Last updated on 2020-03-13 16:46:45 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.1.0 | 5.04 | 21.82 | 26.86 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.1.0 | 3.20 | 17.44 | 20.64 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.1.0 | 34.30 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.1.0 | 30.34 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.1.0 | 13.00 | 39.00 | 52.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.1.0 | 13.00 | 39.00 | 52.00 | OK | |
| r-patched-linux-x86_64 | 1.1.0 | 3.55 | 19.75 | 23.30 | OK | |
| r-patched-solaris-x86 | 1.1.0 | 58.30 | OK | |||
| r-release-linux-x86_64 | 1.1.0 | 3.59 | 19.78 | 23.37 | OK | |
| r-release-windows-ix86+x86_64 | 1.1.0 | 13.00 | 39.00 | 52.00 | OK | |
| r-release-osx-x86_64 | 1.1.0 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 1.1.0 | 10.00 | 37.00 | 47.00 | OK | |
| r-oldrel-osx-x86_64 | 1.1.0 | OK |
Version: 1.1.0
Check: examples
Result: ERROR
Running examples in 'HotDeckImputation-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: impute.NN_HD
> ### Title: The Nearest Neighbor Hot Deck Algorithms
> ### Aliases: impute.NN_HD
> ### Keywords: NA manip optimize multivariate
>
> ### ** Examples
>
> #Set the random seed to an arbitrary number
> set.seed(421)
>
> #Generate random integer matrix size 10x4
> Y<-matrix(sample(x=1:100,size=10*4),nrow=10)
>
> #remove 5 values, ensuring one complete covariate and 5 donors
> Y[-c(1:5),-1][sample(1:15,size=5)]<-NA
>
> #Impute using various different (arbitrarily chosen) settings
> impute.NN_HD(DATA=Y,distance="man",weights="var")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
HotDeckImputation
--- call from context ---
impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- call from argument ---
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data, weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
} else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
--- R stacktrace ---
where 1: impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (DATA = NULL, distance = "man", weights = "range", attributes = "sim",
comp = "rw_dist", donor_limit = Inf, optimal_donor = "no",
list_donors_recipients = NULL, diagnose = NULL)
{
arguements <- list(distance = distance, weights = weights,
attributes = attributes, comp = comp, donor_limit = donor_limit,
optimal_donor = optimal_donor, list_donors_recipients = list_donors_recipients,
diagnose = diagnose)
original_data <- DATA
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data,
weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
}
else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
n <- dim(DATA)[1]
m <- dim(DATA)[2]
if (attributes == "sim") {
if (is.null(list_donors_recipients)) {
list_donors_recipients <- .create.dr_list(DATA = DATA,
attributes = attributes)
}
if (!is.matrix(distance)) {
distance <- .calculate.distancematrix(DATA = DATA,
distance = distance, weights = weights, comp = comp,
list_donors_recipients)
}
donor_limit <- .create.donorlimit(donor_limit = donor_limit,
list_donors_recipients = list_donors_recipients)
list_recip_donor <- .match.donor_recipient(list_donors_recipients = list_donors_recipients,
distance = distance, optimal_donor = optimal_donor,
donor_limit = donor_limit)
envir <- as.environment(".GlobalEnv")
.dump_diagnostics(diagnose = diagnose, diagnostics = list(arguements = arguements,
distances = distance, list_donors_recipients = list_recip_donor),
envir = envir)
DATA <- .impute(DATA = original_data, list_recip_donor = list_recip_donor,
recoded = recoded)
}
else {
stop("Unimplemented way of handling attributes used: \"seq \"")
}
return(DATA)
}
<bytecode: 0x1d819b8>
<environment: namespace:HotDeckImputation>
--- function search by body ---
Function impute.NN_HD in namespace HotDeckImputation has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(DATA) == "data.frame") { :
the condition has length > 1
Calls: impute.NN_HD
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.1.0
Check: examples
Result: ERROR
Running examples in ‘HotDeckImputation-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: impute.NN_HD
> ### Title: The Nearest Neighbor Hot Deck Algorithms
> ### Aliases: impute.NN_HD
> ### Keywords: NA manip optimize multivariate
>
> ### ** Examples
>
> #Set the random seed to an arbitrary number
> set.seed(421)
>
> #Generate random integer matrix size 10x4
> Y<-matrix(sample(x=1:100,size=10*4),nrow=10)
>
> #remove 5 values, ensuring one complete covariate and 5 donors
> Y[-c(1:5),-1][sample(1:15,size=5)]<-NA
>
