Hi William, Thanks, I'll give that a shot. I tried using withCallingHandlers without success but II admit I'm not familiar with it and may have used it wrong.

I'll report back. Jen On Tue, Mar 6, 2018, 5:42 PM William Dunlap <wdun...@tibco.com> wrote: > You can capture warnings by using withCallingHandlers. Here is an > example, > its help file has more information. > > dataList <- list( > A = data.frame(y=c(TRUE,TRUE,TRUE,FALSE,FALSE), x=1:5), > B = data.frame(y=c(TRUE,TRUE,FALSE,TRUE,FALSE), x=1:5), > C = data.frame(y=c(FALSE,FALSE,TRUE,TRUE,TRUE), x=1:5)) > > withWarnings <- function(expr) { > .warnings <- NULL # warning handler will append to this using '<<-' > value <- withCallingHandlers(expr, > warning=function(e) { > .warnings <<- c(.warnings, > conditionMessage(e)) > invokeRestart("muffleWarning") > }) > structure(value, warnings=.warnings) > } > z <- lapply(dataList, function(data) withWarnings(coef(glm(data=data, y ~ > x, family=binomial)))) > z > > The last line produces > > > z > $A > (Intercept) x > 160.80782 -45.97184 > attr(,"warnings") > [1] "glm.fit: fitted probabilities numerically 0 or 1 occurred" > > $B > (Intercept) x > 3.893967 -1.090426 > > $C > (Intercept) x > -115.02321 45.97184 > attr(,"warnings") > [1] "glm.fit: fitted probabilities numerically 0 or 1 occurred" > > and lapply(z, attr, "warnings") will give you the warnings themselves. > > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > 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.