View source: R/113.ConfidenceIntervals_ADJ_n_x.R
ciAWDx | R Documentation |
Adjusted Wald method of CI estimation
ciAWDx(x, n, alp, h)
x |
- Number of successes |
n |
- Number of trials |
alp |
- Alpha value (significance level required) |
h |
- Adding factor |
Given data x
and n
are modified as x + h and n + (2*h)
respectively, where h > 0 then Wald-type interval is applied for the given x
and n
.
A dataframe with
x |
Number of successes (positive samples) |
LAWDx |
Adjusted Wald Lower limit |
UAWDx |
Adjusted Wald Upper Limit |
LABB |
Adjusted Wald Lower Abberation |
UABB |
Adjusted Wald Upper Abberation |
ZWI |
Zero Width Interval |
[1] 1998 Agresti A and Coull BA. Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician: 52; 119 - 126.
[2] 1998 Newcombe RG. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine: 17; 857 - 872.
[3] 2008 Pires, A.M., Amado, C. Interval Estimators for a Binomial Proportion: Comparison of Twenty Methods. REVSTAT - Statistical Journal, 6, 165-197.
prop.test and binom.test
for equivalent base Stats R functionality,
binom.confint
provides similar functionality for 11 methods,
wald2ci
which provides multiple functions for CI calculation ,
binom.blaker.limits
which calculates Blaker CI which is not covered here and
propCI
which provides similar functionality.
Other Adjusted methods of CI estimation given x & n:
PlotciAAllx()
,
ciAASx()
,
ciAAllx()
,
ciALRx()
,
ciALTx()
,
ciASCx()
,
ciATWx()
x= 5; n=5; alp=0.05; h=2 ciAWDx(x,n,alp,h)
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