...is it possible to do that? I apologize for something that must be a very trivial question for most of you but, unfortunately, it is not for me. A binary variable is measured, say, 50 times each year during 10 year. My interest is focused on the percentage of 1s with respect to the total if each year. There is no way to repeat those measure within each year and getting the CIs by the "normal way". By the way, it would be important to get even a rough estimate of the CIs of these estimates (/n/1//n/1+/n/0). In case this is not a blasphemy, how might be done in R? Thanks in advance for any help -- View this message in context: http://r.789695.n4.nabble.com/Resampling-with-replacement-on-a-binary-0-1-dataset-to-get-Cis-tp4127990p4127990.html Sent from the R help mailing list archive at Nabble.com.
Resampling with replacement on a binary (0, 1) dataset to get Cis
6 messages · Simone Santoro, David Winsemius
On Dec 1, 2011, at 6:34 AM, lincoln wrote:
...is it possible to do that? I apologize for something that must be a very trivial question for most of you but, unfortunately, it is not for me. A binary variable is measured, say, 50 times each year during 10 year. My interest is focused on the percentage of 1s with respect to the total if each year. There is no way to repeat those measure within each year and getting the CIs by the "normal way". By the way, it would be important to get even a rough estimate of the CIs of these estimates (/n/1//n/1+/n/0). In case this is not a blasphemy, how might be done in R?
?prop.test
Thanks in advance for any help
David Winsemius, MD West Hartford, CT
Thanks. So, suppose for one specific year (first year over 10) the percentage of successes deriving from 100 trials with 38 successes (and 62 failures), its value would be 38/100=0.38. I could calculate its confidence intervals this way:
success<-38 total<-100 prop.test(success,total,p=0.5,alternative="two.sided")
1-sample proportions test with continuity correction data: success out of total, null probability 0.5 X-squared = 5.29, df = 1, p-value = 0.02145 alternative hypothesis: true p is not equal to 0.5 95 percent confidence interval: 0.2863947 0.4829411 sample estimates: p 0.38 So it would be var$1=0.38 , CI=0.286-0.483 Is it correct? -- View this message in context: http://r.789695.n4.nabble.com/Resampling-with-replacement-on-a-binary-0-1-variable-to-get-CIs-tp4127990p4129048.html Sent from the R help mailing list archive at Nabble.com.
On Dec 1, 2011, at 10:49 AM, lincoln wrote:
Thanks. So, suppose for one specific year (first year over 10) the percentage of successes deriving from 100 trials with 38 successes (and 62 failures), its value would be 38/100=0.38. I could calculate its confidence intervals this way:
success<-38 total<-100 prop.test(success,total,p=0.5,alternative="two.sided")
1-sample proportions test with continuity correction data: success out of total, null probability 0.5 X-squared = 5.29, df = 1, p-value = 0.02145 alternative hypothesis: true p is not equal to 0.5 95 percent confidence interval: 0.2863947 0.4829411 sample estimates: p 0.38 So it would be var$1=0.38 , CI=0.286-0.483 Is it correct?
I couldn't tell if this were homework, so I just threw out a starting point. If you were told to do a resampling method then this would just be a starting point and you would be expected to go further with the boot function.
-- View this message in context: http://r.789695.n4.nabble.com/Resampling-with-replacement-on-a-binary-0-1-variable-to-get-CIs-tp4127990p4129048.html Sent from the R help mailing list archive at Nabble.com.
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David Winsemius, MD West Hartford, CT
Thanks. Anyway, it is not homework and I was not told to do that. My question has not been answered yet, I'll try to reformulate it: Does it make (statistical) sense to resample with replacement in this situation to get an estimate of the CIs? In case it does, how could I do it in R? Some further details on my real case study: 10 independent samples from a population in ten sessions. Each sample consists of a number (somehow variable) of random individuals that are classified as 0 or 1 depending on one specific state (presence or absence of a disease). I can calculate, for each session, the percentage of individuals diseased but I have nothing about the CIs, any suggestion? -- View this message in context: http://r.789695.n4.nabble.com/Resampling-with-replacement-on-a-binary-0-1-variable-to-get-CIs-tp4127990p4145733.html Sent from the R help mailing list archive at Nabble.com.
On Dec 2, 2011, at 3:55 AM, lincoln wrote:
Thanks. Anyway, it is not homework and I was not told to do that. My question has not been answered yet, I'll try to reformulate it: Does it make (statistical) sense to resample with replacement in this situation to get an estimate of the CIs? In case it does, how could I do it in R?
Some further details on my real case study: 10 independent samples from a population in ten sessions. Each sample consists of a number (somehow variable) of random individuals that are classified as 0 or 1 depending on one specific state (presence or absence of a disease). I can calculate, for each session, the percentage of individuals diseased but I have nothing about the CIs, any suggestion?
I do not see much advantage to using resampling in this instance. The variance of a proportion is not theoretically complicated and you have introduced no further complicating factors that would call into question the validity of the estimates you would get from prop.test.
David Winsemius, MD West Hartford, CT