> #Impute using various different (arbitrarily chosen) settings
> impute.NN_HD(DATA=Y,distance="man",weights="var")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
HotDeckImputation
--- call from context ---
impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- call from argument ---
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data, weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
} else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
--- R stacktrace ---
where 1: impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (DATA = NULL, distance = "man", weights = "range", attributes = "sim",
comp = "rw_dist", donor_limit = Inf, optimal_donor = "no",
list_donors_recipients = NULL, diagnose = NULL)
{
arguements <- list(distance = distance, weights = weights,
attributes = attributes, comp = comp, donor_limit = donor_limit,
optimal_donor = optimal_donor, list_donors_recipients = list_donors_recipients,
diagnose = diagnose)
original_data <- DATA
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data,
weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
}
else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
n <- dim(DATA)[1]
m <- dim(DATA)[2]
if (attributes == "sim") {
if (is.null(list_donors_recipients)) {
list_donors_recipients <- .create.dr_list(DATA = DATA,
attributes = attributes)
}
if (!is.matrix(distance)) {
distance <- .calculate.distancematrix(DATA = DATA,
distance = distance, weights = weights, comp = comp,
list_donors_recipients)
}
donor_limit <- .create.donorlimit(donor_limit = donor_limit,
list_donors_recipients = list_donors_recipients)
list_recip_donor <- .match.donor_recipient(list_donors_recipients = list_donors_recipients,
distance = distance, optimal_donor = optimal_donor,
donor_limit = donor_limit)
envir <- as.environment(".GlobalEnv")
.dump_diagnostics(diagnose = diagnose, diagnostics = list(arguements = arguements,
distances = distance, list_donors_recipients = list_recip_donor),
envir = envir)
DATA <- .impute(DATA = original_data, list_recip_donor = list_recip_donor,
recoded = recoded)
}
else {
stop("Unimplemented way of handling attributes used: \"seq \"")
}
return(DATA)
}
<bytecode: 0x5645f38522f8>
<environment: namespace:HotDeckImputation>
--- function search by body ---
Function impute.NN_HD in namespace HotDeckImputation has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(DATA) == "data.frame") { :
the condition has length > 1
Calls: impute.NN_HD
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.1.0
Check: compiled code
Result: NOTE
File ‘HotDeckImputation/libs/HotDeckImputation.so’:
Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’
It is good practice to register native routines and to disable symbol
search.
See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.1.0
Check: examples
Result: ERROR
Running examples in ‘HotDeckImputation-Ex.R’ failed
The error most likely occurred in:
> ### Name: impute.NN_HD
> ### Title: The Nearest Neighbor Hot Deck Algorithms
> ### Aliases: impute.NN_HD
> ### Keywords: NA manip optimize multivariate
>
> ### ** Examples
>
> #Set the random seed to an arbitrary number
> set.seed(421)
>
> #Generate random integer matrix size 10x4
> Y<-matrix(sample(x=1:100,size=10*4),nrow=10)
>
> #remove 5 values, ensuring one complete covariate and 5 donors
> Y[-c(1:5),-1][sample(1:15,size=5)]<-NA
>
> #Impute using various different (arbitrarily chosen) settings
> impute.NN_HD(DATA=Y,distance="man",weights="var")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
HotDeckImputation
--- call from context ---
impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- call from argument ---
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data, weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
} else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
--- R stacktrace ---
where 1: impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (DATA = NULL, distance = "man", weights = "range", attributes = "sim",
comp = "rw_dist", donor_limit = Inf, optimal_donor = "no",
list_donors_recipients = NULL, diagnose = NULL)
{
arguements <- list(distance = distance, weights = weights,
attributes = attributes, comp = comp, donor_limit = donor_limit,
optimal_donor = optimal_donor, list_donors_recipients = list_donors_recipients,
diagnose = diagnose)
original_data <- DATA
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data,
weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
}
else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
n <- dim(DATA)[1]
m <- dim(DATA)[2]
if (attributes == "sim") {
