Hello,
Sorry, bug. In both by instructions it's boot_mean_se, not bootprop.
boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R = R)
boot_year_area <- by(DaT,
INDICES = list(Year = DaT$Year, Area = DaT$Area),
FUN = boot_mean_se,
statistic = bootprop, R = R)
Hope this helps,
Rui Barradas
?s 16:48 de 31/10/21, Rui Barradas escreveu:> Hello,
>
> Try to aggregate with ?by.
>
>
> bootprop <- function(data, index){
> ? d <- data[index, ]
> ? sum(d[["bothTimes"]], na.rm =
TRUE)/sum(d[["total"]], na.rm = TRUE)#
> }
> boot_mean_se <- function(data, statistic, R){
> ? b <- boot::boot(DaT, bootprop, R = R)
> ? c(bootMean = mean(b$t), bootSE = sd(b$t))
> }
>
> boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R =
R)
> boot_year_area <- by(DaT,
> ???????????????????? INDICES = list(Year = DaT$Year, Area = DaT$Area),
> ???????????????????? FUN = boot_mean_se,
> ???????????????????? statistic = bootprop, R = R)
> boot_year
> boot_year_area
>
> boot_year <- do.call(rbind, boot_year)
>
> d <- dimnames(boot_year_area)
> boot_year_area <- cbind(Reduce(expand.grid, rev(d))[2:1],
> ??????????????????????? do.call(rbind, boot_year_area))
> names(boot_year_area)[1:2] <- names(d)
> boot_year_area
>
>
> Hope this helps,
>
> Rui Barradas
>
> ?s 11:47 de 31/10/21, Marna Wagley escreveu:
>> Hi R users,
>> I was trying to extract the bootstrap mean and its SE by group but I
have
>> been doing? it by separating the group manually. The data set is big so
>> doing it manually is a kind of tedious task. I am wondering whether
there
>> is a possibility to do it by creating a loop. I am weak in writing loop
>> functions. I am attaching an example data and how I performed the
>> analysis, see below.
>> Thanks for your help.
>> Sincerely,
>> MW
>> ####
>> library(boot)
>> DaT<-structure(list(bothTimes = c(0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
>> 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L),
>> total = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Area =
c("A",
>> "A", "A", "A", "A",
"A", "B", "B", "B", "B",
"B", "B", "A", "A",
>> "A", "A", "A", "A",
"B", "B", "B", "B", "B",
"B"), Year = c(2015L, 2015L,
>> 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
>> 2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
>> 2016L, 2016L, 2016L, 2016L, 2016L)), class = "data.frame",
row.names >> c(NA, -24L))
>>
>> head(DaT)
>> R=100
>> bootprop <- function(data, index){
>> ?? d <- data[index, ]
>> ?? sum(d[["bothTimes"]], na.rm =
TRUE)/sum(d[["total"]], na.rm = TRUE)#
>> }
>>
>> ###################
>> #2015
>> ###################
>> #-----Year2015_pooled
>> Y2015_pooled<-subset(DaT, DaT$Year=="2015")
>> Y2015_pooled_boot <- boot(Y2015_pooled, bootprop, R)
>> boot_Y2015_pooled<-data.frame(Year="2015", Area=
"Pooled", bootMean>> Y2015_pooled_boot$t0,
SE=sd(Y2015_pooled_boot$t))
>> #-----Year2015_AreaA
>> Y2015_A<-subset(DaT, DaT$Year=="2015" &
DaT$Area=="A")
>> Y2015_A_boot <- boot(Y2015_A, bootprop, R)
>> boot_Y2015_A<-data.frame(Year="2015", Area= "A",
bootMean=
>> Y2015_A_boot$t0,
>> SE=sd(Y2015_A_boot$t))
>> #----Year2015_AreaB
>> Y2015_B<-subset(DaT, DaT$Year=="2015" &
DaT$Area=="B")
>> Y2015_B_boot <- boot(Y2015_B, bootprop, R)
>> boot_Y2015_B<-data.frame(Year="2015", Area= "B",
bootMean=
>> Y2015_B_boot$t0,
>> SE=sd(Y2015_B_boot$t))
>> ###################
>> #2016
>> ###################
>> #-----Year2016_pooled
>> Y2016_pooled<-subset(DaT, DaT$Year=="2016")
>> Y2016_pooled_boot <- boot(Y2016_pooled, bootprop, R)
>> boot_Y2016_pooled<-data.frame(Year="2016", Area=
"Pooled", bootMean>> Y2016_pooled_boot$t0,
SE=sd(Y2016_pooled_boot$t))
>>
>> #-----Year2016_AreaA
>> Y2016_A<-subset(DaT, DaT$Year=="2016" &
DaT$Area=="A")
>> Y2016_A_boot <- boot(Y2016_A, bootprop, R)
>>
>> boot_Y2016_A<-data.frame(Year="2016", Area= "A",
bootMean=
>> Y2016_A_boot$t0,
>> SE=sd(Y2016_A_boot$t))
>> #----Year2016_AreaB
>> Y2016_B<-subset(DaT, DaT$Year=="2016" &
DaT$Area=="B")
>> Y2016_B_boot <- boot(Y2016_B, bootprop, R)
>> boot_Y2016_B<-data.frame(Year="2016", Area= "B",
bootMean=
>> Y2016_B_boot$t0,
>> SE=sd(Y2016_B_boot$t))
>>
>> ## output data.matrix
>>
BootMean_All<-rbind(boot_Y2015_pooled,boot_Y2015_A,boot_Y2015_B,boot_Y2016_pooled,boot_Y2016_A,boot_Y2016_B)
>>
>> BootMean_All
>>
>> ????[[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at 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.
