add.test: Apply a test to a Contingency Tables object
In Deducer: A Data Analysis GUI for R
Description
Usage
Arguments
Details
Value
See Also
Examples
View source: R/contingency.tables.R
Description
Applies and adds a test to a contingency.tables object.
Usage
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11
add.test(tables,name,htests,types=c("asymptotic","monte.carlo","exact"))
add.chi.squared(tables, simulate.p.value = FALSE, B = 10000)
add.likelihood.ratio(tables, conservative = FALSE, simulate.p.value = FALSE, B = 10000)
add.fishers.exact(tables, simulate.p.value = FALSE, B = 10000)
add.correlation(tables,method=c("spearman","kendall"))
add.kruskal(tables,nominal=c("both","rows","cols"))
Arguments
tables
An object of class contingency.tables
name
Name of the test
htests
A function or list of functions which take a matrix as it's argument
and returns an object of class htest
types
A character vector with the same number of items as htests, indicating what type of test
was done
conservative
Should a conservative p-value be computed. i.e. One with a continuity correction for
asymptotic tests and not using the mid p-value for exact and approximate tests
simulate.p.value
If TRUE p-values will be computed via monte carlo simulation
B
the number of samples for the monte carlo simulation
method
the type of correlation
nominal
Should the rows or columns be considered nominal.
Details
add.test applies a supplied list of tests to all of the tables in tables.
add.chi.squared is a wrapper function applying the chisq.test function to each table.
add.likelihood.ratio is a wrapper function applying the likelihood.test function to each table.
add.fishers.exact is a wrapper function applying the fisher.test function to each table.
add.correlation is a wrapper function applying the cor.test function to each table.
add.kruskal is a wrapper function applying the kruskal.test function to each table.
Value
A contingency.tables object identical to tables, but with the test applied to each table.
See Also
add.cross.strata.test
likelihood.test
cor.test
kruskal.test
Examples
1
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dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables<-contingency.tables(
row.vars=a,
col.vars=b,
stratum.var=c,data=dat)
tables<-add.chi.squared(tables,simulate.p.value=TRUE,B=10000)
tables<-add.likelihood.ratio(tables)
tables<-add.fishers.exact(tables)
tables<-add.correlation(tables,method='kendall')
tables<-add.kruskal(tables)
tables<-add.mantel.haenszel(tables)
print(tables)
remove(tables)
Example output
Loading required package: ggplot2
Loading required package: JGR
Loading required package: rJava
Loading required package: JavaGD
OpenJDK 64-Bit Server VM warning: Can't detect primordial thread stack location - find_vma failed
Please type JGR() to launch console. Platform specific launchers (.exe and .app) can also be obtained at http://www.rforge.net/JGR/files/.
Loading required package: car
Loading required package: carData
Loading required package: MASS
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
Note Non-JGR console detected:
Deducer is best used from within JGR (http://jgr.markushelbig.org/).
To Bring up GUI dialogs, type deducer().
Warning message:
In chisq.test(x, correct = FALSE) :
Chi-squared approximation may be incorrect
================================================================================
==================================================
========== Table: a by b ==========
| -- Stratum = FALSE --
| b
a | FALSE | TRUE | Row Total |
-----------------------|-----------|-----------|-----------|
FALSE Count | 4 | 9 | 13 |
Row % | 30.769% | 69.231% | 59.091% |
Column % | 57.143% | 60.000% | |
Total % | 18.182% | 40.909% | |
-----------------------|-----------|-----------|-----------|
TRUE Count | 3 | 6 | 9 |
Row % | 33.333% | 66.667% | 40.909% |
Column % | 42.857% | 40.000% | |
Total % | 13.636% | 27.273% | |
-----------------------|-----------|-----------|-----------|
Column Total | 7 | 15 | 22 |
Column % | 31.818% | 68.182% | |
| -- Stratum = TRUE --
| b
a | FALSE | TRUE | Row Total |
-----------------------|-----------|-----------|-----------|
FALSE Count | 24 | 29 | 53 |
Row % | 45.283% | 54.717% | 67.949% |
Column % | 68.571% | 67.442% | |
Total % | 30.769% | 37.179% | |
-----------------------|-----------|-----------|-----------|
TRUE Count | 11 | 14 | 25 |
Row % | 44.000% | 56.000% | 32.051% |
Column % | 31.429% | 32.558% | |
Total % | 14.103% | 17.949% | |
-----------------------|-----------|-----------|-----------|
Column Total | 35 | 43 | 78 |
Column % | 44.872% | 55.128% | |
Large Sample
Test Statistic DF p-value | Effect Size est.
