Bhaskar Mitra
2022-May-29 21:23 UTC
[R] Request for some help about uncertainty analysis using bootstrap approach
Hello Everyone, I have a query about uncertainty analysis and would really appreciate some help in this regard. I intend to gapfill the NAs in the ?X? column of the dataframe (Df1). I have grouped the data using the column ?Group? , determined the mean and generated the ?Z? column. While I am using the mean and standard error approach to generate the uncertainty analysis, can we use the bootstrap approach to generate the uncertainty for the ?Z? column? Any help in this regard will be really appreciated. Regards, Bhaskar --------------------------------------------------------------- Df1 <- Group X Y Z 1 2 3 2 1 NA 3 3 1 3 3 3 1 4 3 4 2 2 2 1 2 NA 2 3 2 NA 2 3 2 4 2 4 3 2 2 2 3 NA 2 2 3 2 2 2 ------------------------------------------------------------------------------- Codes: Df1 <- Df1 %>% group_by(Group) %>% summarise(Y= mean(X), na.rm=T) Df1 <- Df1%>% mutate(Z= coalesce(X,Y)) [[alternative HTML version deleted]]
Rui Barradas
2022-May-31 11:03 UTC
[R] Request for some help about uncertainty analysis using bootstrap approach
Hello,
You can use package boot to bootstrap the statistic for you.
Write a function to compute the new column and assign the column means
to the new variable Z or, like in the code below Z2 (so that you can
compare to the Z column of simple averages).
library(dplyr)
library(boot)
boot_uncert <- function(data, indices) {
data[indices, ] %>%
group_by(Group) %>%
mutate(Y = mean(X, na.rm = TRUE),
Z = coalesce(X, Y)) %>%
pull(Z)
}
Df1 <- Df1 %>%
group_by(Group) %>%
mutate(Y = mean(X, na.rm = TRUE),
Z = coalesce(X, Y)) %>%
ungroup()
set.seed(2022)
R <- 1e3
Df1 %>%
mutate(Z2 = colMeans(boot(., boot_uncert, R = R)$t, na.rm = TRUE),
Z2 = coalesce(X, Z2))
Hope this helps,
Rui Barradas
?s 22:23 de 29/05/2022, Bhaskar Mitra escreveu:> Hello Everyone,
>
> I have a query about uncertainty analysis and would really appreciate some
> help in this regard.
>
> I intend to gapfill the NAs in the ?X? column of the dataframe (Df1). I
> have grouped the data using the column ?Group? ,
> determined the mean and generated the ?Z? column.
>
> While I am using the mean and standard error approach to generate the
> uncertainty analysis, can we use the bootstrap approach to
> generate the uncertainty for the ?Z? column? Any help in this regard will
> be really appreciated.
>
> Regards,
> Bhaskar
> ---------------------------------------------------------------
>
> Df1 <-
>
> Group X Y Z
> 1 2 3 2
> 1 NA 3 3
> 1 3 3 3
> 1 4 3 4
> 2 2 2 1
> 2 NA 2 3
> 2 NA 2 3
> 2 4 2 4
> 3 2 2 2
> 3 NA 2 2
> 3 2 2 2
>
>
-------------------------------------------------------------------------------
> Codes:
>
> Df1 <- Df1 %>% group_by(Group) %>% summarise(Y= mean(X), na.rm=T)
>
> Df1 <- Df1%>% mutate(Z= coalesce(X,Y))
>
> [[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.
Bhaskar Mitra
2022-Jun-01 16:22 UTC
[R] Request for some help about uncertainty analysis using bootstrap approach
Thanks Rui. This is really helpful. Regards, Bhaskar On Tue, May 31, 2022 at 4:03 AM Rui Barradas <ruipbarradas at sapo.pt> wrote:> Hello, > > You can use package boot to bootstrap the statistic for you. > Write a function to compute the new column and assign the column means > to the new variable Z or, like in the code below Z2 (so that you can > compare to the Z column of simple averages). > > library(dplyr) > library(boot) > > boot_uncert <- function(data, indices) { > data[indices, ] %>% > group_by(Group) %>% > mutate(Y = mean(X, na.rm = TRUE), > Z = coalesce(X, Y)) %>% > pull(Z) > } > > Df1 <- Df1 %>% > group_by(Group) %>% > mutate(Y = mean(X, na.rm = TRUE), > Z = coalesce(X, Y)) %>% > ungroup() > > set.seed(2022) > R <- 1e3 > > Df1 %>% > mutate(Z2 = colMeans(boot(., boot_uncert, R = R)$t, na.rm = TRUE), > Z2 = coalesce(X, Z2)) > > Hope this helps, > > Rui Barradas > > > ?s 22:23 de 29/05/2022, Bhaskar Mitra escreveu: > > Hello Everyone, > > > > I have a query about uncertainty analysis and would really appreciate > some > > help in this regard. > > > > I intend to gapfill the NAs in the ?X? column of the dataframe (Df1). I > > have grouped the data using the column ?Group? , > > determined the mean and generated the ?Z? column. > > > > While I am using the mean and standard error approach to generate the > > uncertainty analysis, can we use the bootstrap approach to > > generate the uncertainty for the ?Z? column? Any help in this regard will > > be really appreciated. > > > > Regards, > > Bhaskar > > --------------------------------------------------------------- > > > > Df1 <- > > > > Group X Y Z > > 1 2 3 2 > > 1 NA 3 3 > > 1 3 3 3 > > 1 4 3 4 > > 2 2 2 1 > > 2 NA 2 3 > > 2 NA 2 3 > > 2 4 2 4 > > 3 2 2 2 > > 3 NA 2 2 > > 3 2 2 2 > > > > > ------------------------------------------------------------------------------- > > Codes: > > > > Df1 <- Df1 %>% group_by(Group) %>% summarise(Y= mean(X), na.rm=T) > > > > Df1 <- Df1%>% mutate(Z= coalesce(X,Y)) > > > > [[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. >[[alternative HTML version deleted]]