I have a dataset of successes, probabilities, and sample sizes that I am running binomial tests on.
Here is a sample of the data (note that the actual dataset has me run >100 binomial tests):
km n_1 prey_pred p0_prey_pred
<fct> <dbl> <int> <dbl>
80 93 12 0.119
81 1541 103 0.0793
83 316 5 0.0364
84 721 44 0.0796
89 866 58 0.131
I normally run this (example for first row):
n=93
p0=0.119
successes=12
binom.test(obs.successes, n, p0, "two.sided")
> Exact binomial test
data: 12 and 93
number of successes = 12, number of trials = 93, p-value = 0.74822
alternative hypothesis: true probability of success is not equal to 0.119
95 percent confidence interval:
0.068487201 0.214548325
sample estimates:
probability of success
0.12903226
Is there a way to systematically have it run multiple binomial tests on each row of data, and then storing all the output (p-value, confidence intervals, probability of success) as separate columns?
I've tried the solution proposed here, but I am clearly m
Using apply.
res <- t(`colnames<-`(apply(dat, 1, FUN=function(x) {
rr <- binom.test(x[3], x[2], x[4], "two.sided")
with(rr, c(x, "2.5%"=conf.int[1], estimate=unname(estimate),
"97.5%"=conf.int[2], p.value=unname(p.value)))
}), dat$km))
res
# km n_1 prey_pred p0_prey_pred 2.5% estimate 97.5% p.value
# 80 80 93 12 0.1190 0.068487201 0.12903226 0.21454832 7.482160e-01
# 81 81 1541 103 0.0793 0.054881013 0.06683971 0.08047927 7.307921e-02
# 83 83 316 5 0.0364 0.005157062 0.01582278 0.03653685 4.960168e-02
# 84 84 721 44 0.0796 0.044688325 0.06102635 0.08106220 7.311463e-02
# 89 89 866 58 0.1310 0.051245893 0.06697460 0.08572304 1.656621e-09
Edit
If you have multiple column sets, in wide format (and for some reason want to stay there)
dat2 <- `colnames<-`(cbind(dat, dat[-1]), c("km", "n_1.1", "prey_pred.1", "p0_prey_pred.1",
"n_1.2", "prey_pred.2", "p0_prey_pred.2"))
dat2[1:3,]
# km n_1.1 prey_pred.1 p0_prey_pred.1 n_1.2 prey_pred.2 p0_prey_pred.2
# 1 80 93 12 0.1190 93 12 0.1190
# 2 81 1541 103 0.0793 1541 103 0.0793
# 3 83 316 5 0.0364 316 5 0.0364
you may do:
res2 <- t(`colnames<-`(apply(dat2, 1, FUN=function(x) {
rr1 <- binom.test(x[3], x[2], x[4], "two.sided")
rr2 <- binom.test(x[6], x[5], x[7], "two.sided")
rrr1 <- with(rr1, c("2.5%.1"=conf.int[1], estimate.1=unname(estimate),
"97.5%.1"=conf.int[2], p.value.1=unname(p.value)))
rrr2 <- with(rr2, c("2.5%.1"=conf.int[1], estimate.1=unname(estimate),
"97.5%.1"=conf.int[2], p.value.1=unname(p.value)))
c(x, rrr1, rrr2)
}), dat2$km))
res2
# km n_1.1 prey_pred.1 p0_prey_pred.1 n_1.2 prey_pred.2 p0_prey_pred.2 2.5%.1
# 80 80 93 12 0.1190 93 12 0.1190 0.068487201
# 81 81 1541 103 0.0793 1541 103 0.0793 0.054881013
# 83 83 316 5 0.0364 316 5 0.0364 0.005157062
# 84 84 721 44 0.0796 721 44 0.0796 0.044688325
# 89 89 866 58 0.1310 866 58 0.1310 0.051245893
# estimate.1 97.5%.1 p.value.1 2.5%.1 estimate.1 97.5%.1 p.value.1
# 80 0.12903226 0.21454832 7.482160e-01 0.068487201 0.12903226 0.21454832 7.482160e-01
# 81 0.06683971 0.08047927 7.307921e-02 0.054881013 0.06683971 0.08047927 7.307921e-02
# 83 0.01582278 0.03653685 4.960168e-02 0.005157062 0.01582278 0.03653685 4.960168e-02
# 84 0.06102635 0.08106220 7.311463e-02 0.044688325 0.06102635 0.08106220 7.311463e-02
# 89 0.06697460 0.08572304 1.656621e-09 0.051245893 0.06697460 0.08572304 1.656621e-09
One could code this more nested, but I recommend to keep things easy so later others understand better what's going on, and probably including oneself.
Data:
dat <- read.table(text="km n_1 prey_pred p0_prey_pred
80 93 12 0.119
81 1541 103 0.0793
83 316 5 0.0364
84 721 44 0.0796
89 866 58 0.131 ", header=TRUE)