Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates risk ratio by unconditional maximum likelihood estimation (Wald), and small sample adjustment (small). Confidence intervals are calculated using normal approximation (Wald), and normal approximation with small sample adjustment (small), and bootstrap method (boot).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  riskratio(x, y = NULL,
method = c("wald", "small", "boot"),
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE,
replicates = 5000)
riskratio.wald(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE)
riskratio.small(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE)
riskratio.boot(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
correction = FALSE,
verbose = FALSE,
replicates = 5000)

x 
input data can be one of the following: r x 2 table, vector
of numbers from a contigency table (will be transformed into r x 2
table in rowwise order), or single factor or character vector that
will be combined with 
y 
single factor or character vector that will be combined with

method 
method for calculating risk ratio and confidence interval 
conf.level 
confidence level (default is 0.95) 
rev 
reverse order of "rows", "colums", "both", or "neither" (default) 
correction 
set to TRUE for Yate's continuity correction (default is FALSE) 
verbose 
set to TRUE to return more detailed results (default is FALSE) 
replicates 
Number of bootstrap replicates (default = 5000) 
Calculates risk ratio by unconditional maximum likelihood estimation (Wald), and small sample adjustment (small). Confidence intervals are calculated using normal approximation (Wald), and normal approximation with small sample adjustment (small), and bootstrap method (boot).
This function expects the following table struture:
1 2 3 4 5 6  disease=0 disease=1
exposed=0 (ref) n00 n01
exposed=1 n10 n11
exposed=2 n20 n21
exposed=3 n30 n31

The reason for this is because each level of exposure is compared to the reference level.
If you are providing a 2x2 table the following table is preferred:
1 2 3 4  disease=0 disease=1
exposed=0 (ref) n00 n01
exposed=1 n10 n11

If the table you want to provide to this function is not in the
preferred form, just use the rev
option to "reverse" the rows,
columns, or both. If you are providing categorical variables (factors
or character vectors), the first level of the "exposure" variable is
treated as the reference. However, you can set the reference of a
factor using the relevel
function.
Likewise, each row of the rx2 table is compared to the exposure reference level and test of independence twosided p values are calculated using Fisher's Exact, Monte Carlo simulation, and the chisquare test.
x 
table that was used in analysis (verbose = TRUE) 
data 
same table as 
p.exposed 
proportions exposed (verbose = TRUE) 
p.outcome 
proportions experienced outcome (verbose = TRUE) 
measure 
risk ratio and confidence interval 
conf.level 
confidence level used (verbose = TRUE) 
boot.replicates 
number of replicates used in bootstrap estimation of confidence intervals (verbose = TRUE) 
p.value 
p value for test of independence 
mc.replicates 
number of replicates used in Monte Carlo simulation p value (verbose = TRUE) 
correction 
logical specifying if continuity correction was used 
Tomas Aragon, [email protected], http://www.phdata.science
Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, LippincottRaven Publishers
Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press
Nicolas P. Jewell (2004), Statistics for Epidemiology, 1st Edition, 2004, Chapman & Hall, pp. 7381
Steve Selvin (1998), Modern Applied Biostatistical Methods Using SPlus, 1st Edition, Oxford University Press
tab2by2.test
, oddsratio
,
rateratio
, epitab
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ##Casecontrol study assessing whether exposure to tap water
##is associated with cryptosporidiosis among AIDS patients
tapw < c("Lowest", "Intermediate", "Highest")
outc < c("Case", "Control")
dat < matrix(c(2, 29, 35, 64, 12, 6),3,2,byrow=TRUE)
dimnames(dat) < list("Tap water exposure" = tapw, "Outcome" = outc)
riskratio(dat, rev="c")
riskratio.wald(dat, rev="c")
riskratio.small(dat, rev="c")
##Selvin 1998, p. 289
sel < matrix(c(178, 79, 1411, 1486), 2, 2)
dimnames(sel) < list("Behavior type" = c("Type A", "Type B"),
"Outcome" = c("CHD", "No CHD")
)
riskratio.boot(sel, rev = "b")
riskratio.boot(sel, rev = "b", verbose = TRUE)
riskratio(sel, rev = "b", method = "boot")

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.