>>
>
> ______________________________________________
> R-help at 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.
Hello, Now I'm spamming the list, not one of my days. My first post was right, there was no bug and the 2nd one was exactly the same code, it corrected nothing at all. Apologies for the noise, Rui Barradas ?s 16:55 de 31/10/21, Rui Barradas escreveu:> Hello, > > Sorry, bug. In both by instructions it's boot_mean_se, not bootprop. > > > boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R = R) > boot_year_area <- by(DaT, > ???????????????????? INDICES = list(Year = DaT$Year, Area = DaT$Area), > ???????????????????? FUN = boot_mean_se, > ???????????????????? statistic = bootprop, R = R) > > > Hope this helps, > > Rui Barradas > > ?s 16:48 de 31/10/21, Rui Barradas escreveu: >> Hello, >> >> Try to aggregate with ?by. >> >> >> bootprop <- function(data, index){ >> ?? d <- data[index, ] >> ?? sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)# >> } >> boot_mean_se <- function(data, statistic, R){ >> ?? b <- boot::boot(DaT, bootprop, R = R) >> ?? c(bootMean = mean(b$t), bootSE = sd(b$t)) >> } >> >> boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R = R) >> boot_year_area <- by(DaT, >> ????????????????????? INDICES = list(Year = DaT$Year, Area = DaT$Area), >> ????????????????????? FUN = boot_mean_se, >> ????????????????????? statistic = bootprop, R = R) >> boot_year >> boot_year_area >> >> boot_year <- do.call(rbind, boot_year) >> >> d <- dimnames(boot_year_area) >> boot_year_area <- cbind(Reduce(expand.grid, rev(d))[2:1], >> ???????????????????????? do.call(rbind, boot_year_area)) >> names(boot_year_area)[1:2] <- names(d) >> boot_year_area >> >> >> Hope this helps, >> >> Rui Barradas >> >> ?s 11:47 de 31/10/21, Marna Wagley escreveu: >>> Hi R users, >>> I was trying to extract the bootstrap mean and its SE by group but I >>> have >>> been doing? it by separating the group manually. The data set is big so >>> doing it manually is a kind of tedious task. I am wondering whether >>> there >>> is a possibility to do it by creating a loop. I am weak in writing loop >>> functions. I am attaching an example data and how I performed the >>> analysis, see below. >>> Thanks for your help. >>> Sincerely, >>> MW >>> #### >>> library(boot) >>> DaT<-structure(list(bothTimes = c(0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, >>> 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L), >>> total = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, >>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Area = c("A", >>> "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "A", "A", >>> "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"), Year = c(2015L, >>> 2015L, >>> 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, >>> 2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, >>> 2016L, 2016L, 2016L, 2016L, 2016L)), class = "data.frame", row.names >>> c(NA, -24L)) >>> >>> head(DaT) >>> R=100 >>> bootprop <- function(data, index){ >>> ?? d <- data[index, ] >>> ?? sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)# >>> } >>> >>> ################### >>> #2015 >>> ################### >>> #-----Year2015_pooled >>> Y2015_pooled<-subset(DaT, DaT$Year=="2015") >>> Y2015_pooled_boot <- boot(Y2015_pooled, bootprop, R) >>> boot_Y2015_pooled<-data.frame(Year="2015", Area= "Pooled", bootMean>>> Y2015_pooled_boot$t0, SE=sd(Y2015_pooled_boot$t)) >>> #-----Year2015_AreaA >>> Y2015_A<-subset(DaT, DaT$Year=="2015" & DaT$Area=="A") >>> Y2015_A_boot <- boot(Y2015_A, bootprop, R) >>> boot_Y2015_A<-data.frame(Year="2015", Area= "A", bootMean= >>> Y2015_A_boot$t0, >>> SE=sd(Y2015_A_boot$t)) >>> #----Year2015_AreaB >>> Y2015_B<-subset(DaT, DaT$Year=="2015" & DaT$Area=="B") >>> Y2015_B_boot <- boot(Y2015_B, bootprop, R) >>> boot_Y2015_B<-data.frame(Year="2015", Area= "B", bootMean= >>> Y2015_B_boot$t0, >>> SE=sd(Y2015_B_boot$t)) >>> ################### >>> #2016 >>> ################### >>> #-----Year2016_pooled >>> Y2016_pooled<-subset(DaT, DaT$Year=="2016") >>> Y2016_pooled_boot <- boot(Y2016_pooled, bootprop, R) >>> boot_Y2016_pooled<-data.frame(Year="2016", Area= "Pooled", bootMean>>> Y2016_pooled_boot$t0, SE=sd(Y2016_pooled_boot$t)) >>> >>> #-----Year2016_AreaA >>> Y2016_A<-subset(DaT, DaT$Year=="2016" & DaT$Area=="A") >>> Y2016_A_boot <- boot(Y2016_A, bootprop, R) >>> >>> boot_Y2016_A<-data.frame(Year="2016", Area= "A", bootMean= >>> Y2016_A_boot$t0, >>> SE=sd(Y2016_A_boot$t)) >>> #----Year2016_AreaB >>> Y2016_B<-subset(DaT, DaT$Year=="2016" & DaT$Area=="B") >>> Y2016_B_boot <- boot(Y2016_B, bootprop, R) >>> boot_Y2016_B<-data.frame(Year="2016", Area= "B", bootMean= >>> Y2016_B_boot$t0, >>> SE=sd(Y2016_B_boot$t)) >>> >>> ## output data.matrix >>> BootMean_All<-rbind(boot_Y2015_pooled,boot_Y2015_A,boot_Y2015_B,boot_Y2016_pooled,boot_Y2016_A,boot_Y2016_B) >>> >>> BootMean_All >>> >>> ????[[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help at 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. >>> >> >> ______________________________________________ >> R-help at 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. > > ______________________________________________ > R-help at 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.
Thank you Rui. It helped a lot. -MW On Sun, Oct 31, 2021 at 10:16 AM Rui Barradas <ruipbarradas at sapo.pt> wrote:> Hello, > > Now I'm spamming the list, not one of my days. > > My first post was right, there was no bug and the 2nd one was exactly > the same code, it corrected nothing at all. > > Apologies for the noise, > > Rui Barradas > > ?s 16:55 de 31/10/21, Rui Barradas escreveu: > > Hello, > > > > Sorry, bug. In both by instructions it's boot_mean_se, not bootprop. > > > > > > boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R = R) > > boot_year_area <- by(DaT, > > INDICES = list(Year = DaT$Year, Area = DaT$Area), > > FUN = boot_mean_se, > > statistic = bootprop, R = R) > > > > > > Hope this helps, > > > > Rui Barradas > > > > ?s 16:48 de 31/10/21, Rui Barradas escreveu: > >> Hello, > >> > >> Try to aggregate with ?by. > >> > >> > >> bootprop <- function(data, index){ > >> d <- data[index, ] > >> sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)# > >> } > >> boot_mean_se <- function(data, statistic, R){ > >> b <- boot::boot(DaT, bootprop, R = R) > >> c(bootMean = mean(b$t), bootSE = sd(b$t)) > >> } > >> > >> boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R > R) > >> boot_year_area <- by(DaT, > >> INDICES = list(Year = DaT$Year, Area = DaT$Area), > >> FUN = boot_mean_se, > >> statistic = bootprop, R = R) > >> boot_year > >> boot_year_area > >> > >> boot_year <- do.call(rbind, boot_year) > >> > >> d <- dimnames(boot_year_area) > >> boot_year_area <- cbind(Reduce(expand.grid, rev(d))[2:1], > >> do.call(rbind, boot_year_area)) > >> names(boot_year_area)[1:2] <- names(d) > >> boot_year_area > >> > >> > >> Hope this helps, > >> > >> Rui Barradas > >> > >> ?s 11:47 de 31/10/21, Marna Wagley escreveu: > >>> Hi R users, > >>> I was trying to extract the bootstrap mean and its SE by group but I > >>> have > >>> been