The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

hypr

hypr is an R package for easy translation between experimental (null) hypotheses and contrast matrices as used for linear regression. For an extensive overview of the package functions, see the hypr-intro vignette, e.g. by running vignette("hypr-intro") after installing the package.

Installation

Install from CRAN within R using:

install.packages("hypr")

Install the development version in R using devtools:

devtools::install_github("mmrabe/hypr", build_vignettes = TRUE)

Deriving contrast matrices

For a treatment contrast with a baseline and three treatment conditions:

trtC <- hypr(mu1~0, mu2~mu1, mu3~mu1, mu4~mu1)
trtC

## hypr object containing 4 null hypotheses:
## H0.1: 0 = mu1        (Intercept)
## H0.2: 0 = mu2 - mu1
## H0.3: 0 = mu3 - mu1
## H0.4: 0 = mu4 - mu1
## 
## Call:
## hypr(~mu1, ~mu2 - mu1, ~mu3 - mu1, ~mu4 - mu1, levels = c("mu1", 
## "mu2", "mu3", "mu4"))
## 
## Hypothesis matrix (transposed):
##     [,1] [,2] [,3] [,4]
## mu1  1   -1   -1   -1  
## mu2  0    1    0    0  
## mu3  0    0    1    0  
## mu4  0    0    0    1  
## 
## Contrast matrix:
##     [,1] [,2] [,3] [,4]
## mu1 1    0    0    0   
## mu2 1    1    0    0   
## mu3 1    0    1    0   
## mu4 1    0    0    1

To assign the contrast matrix to a factor fac with an intermediate hypr object:

contrasts(fac) <- contr.hypothesis(trtC)

… or without an intermediate object:

contrasts(fac) <- contr.hypothesis(mu1~0, mu2~mu1, mu3~mu1, mu4~mu1) 

For more information, see vignette("hypr-regression").

Deriving research hypotheses

To check which reserach (null) hypotheses a given contrast matrix is testing, we can create an empty hypr object.

testC <- hypr() # create an empty hypr object

A treatment contrast with 4 levels (incl. the baseline) can look as follows:

contr.treatment(4)

##   2 3 4
## 1 0 0 0
## 2 1 0 0
## 3 0 1 0
## 4 0 0 1

We can now populate the hypr object by setting its contrast matrix. Note that the treatment contrast does not have an intercept. We thus have to add it when populating the hypr object:

cmat(testC, add_intercept = TRUE) <- contr.treatment(4) # populate object via contrast matrix

Now, the hypr object contains 4 hypotheses, a hypothesis matrix and the contrast matrix identical to the treatment contrast with an intercept added:

testC

## hypr object containing 4 null hypotheses:
## H0.Intercept: 0 = X1        (Intercept)
##         H0.2: 0 = -X1 + X2
##         H0.3: 0 = -X1 + X3
##         H0.4: 0 = -X1 + X4
## 
## Call:
## hypr(Intercept = ~X1, `2` = ~-X1 + X2, `3` = ~-X1 + X3, `4` = ~-X1 + 
##     X4, levels = c("X1", "X2", "X3", "X4"))
## 
## Hypothesis matrix (transposed):
##    Intercept 2  3  4 
## X1  1        -1 -1 -1
## X2  0         1  0  0
## X3  0         0  1  0
## X4  0         0  0  1
## 
## Contrast matrix:
##    Intercept 2 3 4
## X1 1         0 0 0
## X2 1         1 0 0
## X3 1         0 1 0
## X4 1         0 0 1

The derived hypotheses can be rewritten as:

For more information, see vignette("hypr-contrasts").

Community guidelines

If you want to report a bug, are having technical difficulties or want to recommend features, it’s best to open a Github Issue. If you want to suggest a specific implementation of a feature or bug fix, you’re welcome to fork the repository and submit a pull request! Alternatively, if you are having problems or questions, you can also send an e-mail (maximilian.rabe@uni-potsdam.de).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.