# [R] compare proportions

array chip arrayprofile at yahoo.com
Wed Sep 28 22:28:35 CEST 2011

```Hi all, I asked this yesterday, but hadn't got any response yet. Understand this is not pure R technical question, but more of statistical. With many statistical experts in the list, I would appreciate any suggestions...

Many thanks

John

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Hi, I have a seemingly simple proportional test.  here is the question I am trying to answer:

There is a test running each day in the lab, the test comes out as either positive or negative. So at the end of each month, we can calculate a positive rate in that month as the proportion of positive test results. The data look like:

Month      # positive       # total tests    positive rate
January         24                205             11.7%
February        31                234             13.2%
March           26                227             11.5%
:
:
:
August          42                241          17.4%

The total # of positive before August is 182, and the total # of tests before August is 1526. It appears that from January to July, the positive monthly rate is between 11% to 13%, the rate in August is up around 17%. So the question is whether is up in August is statistically significant?

I can think of 3 ways to do this test:

1. Use binom.test(), set “p” as the average positive
rate between January and July (=182/1526):

> binom.test(42,241,182/1526)

Exact binomial test

data:  42 and 241
number of successes = 42, number
of trials = 241, p-value = 0.0125
alternative hypothesis: true
probability of success is not equal to 0.1192661
95 percent confidence interval:
0.1285821 0.2281769
sample estimates:
probability of success
0.1742739

2. Use prop.test(), where I compare the average
positive rate between January & July with the positive rate in August:

> prop.test(c(182,42),c(1526,241))

2-sample test for equality of
proportions with continuity correction

data:  c(182, 42) out of c(1526, 241)
X-squared = 5.203, df = 1,
p-value = 0.02255
alternative hypothesis:
two.sided
95 percent confidence interval:
-0.107988625 -0.002026982
sample estimates:
prop 1    prop 2
0.1192661 0.1742739

3. Use prop.test(), where I compare the average
MONTHLY positive rate between January & July with the positive rate in
August. The average monthly # of positives is 182/7=26, the average monthly \$
of total tests is 1526/7=218:

> prop.test(c(26,42),c(218,241))

2-sample test for equality of
proportions with continuity correction

data:  c(26, 42) out of c(218, 241)
X-squared = 2.3258, df = 1,
p-value = 0.1272
alternative hypothesis:
two.sided
95 percent confidence interval:
-0.12375569  0.01374008
sample estimates:
prop 1    prop 2
0.1192661 0.1742739

As you can see that the method 3 gave insignificant p value compared to method 1 & 2. While I understand each method is testing different hypothesis, but for the question I am trying to answer (does August have higher positive rate compare to earlier months?), which method is more relevant? Or I should consider some regression techniques, then what type of regression is appropriate?

Thanks for any suggestions,

John

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