doing it by separating the group manually. The data set is big so > >>> doing it manually is a kind of tedious task. I am wondering whether > >>> there > >>> is a possibility to do it by creating a loop. I am weak in writing loop > >>> functions. I am attaching an example data and how I performed the > >>> analysis, see below. > >>> Thanks for your help. > >>> Sincerely, > >>> MW > >>> #### > >>> library(boot) > >>> DaT<-structure(list(bothTimes = c(0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, > >>> 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L), > >>> total = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > >>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Area = c("A", > >>> "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "A", "A", > >>> "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"), Year = c(2015L, > >>> 2015L, > >>> 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, > >>> 2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, > >>> 2016L, 2016L, 2016L, 2016L, 2016L)), class = "data.frame", row.names > >>> c(NA, -24L)) > >>> > >>> head(DaT) > >>> R=100 > >>> bootprop <- function(data, index){ > >>> d <- data[index, ] > >>> sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)# > >>> } > >>> > >>> ################### > >>> #2015 > >>> ################### > >>> #-----Year2015_pooled > >>> Y2015_pooled<-subset(DaT, DaT$Year=="2015") > >>> Y2015_pooled_boot <- boot(Y2015_pooled, bootprop, R) > >>> boot_Y2015_pooled<-data.frame(Year="2015", Area= "Pooled", bootMean> >>> Y2015_pooled_boot$t0, SE=sd(Y2015_pooled_boot$t)) > >>> #-----Year2015_AreaA > >>> Y2015_A<-subset(DaT, DaT$Year=="2015" & DaT$Area=="A") > >>> Y2015_A_boot <- boot(Y2015_A, bootprop, R) > >>> boot_Y2015_A<-data.frame(Year="2015", Area= "A", bootMean> >>> Y2015_A_boot$t0, > >>> SE=sd(Y2015_A_boot$t)) > >>> #----Year2015_AreaB > >>> Y2015_B<-subset(DaT, DaT$Year=="2015" & DaT$Area=="B") > >>> Y2015_B_boot <- boot(Y2015_B, bootprop, R) > >>> boot_Y2015_B<-data.frame(Year="2015", Area= "B", bootMean> >>> Y2015_B_boot$t0, > >>> SE=sd(Y2015_B_boot$t)) > >>> ################### > >>> #2016 > >>> ################### > >>> #-----Year2016_pooled > >>> Y2016_pooled<-subset(DaT, DaT$Year=="2016") > >>> Y2016_pooled_boot <- boot(Y2016_pooled, bootprop, R) > >>> boot_Y2016_pooled<-data.frame(Year="2016", Area= "Pooled", bootMean> >>> Y2016_pooled_boot$t0, SE=sd(Y2016_pooled_boot$t)) > >>> > >>> #-----Year2016_AreaA > >>> Y2016_A<-subset(DaT, DaT$Year=="2016" & DaT$Area=="A") > >>> Y2016_A_boot <- boot(Y2016_A, bootprop, R) > >>> > >>> boot_Y2016_A<-data.frame(Year="2016", Area= "A", bootMean> >>> Y2016_A_boot$t0, > >>> SE=sd(Y2016_A_boot$t)) > >>> #----Year2016_AreaB > >>> Y2016_B<-subset(DaT, DaT$Year=="2016" & DaT$Area=="B") > >>> Y2016_B_boot <- boot(Y2016_B, bootprop, R) > >>> boot_Y2016_B<-data.frame(Year="2016", Area= "B", bootMean> >>> Y2016_B_boot$t0, > >>> SE=sd(Y2016_B_boot$t)) > >>> > >>> ## output data.matrix > >>> > BootMean_All<-rbind(boot_Y2015_pooled,boot_Y2015_A,boot_Y2015_B,boot_Y2016_pooled,boot_Y2016_A,boot_Y2016_B) > > >>> > >>> BootMean_All > >>> > >>> [[alternative HTML version deleted]] > >>> > >>> ______________________________________________ > >>> R-help at 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. > >>> > >> > >> ______________________________________________ > >> R-help at 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. > > > > ______________________________________________ > > R-help at 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]]