hrm_test: Test for Multi-Factor High-Dimensional Repeated Measures

In HRM: High-Dimensional Repeated Measures

Description

Performing main and interaction effects of up to three whole- or subplot-factors. In total, a maximum of four factors can be used. There are two different S3 methods available. The first method requires a list of matrices in the wide table format. The second methodl requres a data.frame in the long table format.

Usage

hrm_test(data, ...)

## S3 method for class 'list'
hrm_test(data, alpha = 0.05, ...)

## S3 method for class 'data.frame'
hrm_test(
  data,
  formula,
  alpha = 0.05,
  subject,
  variable = NULL,
  nonparametric = FALSE,
  np.correction = NA,
  character.only = FALSE,
  ...
)

Arguments

data	Either a data.frame (one observation per row) or a list with matrices (one subject per row) for all groups containing the data
...	Further arguments passed to 'hrm_test' will be ignored
alpha	alpha level used for calculating the critical value for the test
formula	A model formula object. The left hand side contains the response variable and the right hand side contains the whole- and subplot factors.
subject	column name within the data frame X identifying the subjects
variable	if not 'NULL' then multivariate tests are applied. We assume that for each factor level of 'variable', we observe several repated measurements. Currently only supports designs with 1 whole- and one sub-plot factor.
nonparametric	Logical variable indicating wether the noparametric version of the test statistic should be used
np.correction	Logical variable indicating wether a small sample size correction for the nonparametric test should be used (TRUE) or not (FALSE). By using NA, np.correction is used automatically in an high-dimensional setting.
character.only	a logical indicating whether subject can be assumed to be a character string

Value

Returns an object from class HRM containing

result	A dataframe with the results from the hypotheses tests.
formula	The formula object which was used.
alpha	The type-I error rate which was used.
subject	The column name identifying the subjects.
factors	A list containing the whole- and subplot factors.
data	The data.frame or list containing the data.

Examples

## hrm_test with a list of matrices

# number patients per group
n = c(10,10)
# number of groups
a=2
# number of variables
d=40

# defining the list consisting of the samples from each group
mu_1 = mu_2 = rep(0,d)
# autoregressive covariance matrix
sigma_1 = diag(d)
for(k in 1:d) for(l in 1:d) sigma_1[k,l] = 1/(1-0.5^2)*0.5^(abs(k-l))
sigma_2 = 1.5*sigma_1
X = list(mvrnorm(n[1],mu_1, sigma_1), mvrnorm(n[2],mu_2, sigma_2))
X=lapply(X, as.matrix)

hrm_test(data=X, alpha=0.05)


## hrm.test with a data.frame using a 'formula' object

# using the EEG dataset
?EEG

# Univariate Approach
hrm_test(value ~ group*region*variable, subject = "subject", data = EEG)

# Multivariate Approach: testing effects for each variable
hrm_test(value~group*region, subject=subject, variable=variable, data = EEG)

HRM documentation built on Feb. 6, 2020, 5:15 p.m.