed(FALSE)
    >
    > #create a biglm model from the small chunk with all variables that will be consdered
    > #for variable selection.
    > bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
    >
    > #now run variable selection
    > FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria="MSE",silent=FALSE)
    [1] "Total memory usage for 1000 lines:"
    110560 bytes
    [1] "Chunksize for dataframe after adjustment factor:"
    [1] 429630
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Criteria:MSE"
    [1] "Iteration Result"
     AICValue BICValue MSEValue
     "1529253.65122942" "1529265.34987705" "1.29997019294336e+129"
    
     "cont3" "1"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Iterating Through Dataset and Updating Coefficients"
    [1] "Criteria:MSE"
    [1] "Iteration Result"
     AICValue BICValue MSEValue
     "1529255.65122942" "1529273.19920087" "1.29902541076004e+129"
    
     "cont1" "2"
    [1] "Final Results of Variable Selection:"
     AICValue BICValue MSEValue
    IterationSummary "1529253.65122942" "1529265.34987705" "1.29997019294336e+129"
    IterationSummary "1529255.65122942" "1529273.19920087" "1.29902541076004e+129"
    
    IterationSummary "cont3" "1"
    IterationSummary "cont1" "2"
    [1] "Results Stored in $SelectionSummary"
    >
    >
    >
    >
    >
    >
    >
    > cleanEx()
    Warning in .Internal(gc(verbose, reset, full)) :
     closing unused connection 3 (D:/temp/RtmpE9PIOK/RLIBS_eb3860833110/allan/extdata/SampleDataFile.csv)
    Error: connections left open:
     D:/temp/RtmpE9PIOK/RLIBS_eb3860833110/allan/extdata/SampleDataFile.csv (file)
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64