Package 'pARI' reference manual

Title:	Permutation-Based All-Resolutions Inference
Description:	Computes the All-Resolution Inference method in the permutation framework, i.e., simultaneous lower confidence bounds for the number of true discoveries. <doi:10.1002/sim.9725>.
Authors:	Angela Andreella [aut, cre] (Main author, <https://orcid.org/0000-0002-1141-3041>)
Maintainer:	Angela Andreella <[email protected]>
License:	GPL (>= 2)
Version:	1.1.2
Built:	2024-10-08 06:20:57 UTC
Source:	CRAN

Critical vector

Description

Compute critical vector curve.

Usage

criticalVector(pvalues, family = "simes", alpha = 0.05, lambda, delta = 1, m = NULL)
criticalVector(pvalues, family = "simes", alpha = 0.05, lambda, delta = 1, m = NULL)

Arguments

`pvalues`	Matrix of pvalues with dimensions $m \times B$ used instead of the data matrix `X`. Default to `NULL`.
`family`	String character. Name of the family confidence envelope to compute the critical vector from `"simes"`, `"aorc"`, `"beta"`, `"higher.criticism"`, and `"power"`. Default to "simes".
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.
`lambda`	Numeric value. $\lambda$ value computed by `lambdaOpt`. Default to 1.
`delta`	Numeric value. $\delta$ value. Please see the reference below. Default to 1.
`m`	Numeric value. Number of hypothesis. Default to `NULL`.

Value

Numeric vector. Critical vector curve with length $m$ .

Author(s)

Angela Andreella

References

Andreella, A., Hemerik, J., Finos, L., Weeda, W., & Goeman, J. (2023). Permutation-based true discovery proportions for functional magnetic resonance imaging cluster analysis. Statistics in Medicine, 42(14), 2311-2340.

Examples

db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- pARI::signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 1)
plot(sort(pv[,1]), type = "l")
lines(cv)
db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- pARI::signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 1)
plot(sort(pv[,1]), type = "l")
lines(cv)

Lower bound for the number of true discoveries

Description

Calculates 1- $\alpha$ lower confidence bound for the set-wise of false null hypotheses.

Usage

dI(ix, cv, pvalues, iterative, approx, ncomb, ...)
dI(ix, cv, pvalues, iterative, approx, ncomb, ...)

Arguments

`ix`	Numeric vector: set-wise hypotheses considered.
`cv`	Numeric vector: critical vector computed by `criticalVector`.
`pvalues`	If `iterative = TRUE` you must put here the matrix of $p$ -values with dimensions $m \times B$ where $m$ is the number of variables and $B$ the number of permutations. Instead, if `iterative = FALSE`, you can put directly the vector of $m$ observed $p$ -values.
`iterative`	Boolean value. If `iterative = TRUE`, the iterative method is applied (computationally demanding). Default to `FALSE`. Please see the reference below.
`approx`	Boolean value. Default to `TRUE`. If you are analyzing high dimensional data, we suggest to put `approx = TRUE` to speed up the computation time. Please see the reference below.
`ncomb`	Numeric value. If `approx = TRUE`, you must decide how many random sub collections (level of approximation) considered. Default to 100.
`...`	Further arguments for the iterative approach, i.e., `iterative = TRUE`.

Value

Numeric value: the lower confidence bound for the number of true discoveries concerning the cluster ix specified.

Author(s)

Angela Andreella

References

Examples

db <- simulateData(pi0 = 0.7, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 0.1, alpha = 0.1)
dI(ix = c(1:100), cv = cv, pvalues = pv)
db <- simulateData(pi0 = 0.7, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 0.1, alpha = 0.1)
dI(ix = c(1:100), cv = cv, pvalues = pv)

Lambda calibration

Description

Computes the optimal lambda calibration parameter used in the critical vector criticalVector.

Usage

lambdaOpt(pvalues, family, alpha = 0.05, delta = 0, step.down = FALSE,
 max.step = 10, m = NULL)
lambdaOpt(pvalues, family, alpha = 0.05, delta = 0, step.down = FALSE,
 max.step = 10, m = NULL)

Arguments

`pvalues`	Matrix of $p$ -values with dimensions $m \times B$ where $m$ is the number of variables and $B$ the number of permutations used instead of the data matrix `X`. Default to `NULL`.
`family`	String character. Name of the family confidence envelope to compute the critical vector from `"simes"`, `"aorc"`, `"beta"`, `"higher.criticism"`, and `"power"`. Default to "simes".
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.
`delta`	Numeric value. $\delta$ value. Please see the reference below. Default to 0.
`step.down`	Boolean value. Default to `FALSE` If you want to compute the lambda calibration parameter using the step-down approach put `TRUE`. Please see the reference below.
`max.step`	Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when `step.down = TRUE`.
`m`	Numeric value. Number of hypotheses. Default to `NULL`.