Stratum = FALSE Chi Squared 0.016 1 0.899 |
Likelihood 0.016 1 0.899 |
Fishers Exact |
kendall's Correlation -0.124 0.901 | tau -0.027
Kruskal-W (nominal rows) 0.015 1 0.901 |
Kruskal-W (nominal cols) 0.015 1 0.901 |
Stratum = TRUE Chi Squared 0.011 1 0.915 |
Likelihood 0.011 1 0.915 |
Fishers Exact |
kendall's Correlation 0.106 0.916 | tau 0.012
Kruskal-W (nominal rows) 0.011 1 0.916 |
Kruskal-W (nominal cols) 0.011 1 0.916 |
Test Lower (%) Upper (%)
Stratum = FALSE Chi Squared
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
Stratum = TRUE Chi Squared
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
------------------------------
Monte Carlo
Test Statistic DF p-value
Stratum = FALSE Chi Squared 0.016 1
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
Stratum = TRUE Chi Squared 0.011 1
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
------------------------------
Exact
Test Statistic DF p-value
Stratum = FALSE Chi Squared
Likelihood
Fishers Exact 1
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
Stratum = TRUE Chi Squared
Likelihood
Fishers Exact 1
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
------------------------------
Large Sample
Test Statistic DF p-value | Effect Size est. Lower (%)
Mantel-Haenszel 0.001 1 0.972 | common odds ratio 1.015 0.435 ()
Test Upper (%)
Mantel-Haenszel 2.368 ()
---------------
================================================================================
Deducer documentation built on May 2, 2019, 8:35 a.m.
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Description Usage Arguments Details Value See Also Examples
View source: R/contingency.tables.R
Description
Applies and adds a test to a contingency.tables object.
Usage
1 2 3 4 5 6 7 8 9 10 11 | add.test(tables,name,htests,types=c("asymptotic","monte.carlo","exact"))
add.chi.squared(tables, simulate.p.value = FALSE, B = 10000)
add.likelihood.ratio(tables, conservative = FALSE, simulate.p.value = FALSE, B = 10000)
add.fishers.exact(tables, simulate.p.value = FALSE, B = 10000)
add.correlation(tables,method=c("spearman","kendall"))
add.kruskal(tables,nominal=c("both","rows","cols"))
|
Arguments
tables |
An object of class |
name |
Name of the test |
htests |
A function or list of functions which take a matrix as it's argument
and returns an object of class |
types |
A character vector with the same number of items as |
conservative |
Should a conservative p-value be computed. i.e. One with a continuity correction for asymptotic tests and not using the mid p-value for exact and approximate tests |
simulate.p.value |
If |
B |
the number of samples for the monte carlo simulation |
method |
the type of correlation |
nominal |
Should the rows or columns be considered nominal. |
Details
add.test applies a supplied list of tests to all of the tables in tables.
add.chi.squared is a wrapper function applying the chisq.test function to each table.
add.likelihood.ratio is a wrapper function applying the likelihood.test function to each table.
add.fishers.exact is a wrapper function applying the fisher.test function to each table.
add.correlation is a wrapper function applying the cor.test function to each table.
add.kruskal is a wrapper function applying the kruskal.test function to each table.
Value
A contingency.tables object identical to tables, but with the test applied to each table.
See Also
add.cross.strata.test
likelihood.test
cor.test
kruskal.test
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 | dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables<-contingency.tables(
row.vars=a,
col.vars=b,
stratum.var=c,data=dat)
tables<-add.chi.squared(tables,simulate.p.value=TRUE,B=10000)
tables<-add.likelihood.ratio(tables)
tables<-add.fishers.exact(tables)
tables<-add.correlation(tables,method='kendall')
tables<-add.kruskal(tables)
tables<-add.mantel.haenszel(tables)
print(tables)
remove(tables)
|
Example output
Loading required package: ggplot2
Loading required package: JGR
Loading required package: rJava
Loading required package: JavaGD
OpenJDK 64-Bit Server VM warning: Can't detect primordial thread stack location - find_vma failed
Please type JGR() to launch console. Platform specific launchers (.exe and .app) can also be obtained at http://www.rforge.net/JGR/files/.
Loading required package: car
Loading required package: carData
Loading required package: MASS
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
Note Non-JGR console detected:
Deducer is best used from within JGR (http://jgr.markushelbig.org/).
To Bring up GUI dialogs, type deducer().
Warning message:
In chisq.test(x, correct = FALSE) :
Chi-squared approximation may be incorrect
================================================================================
==================================================
========== Table: a by b ==========
| -- Stratum = FALSE --
| b
a | FALSE | TRUE | Row Total |
-----------------------|-----------|-----------|-----------|
FALSE Count | 4 | 9 | 13 |
Row % | 30.769% | 69.231% | 59.091% |
Column % | 57.143% | 60.000% | |
Total % | 18.182% | 40.909% | |
-----------------------|-----------|-----------|-----------|
TRUE Count | 3 | 6 | 9 |
Row % | 33.333% | 66.667% | 40.909% |
Column % | 42.857% | 40.000% | |
Total % | 13.636% | 27.273% | |
-----------------------|-----------|-----------|-----------|
Column Total | 7 | 15 | 22 |
Column % | 31.818% | 68.182% | |
| -- Stratum = TRUE --
| b
a | FALSE | TRUE | Row Total |
-----------------------|-----------|-----------|-----------|
FALSE Count | 24 | 29 | 53 |
Row % | 45.283% | 54.717% | 67.949% |
Column % | 68.571% | 67.442% | |
Total % | 30.769% | 37.179% | |
-----------------------|-----------|-----------|-----------|
TRUE Count | 11 | 14 | 25 |
Row % | 44.000% | 56.000% | 32.051% |
Column % | 31.429% | 32.558% | |
Total % | 14.103% | 17.949% | |
-----------------------|-----------|-----------|-----------|
Column Total | 35 | 43 | 78 |
Column % | 44.872% | 55.128% | |
Large Sample
Test Statistic DF p-value | Effect Size est.
Stratum = FALSE Chi Squared 0.016 1 0.899 |
Likelihood 0.016 1 0.899 |
Fishers Exact |
kendall's Correlation -0.124 0.901 | tau -0.027
Kruskal-W (nominal rows) 0.015 1 0.901 |
Kruskal-W (nominal cols) 0.015 1 0.901 |
Stratum = TRUE Chi Squared 0.011 1 0.915 |
Likelihood 0.011 1 0.915 |
Fishers Exact |
kendall's Correlation 0.106 0.916 | tau 0.012
Kruskal-W (nominal rows) 0.011 1 0.916 |
Kruskal-W (nominal cols) 0.011 1 0.916 |
Test Lower (%) Upper (%)
Stratum = FALSE Chi Squared
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
Stratum = TRUE Chi Squared
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
------------------------------
Monte Carlo
Test Statistic DF p-value
Stratum = FALSE Chi Squared 0.016 1
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
Stratum = TRUE Chi Squared 0.011 1
Likelihood
Fishers Exact
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
------------------------------
Exact
Test Statistic DF p-value
Stratum = FALSE Chi Squared
Likelihood
Fishers Exact 1
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
Stratum = TRUE Chi Squared
Likelihood
Fishers Exact 1
kendall's Correlation
Kruskal-W (nominal rows)
Kruskal-W (nominal cols)
------------------------------
Large Sample
Test Statistic DF p-value | Effect Size est. Lower (%)
Mantel-Haenszel 0.001 1 0.972 | common odds ratio 1.015 0.435 ()
Test Upper (%)
Mantel-Haenszel 2.368 ()
---------------
================================================================================
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