| Title: | Visualize Results of Statistical Hypothesis Tests |
| Version: | 0.1.2 |
| Maintainer: | Michael Czekanski <mczekanski@middlebury.edu> |
| Description: | Provides functionality to produce graphs of sampling distributions of test statistics from a variety of common statistical tests. With only a few keystrokes, the user can conduct a hypothesis test and visualize the test statistic and corresponding p-value through the shading of its sampling distribution. Initially created for statistics at Middlebury College. |
| Depends: | R (≥ 3.4.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.0.2 |
| Imports: | dplyr, ggplot2, ggthemes, gridExtra, magrittr, rlang, stats, tidyr |
| Suggests: | testthat |
| NeedsCompilation: | no |
| Packaged: | 2020-02-25 15:14:12 UTC; michael |
| Author: | Michael Czekanski [aut, cre], Alex Lyford [aut] |
| Repository: | CRAN |
| Date/Publication: | 2020-02-26 06:50:02 UTC |
Boostrap
Description
Boostrap using given data and statistic
Usage
bootstrap(fun, data, h0, nreps, conf.level = 0.95, verbose = 1)
Arguments
fun |
function to calculate on each sample. This can be a user-defined function that takes in data as a vector and returns a statistic. |
data |
data to use for bootstrapping. Should be a respresentative sample |
h0 |
null hypothesis value |
nreps |
number of times to bootstrap |
conf.level |
confidence value |
verbose |
default is 1 which will create a graph. To turn this off use verbose = 0. |
Value
results from boostrapping. A vector of length @param nreps containing each statistic calculated
Examples
x <- rnorm(100)
bootstrap(mean, x, 0.5, 1000, verbose = 0)
bootstrap(mean, x, 0.5, 1000)
Print "hello world!"
Description
print "hello world!"
Usage
hello()
Examples
hello()
Label Bootstrapped Results
Description
labels bootstrapped results. We use this to create colored histograms.
Usage
labelBootResults(results, lBound, uBound)
Arguments
results |
a vector, data from bootstrapping |
lBound |
lower bound of confidence interval |
uBound |
upper bound of confidence interval |
Value
vector of labels corresponding to result values
Examples
x <- rnorm(100)
labelBootResults(x, -1, 1)
Label discrete PDF
Description
labels a discrete pdf
Usage
labelPDFDis(x, obsVal, expVal)
Arguments
x |
x value |
obsVal |
observed event |
expVal |
expected value |
Value
vector of labels for x value in relation to observed event
Examples
labelPDFDis(0:10, 3, 5)
Density of Chi-Square distribution
Description
Density of Chi-Square distribution
Usage
mcDChiSq(x, degFree, ...)
Arguments
x |
x value |
degFree |
degrees of freedom |
... |
optional additional parameters which are ignored |
Value
density of given Chi-Square dist. at x
Density of F-distribution
Description
Density of F-distribution
Usage
mcDF(x, degFree1, degFree2, ...)
Arguments
x |
x value |
degFree1 |
degrees of freedom 1 |
degFree2 |
degrees of freedom 2 |
... |
optional additional parameters which are ignored |
Value
density of given F-dist. at x
dnorm but with more arguments
Description
compute density of normal distribution while allowing for more arguments which are ignored
Usage
mcDNorm(x, mean = 0, sd = 1, log = FALSE, ...)
Arguments
x |
x value |
mean |
mean of normal distribution |
sd |
std. dev. of noraml distribution |
log |
logical; if TRUE probabilities are given as log(p). See stats::dnorm |
... |
extra parameters which are ignored |
Value
density of normal distribution
Density of t-distribution
Description
Density of t-distribution
Usage
mcDT(x, degFree, ...)
Arguments
x |
x value |
degFree |
degrees of freedom |
... |
optional additional parameters which are ignored |
Value
density of given t-dist. at x
Used to shade in a PDF
Description
Returns density with extreme event region having NAs
Usage
shadePDFCts(x, fun, testStat, ...)
Arguments
x |
x value |
fun |
density function to use |
testStat |
test statistic value |
... |
optional parameters passed to density function |
Value
density if outside of extreme event region
Show results of ANOVA
Description
Visualization of distributional results of ANOVA. Please see aov for more information on parameters
Usage
showANOVA(formula, data = NULL, verbose = 1, ...)