Value

Numeric value. $\lambda$ parameter estimate.

Author(s)

Angela Andreella

References

Examples

db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- lambdaOpt(pvalues = pv, family = "simes", alpha = 0.05)
db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- lambdaOpt(pvalues = pv, family = "simes", alpha = 0.05)

True Discovery Proportion brain map

Description

Performs the True Discovery Proportion brain map.

Usage

map_TDP(ARIout,path,name,mask)
map_TDP(ARIout,path,name,mask)

Arguments

`ARIout`	Output object by `pARIbrain`.
`path`	Character string. Path to save the NIfTI file. The path does not must end with `/`.
`name`	Character string. The name of the map NIfTI file that will be used.
`mask`	NIfTI file or character string. 3D array of logical values (i.e. `TRUE/FALSE` in/out of the brain). Alternatively it may be a (character) NIfTI file name. If `mask=NULL`, it is assumed that none of the voxels have to be excluded.

Value

The function write directly in the path specified the true discovery proportion NIfTI map with name specified in name.

Author(s)

Angela Andreella

Permutation-based All-Resolutions Inference

Description

The main function for All-Resolutions Inference (ARI) method based on the critical vector constructed using the $p$ -values permutation distribution. The function computes simultaneous lower bounds for the number of true discoveries for each set of hypotheses specified in ix controlling family-wise error rate at level alpha.

Usage

pARI(X= NULL, ix, alpha = 0.05, family = "simes", delta = 0, B = 1000, pvalues = NULL, 
test.type = "one_sample", complete = FALSE, clusters = FALSE, iterative = FALSE, 
approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)
pARI(X= NULL, ix, alpha = 0.05, family = "simes", delta = 0, B = 1000, pvalues = NULL, 
test.type = "one_sample", complete = FALSE, clusters = FALSE, iterative = FALSE, 
approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)

Arguments

`X`	Data matrix where rows represent the $m$ variables and columns the $n$ observations.
`ix`	Numeric vector which expresses the set of hypotheses of interest. It can be a vector with length equals $m$ indicating the corresponding cluster for each variable, (in this case, you must put `clusters = TRUE`), or a vector containing the position indices of the variables of interest if only one set/cluster of hypotheses is considered.
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.
`family`	String character. Name of the family confidence envelope to compute the critical vector from `"simes"`, `"aorc"`, `"beta"`, `"higher.criticism"`, and `"power"`. Default to "simes".
`delta`	Numeric value. $\delta$ value. Please see the reference below. Default to 0.
`B`	Numeric value. Number of permutations, default to 1000.
`pvalues`	Matrix of $p$ -values with dimensions $m \times B$ where $m$ is the number of variables and $B$ the number of permutations used instead of the data matrix `X`. Default to NULL.
`test.type`	Character string. Choose a type of tests among `"one_sample"`, i.e., one-sample t-tests, or `"two_samples"`, i.e., two-samples t-tests. Default `"one_sample"`.
`complete`	Boolean value. If `TRUE` the sets of critical vectors and the raw $p$ -values are returned. Default to `FALSE`.
`clusters`	Boolean value. If `ix` indicates many clusters/sets must be `TRUE`. Default @FALSE.
`iterative`	Boolean value. If `iterative = TRUE`, the iterative method is applied (computationally demanding). Default to `FALSE`. Please see the reference below.
`approx`	Boolean value. Default to `TRUE`. If you are analyzing high dimensional data, we suggest to put `approx = TRUE` to speed up the computation time. Please see the reference below.
`ncomb`	Numeric value. If `approx = TRUE`, you must decide how many random sub collections (level of approximation) considered. Default to 100.
`step.down`	Boolean value. Default to `FALSE` If you want to compute the lambda calibration parameter using the step-down approach put `TRUE`. Please see the reference below.
`max.step`	Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when `step.down = TRUE`.
`...`	Further arguments

Value

by default returns a list with the following objects:

discoveries: lower bound for the number of true discoveries in the set selected
ix: selected variables

If complete = TRUE the raw pvalues and cv critical vector are also returned.

