Hello,
Inline.
Às 07:47 de 23/01/21, Marna Wagley escreveu:
Dear Rui,
I was wondering whether we have to square root of SD to find SE, right?
No, we don't. var already divides by n, don't divide again.
This is the code, that can be seen by running the function name at a
command line.
sd
#function (x, na.rm = FALSE)
#sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x),
# na.rm = na.rm))
#<bytecode: 0x55f3ce900848>
#<environment: namespace:stats>
bootprop <- function(data, index){
d <- data[index, ]
sum(d[["BothTimes"]], na.rm = TRUE)/sum(d[["Time1"]], na.rm = TRUE)
}
R <- 1e3
set.seed(2020)
b <- boot(daT, bootprop, R)
b
b$t0 # original
sd(b$t) # bootstrapped estimate of the SE of the sample prop.
sd(b$t)/sqrt(1000)
pandit*(1-pandit)
hist(b$t, freq = FALSE)
Try plotting the normal densities for both cases, the red line is
clearly wrong.
f <- function(x, xbar, s){
dnorm(x, mean = xbar, sd = s)
}
hist(b$t, freq = FALSE)
curve(f(x, xbar = b$t0, s = sd(b$t)), from = 0, to = 1, col = "blue",
add = TRUE)
curve(f(x, xbar = b$t0, s = sd(b$t)/sqrt(R)), from = 0, to = 1, col =
"red", add = TRUE)
Hope this helps,
Rui Barradas
On Fri, Jan 22, 2021 at 3:07 PM Rui Barradas <ruipbarra...@sapo.pt
<mailto:ruipbarra...@sapo.pt>> wrote:
Hello,
Something like this, using base package boot?
library(boot)
bootprop <- function(data, index){
d <- data[index, ]
sum(d[["BothTimes"]], na.rm = TRUE)/sum(d[["Time1"]], na.rm = TRUE)
}
R <- 1e3
set.seed(2020)
b <- boot(daT, bootprop, R)
b
b$t0 # original
sd(b$t) # bootstrapped estimate of the SE of the sample prop.
hist(b$t, freq = FALSE)
Hope this helps,
Rui Barradas
Às 21:57 de 22/01/21, Marna Wagley escreveu:
> Hi All,
> I was trying to estimate standard error (SE) for the proportion
value using
> some kind of randomization process (bootstrapping or jackknifing)
in R, but
> I could not figure it out.
>
> Is there any way to generate SE for the proportion?
>
> The example of the data and the code I am using is attached for your
> reference. I would like to generate the value of proportion with
a SE using
> a 1000 times randomization.
>
> dat<-structure(list(Sample = structure(c(1L, 12L, 13L, 14L, 15L, 16L,
> 17L, 18L, 19L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L), .Label
= c("id1",
> "id10", "id11", "id12", "id13", "id14", "id15", "id16", "id17",
> "id18", "id19", "Id2", "id3", "id4", "id5", "id6", "id7", "id8",
> "id9"), class = "factor"), Time1 = c(0L, 1L, 1L, 1L, 0L, 0L,
> 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L), Time2 = c(1L,
> 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
> 1L, 1L)), .Names = c("Sample", "Time1", "Time2"), class =
"data.frame",
> row.names = c(NA,
> -19L))
> daT<-data.frame(dat %>%
> mutate(Time1.but.not.in.Time2 = case_when(
> Time1 %in% "1" & Time2 %in% "0" ~ "1"),
> Time2.but.not.in.Time1 = case_when(
> Time1 %in% "0" & Time2 %in% "1" ~ "1"),
> BothTimes = case_when(
> Time1 %in% "1" & Time2 %in% "1" ~ "1")))
> daT
> summary(daT)
>
> cols.num <- c("Time1.but.not.in.Time2","Time2.but.not.in.Time1",
> "BothTimes")
> daT[cols.num] <- sapply(daT[cols.num],as.numeric)
> summary(daT)
> ProportionValue<-sum(daT$BothTimes, na.rm=T)/sum(daT$Time1, na.rm=T)
> ProportionValue
> standard error??
>
> [[alternative HTML version deleted]]
>
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