[R] missing values error in if statement

Neha gupta neh@@bo|ogn@90 @end|ng |rom gm@||@com
Fri May 20 15:22:25 CEST 2022 

What do you mean by "fraction" ?

traceback()
4: readable_number(max_value - min_value, FALSE)
3: get_nice_ticks(lower_bound, upper_bound)
2: plot.fairness_object(fc)
1: plot(fc)

On Fri, May 20, 2022 at 3:18 PM PIKAL Petr <petr.pikal using precheza.cz> wrote:

> Hallo
>
> From what you say the error comes from
>
> > fraction <- NA
> > if(fraction <= 1) print(5)
> Error in if (fraction <= 1) print(5) :
>   missing value where TRUE/FALSE needed
> >
> so somewhere fraction is set to NA during your code.
>
> I would consult traceback, you could try debug used functions but maybe you
> should start with explainer, prot and privileged, if they are as expected
> by
> fairness_check
>
> > > fc= fairness_check(explainer,
> > >                           protected = prot,
> > >                    privileged = privileged)
>
> Cheers
> Petr
>
> > -----Original Message-----
> > From: R-help <r-help-bounces using r-project.org> On Behalf Of Neha gupta
> > Sent: Friday, May 20, 2022 3:03 PM
> > To: Jeff Newmiller <jdnewmil using dcn.davis.ca.us>
> > Cc: r-help mailing list <r-help using r-project.org>
> > Subject: Re: [R] missing values error in if statement
> >
> > Actually I am not very sure where exactly the error raised but when I run
> the
> > plot(fc) , it shows the error.
> >
> > I checked it online and people suggested that it may come with missing
> > values in 'if' or 'while; statements etc.
> >
> > I do not know how your code works and mine not.
> >
> > Best regards
> >
> > On Fri, May 20, 2022 at 10:16 AM Neha gupta
> > <neha.bologna90 using gmail.com>
> > wrote:
> >
> > > I am sorry.. The code is here and data is provided at the end of this
> > > email.
> > >
> > > data = readARFF("aho.arff")
> > >
> > > index= sample(1:nrow(data), 0.7*nrow(data)) train= data[index,] test=
> > > data[-index,]
> > >
> > > task = TaskClassif$new("data", backend = train, target = "isKilled")
> > > learner= lrn("classif.randomForest", predict_type = "prob") model=
> > > learner$train(task )
> > >
> > > ///explainer is created to identify a bias in a particular feature
> > > i.e. CE feature in this case
> > >
> > > explainer = explain_mlr3(model,
> > >                          data = test[,-15],
> > >                          y = as.numeric(test$isKilled)-1,
> > >                          label="RF")
> > > prot <- ifelse(test$CE == '2', 1, 0)       /// Error comes here
> > > privileged <- '1'
> > >
> > >
> > > fc= fairness_check(explainer,
> > >                           protected = prot,
> > >                    privileged = privileged)
> > > plot(fc)
> > >
> > >
> > > ////////////////////////////////////////// my data is
> > >
> > > dput(test)
> > > structure(list(DepthTree = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1), NumSubclass = c(0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 2), McCabe = c(1, 1, 1, 3, 3, 3, 3, 1, 2, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 2, 2, 2, 1, 2, 2, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
> > > 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 2, 5, 5, 5, 5, 5,
> > > 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2,
> > > 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 4, 4, 1, 1, 2, 2,
> > > 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1), LOC = c(3,
> > > 3, 4, 10, 10, 10, 10, 4, 5, 22, 22, 22, 22, 22, 22, 22, 22, 3, 3, 3,
> > > 3, 8, 8, 4, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
