I did explore tryCatch but wasn't successful.
However, I did just try your solution, William, and it worked!
I just had to modify this line in my function:
p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1])
to
p <- withWarnings((svyciprop(~grp, grp1, family=quasibinomial))[1])
Then I could use p[1] to get my estimated proportion.
Then I used:
ind <- length(attr(p, "warnings"))
print(ind)
if (ind > 0) {msg <- names(warnings()) }
else { msg <- "No warnings" }
overall[1,5] <- msg
to complete my table.
Thanks, again, William!
Jen
On Tue, Mar 6, 2018, 5:57 PM William Dunlap <wdun...@tibco.com> wrote:
> tryCatch() is good for catching errors but not so good for warnings, as
> it does not let you resume evaluating the expression that emitted
> the warning. withCallingHandlers(), with its companion invokeRestart(),
> lets you collect the warnings while letting the evaluation run to
> completion.
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
> On Tue, Mar 6, 2018 at 2:45 PM, Bert Gunter <bgunter.4...@gmail.com>
> wrote:
>
>> 1. I did not attempt to sort through your voluminous code. But I suspect
>> you are trying to reinvent wheels.
>>
>> 2. I don't understand this:
>>
>> "I've failed to find a solution after much searching of various R related
>> forums."
>>
>> A web search on "error handling in R" **immediately** brought up
>> ?tryCatch,
>> which I think is what you want.
>> If not, you should probably explain why it isn't, so that someone with
>> more
>> patience than I can muster will sort through your code to help.
>>
>> Cheers,
>> Bert
>>
>>
>>
>>
>>
>> Bert Gunter
>>
>> "The trouble with having an open mind is that people keep coming along and
>> sticking things into it."
>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>>
>> On Tue, Mar 6, 2018 at 2:26 PM, Jen <plessthanpointohf...@gmail.com>
>> wrote:
>>
>> > Hi, I am trying to automate the creation of tables for some simply
>> > analyses. There are lots and lots of tables, thus the creation of a
>> > user-defined function to make and output them to excel.
>> >
>> > My problem is that some of the analyses have convergence issues, which I
>> > want captured and included in the output so the folks looking at them
>> know
>> > how to view those estimates.
>> >
>> > I am successfully able to do this in a straightforward set of steps.
>> > However, once I place those steps inside a function it fails.
>> >
>> > Here's the code (sorry this is a long post):
>> >
>> > # create data
>> > wt <- rgamma(6065, 0.7057511981, 0.0005502062)
>> > grp <- sample(c(replicate(315, "Group1"), replicate(3672, "Group2"),
>> > replicate(1080, "Group3"), replicate(998, "Group4")))
>> > dta <- data.frame(grp, wt)
>> > head(dta)
>> > str(dta)
>> >
>> > # declare design
>> > my.svy <- svydesign(ids=~1, weights=~wt, data=dta)
>> >
>> > # subset
>> > grp1 <- subset(my.svy, grp == "Group1")
>> >
>> > # set options and clear old warnings
>> > options(warn=0)
>> > assign("last.warning", NULL, envir = baseenv())
>> >
>> > ## proportions and CIs
>> > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1])
>> >
>> > # save warnings
>> > wrn1 <- warnings(p)
>> >
>> > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1])
>> > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2])
>> >
>> > ## sample counts
>> > n <- unwtd.count(~grp, grp1)[1]
>> >
>> > ## combine into table
>> > overall <- data.frame(n, p, ci_l, ci_u)
>> > colnames(overall) <- c("counts", "Group1", "LL", "UL")
>> >
>> > ## add any warnings
>> > ind <- length(wrn1)
>> > ind
>> >
>> > if (ind == 0) { msg <- "No warnings" }
>> > if (ind > 0) {msg <- names(warnings()) }
>> > overall[1,5] <- msg
>> >
>> > print(overall)
>> >
>> > Here's the output from the above:
>> >
>> > > # set options and clear old warnings
>> > > options(warn=0)