Arguments
formula |
formula specifying a model. |
data |
data on which to perform ANOVA |
verbose |
if verbose > 0 the resulting graph is printed |
... |
Arguments passed to lm. See aov for more detail |
Value
output of call to aov
Examples
showANOVA(yield ~ N + P + K, npk)
Show Chi-Square Test
Description
show results of a chi-square test visually using chisq.test
Usage
showChiSq.Test(
x,
y = NULL,
p = rep(1/length(x), length(x)),
simulate.p.value = FALSE,
nreps = 2000,
verbose = 1
)
Arguments
x |
a numeric vector or matrix. x and can also be factors |
y |
a numeric vector |
p |
a vector of proabilities the same length as x. Used for goodness-of-fit tests. Must be a valid distribution |
simulate.p.value |
boolean, if TRUE use simulation to estimate p-value |
nreps |
if simulate.p.value = TRUE number of simulations to complete |
verbose |
level of visual output, 0 = silent |
Value
results of chisq.test call
Examples
showChiSq.Test(x = c(1,2,1), y= c(1,2,2))
Visualize results of McNemar's Test
Description
relevant parameters are passed to mcnemar.test
Usage
showMcNemarTest(x, y = NULL, correct = TRUE, verbose = 1)
Arguments
x |
two dimensional contingency table as a matrix or a factor object |
y |
factor object, ignored if x is a matrix |
correct |
logical indicating whether or not to perform continuity correction |
verbose |
if verbose > 0 the resulting graph is printed |
Value
results of call to mcnemar.test
Mosaic Plot
Description
Mosaic Plot
Usage
showMosaicPlot(x)
Arguments
x |
must be a matrix with each row and column labelled |
Value
mosaic plot showing observed proportions, colored by residuals from chi-sq. test
Examples
x <- matrix(runif(9,5,100), ncol = 3, dimnames = list(c("Yes1", "No1", "Maybe1"),
c("Yes2", "No2", "Maybe2")))
showMosaicPlot(x)
Show hypothesis tests from OLS
Description
Show hypothesis tests from OLS
Usage
showOLS(formula, data, verbose = 1)
Arguments
formula |
forumula for regression. Passed to lm |
data |
data for regression. Passed to lm |
verbose |
if verbose > 0 the resulting graph is printed |
Value
model object resulting from the regression
Examples
showOLS(mpg ~ cyl + disp, mtcars)
Show results of proportion test using binom.test
Description
Show results of proportion test using binom.test
Usage
showProp.Test(x, n, p = 0.5)
Arguments
x |
x value |
n |
number of repetitions |
p |
probability of success in one Bernoulli trial |
Value
output of call to binom.test
Examples
showProp.Test(3, 10)
Conduct z-test
Description
Runs z-test and outputs graph for interpretation using stats::t.test
Usage
showT.Test(group1, group2 = NULL, mu = 0, paired = FALSE, verbose = 1)
Arguments
group1 |
continuous data to test |
group2 |
optional: second group to include for two sample t-test |
mu |
optional: mean to test against for one-sample t-test |
paired |
boolean, if TRUE perform matched pairs t-test |
verbose |
default is 1 which will create a graph. To turn this off use verbose = 0. |
Value
results of call to t.test
Examples
x <- rnorm(100)
showT.Test(x, verbose = 0)
showT.Test(x)
Highlight extreme events
Description
Make graph highlighting events more extreme than observed sample
Usage
showXtremeEventsCts(
testID,
testStat,
densFun,
degFree = NULL,
degFree1 = NULL,
degFree2 = NULL,
xlims,
verbose = 1,
...
)
Arguments
testID |
name of hypothesis test |
testStat |
test statistic |
densFun |
function that computes appropriate density |
degFree |
degrees of freedom when only one is needed. This gets passed into densFun |
degFree1 |
first degrees of freedom parameter when more than one is needed |
degFree2 |
second degrees of freedom parameter when more than one is needed |
xlims |
x limits of the graph to be used. This is passed to ggplot |
verbose |
if verbose > 0 the resulting graph is printed |
... |
extra arguments passed to density function |
Value
results of call testFun
Examples
x <- rnorm(100)
showT.Test(x, verbose = 0)
showT.Test(x)
Show Extreme Events from a Discrete Distribution
Description
Show Extreme Events from a Discrete Distribution
Usage
showXtremeEventsDis(testID, obsVal, expVal, xVals, probFun, ...)
Arguments
testID |
name of test being performed. This is used to title the graph |
obsVal |
observed x value |
expVal |
expected x value |
xVals |
domain of x (possible values) |
probFun |
probability mass function for the given distribution |
... |
addition arguments passed to probFun |
Value
graph coloring events by how extreme they are under the null hypothesis
Examples
showXtremeEventsDis("Prop. Test", 3, 5, 0:10, probFun = dbinom, size = 10, prob = 0.5)