> > > 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 8, 8, 8, 16, 16, 16, 16, 16,
> > > 16, 16, 16, 16, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 7, 7,
> > > 7, 7, 18, 18, 18, 18, 18, 18, 15, 15, 15, 15, 15, 15, 15, 15, 6, 6, 6,
> > > 15, 15, 15, 15, 15, 15, 9, 9, 9, 9, 9, 9, 9, 4, 4, 3, 3, 3, 3, 4, 4,
> > > 4, 5, 8, 8, 3, 3, 3, 7, 7, 3, 3, 15, 15, 15, 15, 15, 15, 15, 15, 3, 3,
> > > 3, 4, 4, 4, 4, 8, 8, 8, 8, 4, 3), DepthNested = c(1, 1, 1, 2, 2, 2, 2,
> > > 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 2, 2, 1, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2,
> > > 2, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1,
> > > 1), CA = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> > > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,
> > > 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1), CE = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> > > 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2),
> > > Instability = c(0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.667, 0.667, 0.667,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0.667, 0, 0, 0, 0, 0.667,
> > > 0.667), numCovered = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 123,
> > > 54, 54, 54, 123, 54, 54, 39, 84, 54, 54, 15, 138, 189, 189, 189, 27,
> > > 51, 33, 6, 27, 27, 150, 150, 150, 54, 150, 54, 54, 150, 117, 51, 66,
> > > 60, 15, 15, 72, 12, 45, 255, 255, 129, 129, 129, 0, 129, 0, 0, 6, 6,
> > > 6, 303, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 12, 12, 12, 18, 12,
> > > 12, 48, 12, 1557, 48, 12, 171, 0, 0, 0, 141, 141, 45, 141, 18, 39),
> > > operator = structure(c(4L, 13L, 13L, 1L, 4L, 9L, 12L, 4L, 11L, 4L, 7L,
> > > 8L, 8L, 8L, 8L, 8L, 9L, 7L, 8L, 8L, 6L, 7L, 8L, 4L, 1L, 2L, 3L, 4L,
> > > 7L, 8L, 8L, 8L, 8L, 8L, 9L, 11L, 12L, 12L, 4L, 6L, 7L, 7L, 7L, 8L, 8L,
> > > 8L, 8L, 8L, 6L, 9L, 9L, 4L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 10L, 1L, 1L,
> > > 1L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 13L, 13L, 7L, 8L, 8L, 9L, 8L, 8L, 8L,
> > > 8L, 9L, 10L, 1L, 4L, 4L, 6L, 7L, 8L, 8L, 8L, 9L, 10L, 10L, 7L, 8L, 8L,
> > > 10L, 11L, 11L, 7L, 8L, 4L, 8L, 9L, 10L, 10L, 4L, 10L, 7L, 7L, 10L, 6L,
> > > 8L, 8L, 10L, 8L, 8L, 10L, 9L, 8L, 10L, 7L, 7L, 13L, 2L, 2L, 2L, 8L,
> > > 8L, 8L, 8L, 8L, 11L, 10L, 10L, 13L, 13L, 8L, 8L, 8L, 6L, 7L, 8L, 10L,
> > > 13L, 13L), .Label = c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7",
> > > "T8", "T9", "T10", "T11", "T12", "T13", "T14", "T15"), class =
> > > "factor"), methodReturn = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L,
> > > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
> > > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> > > 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L,
> > > 3L, 1L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 1L,
> > > 2L, 2L), .Label = c("I", "V", "Z", "method", "D", "[D", "[[D", "J",
> > > "[I", "C", "[J", "[C", "[S", "F", "[F", "[B", "S", "B", "[Z", "[[S",
> > > "[[B", "[[Z"), class = "factor"), numTestsCover = c(16, 16, 16, 15,
> > > 15, 16, 15, 4, 16, 16, 15, 16, 15, 15, 15, 15, 15, 3, 3, 3, 2, 16, 11,
> > > 4, 16, 3, 16, 16, 16, 16, 16, 4, 16, 16, 16, 4, 16, 16, 3, 3, 3, 2, 4,
> > > 3, 2, 1, 4, 1, 15, 16, 15, 2, 3, 2, 3, 3, 2, 2, 2, 3, 4, 5, 5, 5, 4,
> > > 5, 5, 4, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 5, 4, 5,
> > > 5, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 6, 6, 6, 0, 6, 0, 0, 2, 2, 2,
> > > 7, 0, 0, 0, 15, 16, 16, 16, 15, 17, 17, 17, 15, 5, 4, 4, 4, 3, 4, 4,
> > > 3, 4, 16, 16, 4, 17, 0, 0, 0, 5, 5, 3, 5, 2, 3), mutantAssert = c(55,
> > > 55, 55, 55, 55, 55, 55, 13, 55, 55, 55, 55, 55, 55, 55, 55, 55, 9, 9,
> > > 9, 9, 55, 41, 13, 55, 5, 55, 55, 55, 55, 55, 13, 55, 55, 55, 13, 55,
> > > 55, 13, 13, 13, 8, 13, 13, 8, 4, 13, 4, 55, 55, 55, 9, 9, 9, 9, 9, 9,
> > > 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 5,
> > > 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 14, 14,
> > > 14, 0, 14, 0, 0, 2, 2, 2, 15, 0, 0, 0, 55, 58, 58, 55, 55, 58, 58, 58,
> > > 55, 9, 6, 6, 6, 6, 6, 6, 6, 6, 55, 55, 13, 57, 0, 0, 0, 11, 11, 7, 11,
> > > 9, 9), classAssert = c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10,
> > > 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> > > 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0), isKilled
> > > = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> > > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> > > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> > > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
> > > 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> > > 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
> > > 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
> > > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
> > > 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("yes",
> > > "no"), class = "factor")), row.names = c(3L, 4L, 5L, 7L, 9L, 17L, 20L,
> > > 21L, 26L, 28L, 32L, 33L, 40L, 43L, 45L, 49L, 54L, 62L, 64L, 65L, 70L,
> > > 75L, 77L, 81L, 84L, 86L, 88L, 89L, 93L, 95L, 97L, 99L, 101L, 102L,
> > > 106L, 111L, 112L, 113L, 118L, 122L, 125L, 128L, 129L, 134L, 141L,
> > > 142L, 143L, 146L, 156L, 168L, 169L, 174L, 178L, 179L, 182L, 184L,
> > > 185L, 192L, 193L, 195L, 197L, 198L, 199L, 203L, 205L, 209L, 211L,
> > > 215L, 216L, 218L, 220L, 224L, 227L, 228L, 230L, 233L, 243L, 244L,
> > > 246L, 247L, 251L, 252L, 259L, 262L, 263L, 265L, 270L, 273L, 274L,
> > > 275L, 284L, 285L, 286L, 291L, 296L, 297L, 300L, 301L, 306L, 314L,
> > > 316L, 322L, 328L, 331L, 332L, 333L, 336L, 341L, 346L, 347L, 348L,
> > > 360L, 363L, 366L, 367L, 383L, 392L, 395L, 398L, 404L, 408L, 409L,
> > > 410L, 420L, 421L, 426L, 428L, 434L, 437L, 438L, 440L, 441L, 447L,
> > > 449L, 450L, 452L, 454L, 457L, 458L, 459L, 463L, 465L, 469L, 471L,
> > > 472L, 483L), class = "data.frame")
> > >
> > > On Fri, May 20, 2022 at 12:20 AM Jeff Newmiller
> > > <jdnewmil using dcn.davis.ca.us>
> > > wrote:
> > >
> > >> Not reproducible. Posted HTML.
> > >>
> > >> On May 19, 2022 2:30:58 PM PDT, Neha gupta
> > <neha.bologna90 using gmail.com>
> > >> wrote:
> > >> >Why do I get the following error when my variable in the 'if
> statement'
> > >> has
> > >> >no missing values.
> > >> >
> > >> >I check with is.na(my variable) and it has no missing values
> > >> >
> > >> >Error in if (fraction <= 1) { : missing value where TRUE/FALSE
> > >> >needed
> > >> >
> > >> >Best regards
> > >> >
> > >> >       [[alternative HTML version deleted]]
> > >> >
> > >> >______________________________________________
> > >> >R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > >> >https://stat.ethz.ch/mailman/listinfo/r-help
> > >> >PLEASE do read the posting guide
> > >> http://www.R-project.org/posting-guide.html
> > >> >and provide commented, minimal, self-contained, reproducible code.
> > >>
> > >> --
> > >> Sent from my phone. Please excuse my brevity.
> > >>
> > >
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-
> > guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>

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