if (is.null(list_donors_recipients)) {
list_donors_recipients <- .create.dr_list(DATA = DATA,
attributes = attributes)
}
if (!is.matrix(distance)) {
distance <- .calculate.distancematrix(DATA = DATA,
distance = distance, weights = weights, comp = comp,
list_donors_recipients)
}
donor_limit <- .create.donorlimit(donor_limit = donor_limit,
list_donors_recipients = list_donors_recipients)
list_recip_donor <- .match.donor_recipient(list_donors_recipients = list_donors_recipients,
distance = distance, optimal_donor = optimal_donor,
donor_limit = donor_limit)
envir <- as.environment(".GlobalEnv")
.dump_diagnostics(diagnose = diagnose, diagnostics = list(arguements = arguements,
distances = distance, list_donors_recipients = list_recip_donor),
envir = envir)
DATA <- .impute(DATA = original_data, list_recip_donor = list_recip_donor,
recoded = recoded)
}
else {
stop("Unimplemented way of handling attributes used: \"seq \"")
}
return(DATA)
}
<bytecode: 0x17a1840>
<environment: namespace:HotDeckImputation>
--- function search by body ---
Function impute.NN_HD in namespace HotDeckImputation has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(DATA) == "data.frame") { :
the condition has length > 1
Calls: impute.NN_HD
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.1.0
Check: examples
Result: ERROR
Running examples in ‘HotDeckImputation-Ex.R’ failed
The error most likely occurred in:
> ### Name: impute.NN_HD
> ### Title: The Nearest Neighbor Hot Deck Algorithms
> ### Aliases: impute.NN_HD
> ### Keywords: NA manip optimize multivariate
>
> ### ** Examples
>
> #Set the random seed to an arbitrary number
> set.seed(421)
>
> #Generate random integer matrix size 10x4
> Y<-matrix(sample(x=1:100,size=10*4),nrow=10)
>
> #remove 5 values, ensuring one complete covariate and 5 donors
> Y[-c(1:5),-1][sample(1:15,size=5)]<-NA
>
> #Impute using various different (arbitrarily chosen) settings
> impute.NN_HD(DATA=Y,distance="man",weights="var")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
HotDeckImputation
--- call from context ---
impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- call from argument ---
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data, weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
} else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
--- R stacktrace ---
where 1: impute.NN_HD(DATA = Y, distance = "man", weights = "var")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (DATA = NULL, distance = "man", weights = "range", attributes = "sim",
comp = "rw_dist", donor_limit = Inf, optimal_donor = "no",
list_donors_recipients = NULL, diagnose = NULL)
{
arguements <- list(distance = distance, weights = weights,
attributes = attributes, comp = comp, donor_limit = donor_limit,
optimal_donor = optimal_donor, list_donors_recipients = list_donors_recipients,
diagnose = diagnose)
original_data <- DATA
if (class(DATA) == "data.frame") {
DATA <- .DATA_recode(DATA)
recoded <- TRUE
weights <- .create.weightsvector(DATA = original_data,
weights = weights)
weights <- weights[DATA$weightsindex]
DATA <- DATA$DATA_recode
}
else {
DATA <- data.matrix(DATA)
weights <- .create.weightsvector(DATA = DATA, weights = weights)
recoded <- FALSE
}
n <- dim(DATA)[1]
m <- dim(DATA)[2]
if (attributes == "sim") {
if (is.null(list_donors_recipients)) {
list_donors_recipients <- .create.dr_list(DATA = DATA,
attributes = attributes)
}
if (!is.matrix(distance)) {
distance <- .calculate.distancematrix(DATA = DATA,
distance = distance, weights = weights, comp = comp,
list_donors_recipients)
}
donor_limit <- .create.donorlimit(donor_limit = donor_limit,
list_donors_recipients = list_donors_recipients)
list_recip_donor <- .match.donor_recipient(list_donors_recipients = list_donors_recipients,
distance = distance, optimal_donor = optimal_donor,
donor_limit = donor_limit)
envir <- as.environment(".GlobalEnv")
.dump_diagnostics(diagnose = diagnose, diagnostics = list(arguements = arguements,
distances = distance, list_donors_recipients = list_recip_donor),
envir = envir)
DATA <- .impute(DATA = original_data, list_recip_donor = list_recip_donor,
recoded = recoded)
}
else {
stop("Unimplemented way of handling attributes used: \"seq \"")
}
return(DATA)
}
<bytecode: 0x2942bc0>
<environment: namespace:HotDeckImputation>
--- function search by body ---
Function impute.NN_HD in namespace HotDeckImputation has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(DATA) == "data.frame") { :
the condition has length > 1
Calls: impute.NN_HD
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc