[R] Request for some help about uncertainty analysis using bootstrap approach

[Bhaskar Mitra]

Thanks Rui. This is really helpful.

Regards,
Bhaskar

On Tue, May 31, 2022 at 4:03 AM Rui Barradas <ruipbarradas using 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
>
>
> Às 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]]
> >
> > ______________________________________________
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> > and provide commented, minimal, self-contained, reproducible code.
>

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