meta.ave.propratio2 | R Documentation |
Computes the estimate, standard error, and confidence interval for a geometric average proportion ratio from two or more studies.
meta.ave.propratio2(alpha, f1, f2, n1, n2, bystudy = TRUE)
alpha |
alpha level for 1-alpha confidence |
f1 |
vector of group 1 frequency counts |
f2 |
vector of group 2 frequency counts |
n1 |
vector of group 1 sample sizes |
n2 |
vector of group 2 sample sizes |
bystudy |
logical to also return each study estimate (TRUE) or not |
Returns a matrix. The first row is the average estimate across all studies. If bystudy is TRUE, there is 1 additional row for each study. The matrix has the following columns:
Estimate - estimated effect size
SE - standard error
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
exp(Estimate) - exponentiated estimate
exp(LL) - lower limit of the exponentiated confidence interval
exp(UL) - upper limit of the exponentiated confidence interval
Price2008vcmeta
n1 <- c(204, 201, 932, 130, 77)
n2 <- c(106, 103, 415, 132, 83)
f1 <- c(24, 40, 93, 14, 5)
f2 <- c(12, 9, 28, 3, 1)
meta.ave.propratio2(.05, f1, f2, n1, n2, bystudy = TRUE)
# Should return:
# Estimate SE LL UL
# Average 0.84705608 0.2528742 0.35143178 1.3426804
# Study 1 0.03604257 0.3297404 -0.61023681 0.6823220
# Study 2 0.81008932 0.3442007 0.13546839 1.4847103
# Study 3 0.38746839 0.2065227 -0.01730864 0.7922454
# Study 4 1.49316811 0.6023296 0.31262374 2.6737125
# Study 5 1.50851199 0.9828420 -0.41782290 3.4348469
# exp(Estimate) exp(LL) exp(UL)
# Average 2.332769 1.4211008 3.829294
# Study 1 1.036700 0.5432222 1.978466
# Study 2 2.248109 1.1450730 4.413686
# Study 3 1.473246 0.9828403 2.208350
# Study 4 4.451175 1.3670071 14.493677
# Study 5 4.520000 0.6584788 31.026662
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.