Às 19:19 de 29/03/2025, Ebert,Timothy Aaron escreveu:
How about calculating a 95% confidence interval about the estimated proportion
in favor. The PooledInfRate package will do this for you. If confidence
intervals overlap then there is no significant difference.
-----Original Message-----
From: R-help <r-help-boun...@r-project.org> On Behalf Of Kevin Zembower via
R-help
Sent: Saturday, March 29, 2025 12:10 PM
To: R-help email list <r-help@r-project.org>
Subject: [R] Setting up hypothesis tests with the infer library?
[External Email]
Hello, all,
We're now starting to cover hypothesis tests in my Stats 101 course. As usual
in courses using the Lock5 textbook, 3rd ed., the homework answers are
calculated using their StatKey application. In addition (and for no extra
credit), I'm trying to solve the problems using R. In the case of hypothesis
test, in addition to manually setting up randomized null hypothesis
distributions and graphing them, I'm using the infer library. I've been really
impressed with this library and enjoy solving this type of problem with it.
One of the first steps in solving a hypothesis test with infer is to set up the
initial sampling dataset. Often, in Lock5 problems, this is a dataset that can
be downloaded with library(Lock5Data). However, other problems are worded like
this:
===========================
In 1980 and again in 2010, a Gallup poll asked a random sample of 1000 US citizens
"Are you in favor of the death penalty for a person convicted of murder?" In
1980, the proportion saying yes was 0.66. In 2010, it was 0.64. Does this data provide
evidence that the proportion of US citizens favoring the death penalty was higher in 1980
than it was in 2010? Use p1 for the proportion in 1980 and p2 for the proportion in 2010.
============================
I've been setting up problems like this with code similar to:
===========================
df <- data.frame(
survey = c(rep("1980", 1000), rep("2010", 1000)),
DP = c(rep("Y", 0.66*1000), rep("N", 1000 - (0.66*1000)),
rep("Y", 0.64*1000), rep("N", 1000 - (0.64*1000))))
(d_hat <- df %>%
specify(response = DP, explanatory = survey, success = "Y") %>%
calculate(stat = "diff in props", order = c("1980", "2010")))
============================
My question is, is this the way I should be setting up datasets for problems of
this type? Is there a more efficient way, that doesn't require the construction
of the whole sample dataset?
It seems like I should be able to do something like this:
=================
(df <- data.frame(group1count = 660, #Or, group1prop = 0.66
group1samplesize = 1000,
group2count = 640, #Or, group2prop = 0.64
group2samplesize = 1000)) =================
Am I overlooking a way to set up these sample dataframes for infer?
Thanks for your advice and guidance.
-Kevin
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Hello,
Package PooledInfRate seems promising.
library(PooledInfRate)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
year <- c(1980, 2010)
p <- c(0.66, 0.64)
n <- c(1000, 1000)
df1 <- data.frame(year, p, n)
df1 %>%
mutate(yes = p * n, no = n - yes) %>%
select(-p, -n) %>%
tidyr::pivot_longer(-year, names_to = "answer", values_to = "counts") %>%
mutate(answer = as.integer(answer == "yes")) %>%
pooledBin(answer ~ counts | year, data = .)
#> year P Lower Upper
#> 1 1980 0.001113165 6.831431e-05 0.009052399
#> 2 2010 0.001102918 6.796757e-05 0.008755895
The CI's intersect, so support for the death penalty hasn't changed from
1980 to 2010.
Hope this helps,
Rui Barradas
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