>> > > assign("last.warning", NULL, envir = baseenv())
>> > >
>> > > ## proportions and CIs
>> > > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1])
>> > Warning message:
>> > glm.fit: algorithm did not converge
>> > >
>> > > # save warnings
>> > > wrn1 <- warnings(p)
>> > >
>> > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial),
>> 'ci')[1])
>> > Warning message:
>> > glm.fit: algorithm did not converge
>> > > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial),
>> 'ci')[2])
>> > Warning message:
>> > glm.fit: algorithm did not converge
>> > >
>> > > ## sample counts
>> > > n <- unwtd.count(~grp, grp1)[1]
>> > >
>> > > ## combine into table
>> > > overall <- data.frame(n, p, ci_l, ci_u)
>> > > colnames(overall) <- c("counts", "Group1", "LL", "UL")
>> > >
>> > > ## add any warnings
>> > > ind <- length(wrn1)
>> > > ind
>> > [1] 1
>> > >
>> > > if (ind == 0) { msg <- "No warnings" }
>> > > if (ind > 0) {msg <- names(warnings()) }
>> > > overall[1,5] <- msg
>> > >
>> > > print(overall)
>> > counts Group1 LL UL
>> > V5
>> > counts 315 2.364636e-12 2.002372e-12 2.792441e-12 glm.fit: algorithm
>> did
>> > not converge
>> >
>> > Here's the function:
>> >
>> > est <- function(var) {
>> >
>> > ## set up formula
>> > formula <- paste ("~", var)
>> >
>> > ## set options and clear old warning
>> > options(warn=0)
>> > assign("last.warning", NULL, envir = baseenv())
>> >
>> > ## proportions and CIs
>> > p <- ((svyciprop(as.formula(formula), grp1, family=quasibinomial))[1])
>> >
>> > ## save warnings
>> > wrn1 <- warnings(p)
>> >
>> > ci_l <- (confint(svyciprop(as.formula(formula) , grp1,
>> > family=quasibinomial), 'ci')[1])
>> > ci_u <- (confint(svyciprop(as.formula(formula) , grp1,
>> > family=quasibinomial), 'ci')[2])
>> >
>> > ## sample counts
>> > n <- unwtd.count(as.formula(formula), grp1)[1]
>> >
>> > ## combine into table
>> > overall <- data.frame(n, p, ci_l, ci_u)
>> > colnames(overall) <- c("counts", "Group1", "LL", "UL")
>> >
>> >
>> > ## add any warnings
>> > ind <- length(warnings(p))
>> > print(ind)
>> >
>> > if (ind == 0) { msg <- "No warnings" }
>> > if (ind > 0) {msg <- names(warnings()) }
>> > overall[1,5] <- msg
>> >
>> > print(overall)
>> >
>> > }
>> >
>> > Here's the output from running the function:
>> >
>> > > est("grp")
>> > [1] 0
>> > counts Group1 LL UL V5
>> > counts 315 2.364636e-12 2.002372e-12 2.792441e-12 No warnings
>> > Warning messages:
>> > 1: glm.fit: algorithm did not converge
>> > 2: glm.fit: algorithm did not converge
>> > 3: glm.fit: algorithm did not converge
>> >
>> > So, the warnings are showing up in the output at the end of the function
>> > but they're not being captured like they are when run outside of the
>> > function. Note the 0 output from print(ind) and V7 has "No warnings".
>> > I know a lot of things "behave" differently inside functions. Case in
>> > point, the use of "as.formula(var)" rather than just "~grp" being
>> passed to
>> > the function.
>> >
>> > I've failed to find a solution after much searching of various R related
>> > forums. I even posted this to stackoverflow but with no response. So, if
>> > anyone can help, I'd be appreciative.
>> >
>> > (sidenote: I used rgamma to create my sampling weights because that's
>> what
>> > most resembles the distribution of my weights and it's close enough to
>> > reproduce the convergence issue. If I used rnorm or even rlnorm or
>> rweibull
>> > I couldn't reproduce it. Just FYI.)
>> >
>> > Best,
>> >
>> > Jen
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > ______________________________________________
>> > R-help@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.
>> >
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@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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