Author(s)

Angela Andreella

References

For the general framework of All-Resolutions Inference see:

Goeman, Jelle J., and Aldo Solari. "Multiple testing for exploratory research." Statistical Science 26.4 (2011): 584-597.

For permutation-based All-Resolutions Inference see:

Examples

datas <- simulateData(pi0 = 0.8, m = 1000, n = 30, power = 0.9, rho = 0,seed = 123)
out <- pARI(X = datas, ix = c(1:200),test.type = "one_sample")
out
datas <- simulateData(pi0 = 0.8, m = 1000, n = 30, power = 0.9, rho = 0,seed = 123)
out <- pARI(X = datas, ix = c(1:200),test.type = "one_sample")
out

Permutation-based All-Resolutions Inference for brain imaging.

Description

Usage

pARIbrain(copes, thr=NULL, mask=NULL, alpha=.05, clusters = NULL, 
alternative = "two.sided", summary_stat=c("max", "center-of-mass"),
silent=FALSE, family = "simes", delta = 0, B = 1000, rand = FALSE, 
iterative = FALSE, approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)
pARIbrain(copes, thr=NULL, mask=NULL, alpha=.05, clusters = NULL, 
alternative = "two.sided", summary_stat=c("max", "center-of-mass"),
silent=FALSE, family = "simes", delta = 0, B = 1000, rand = FALSE, 
iterative = FALSE, approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)

Arguments

`copes`	List of NIfTI file. The list of copes, i.e., contrasts maps, one for each subject used to compute the statistical tests.
`thr`	Numeric value. Threshold used to construct the cluster map. Default to `NULL`.
`mask`	NIfTI file or character string. 3D array of logical values (i.e. `TRUE/FALSE` in/out of the brain). Alternatively it may be a (character) NIfTI file name. If `mask=NULL`, it is assumed that none of the voxels have to be excluded.
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.
`clusters`	NIfTI file or character string. 3D array of cluster ids (0 when voxel does not belong to any cluster) or a (character) NIfTI file name. If `cluster=NULL` the cluster map is computed by the `cluster_threshold` function with threshold equals `thr`.
`alternative`	Character string. It refers to the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"lower"`.
`summary_stat`	Character string. Choose among `=c("max", "center-of-mass")`.
`silent`	Boolean value. Default to `FALSE`. If `TRUE` the function prints the results.
`family`	String character. Name of the family confidence envelope to compute the critical vector from `"simes"`, `"aorc"`, `"beta"`, `"higher.criticism"`, and `"power"`. Default to "simes".
`delta`	Numeric value. $\delta$ value. Please see the reference below. Default to 0.
`B`	Numeric value. Number of permutations, default to 1000.
`rand`	Boolean value. Default to `FALSE`. If `rand = TRUE`, the $p$ -values are computed by `rowRanks`. Please see `signTest`
`iterative`	Boolean value. If `iterative = TRUE`, the iterative method is applied (computationally demanding). Default to `FALSE`. Please see the reference below.
`approx`	Boolean value. Default to `TRUE`. If you are analyzing high dimensional data, we suggest to put `approx = TRUE` to speed up the computation time. Please see the reference below.
`ncomb`	Numeric value. If `approx = TRUE`, you must decide how many random sub collections (level of approximation) considered. Default to 100.
`step.down`	Boolean value. Default to `FALSE` If you want to compute the lambda calibration parameter using the step-down approach put `TRUE`. Please see the reference below.
`max.step`	Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when `step.down = TRUE`.
`...`	further arguments. See `signTest`.

Value

A list with elements:

out: Data.frame containing the size, the number of false null hypotheses, the number of true null hypotheses, the lower bound for the true discovery proportion, and other statistics for each cluster.
clusters: Matrix describing the clusters analyzed.

Author(s)

Angela Andreella

References

For the general framework of All-Resolutions Inference see:

Goeman, Jelle J., and Aldo Solari. "Multiple testing for exploratory research." Statistical Science 26.4 (2011): 584-597.

For All-Resolutions Inference for functional Magnetic Resonance Imaging data see:

Rosenblatt, Jonathan D., et al. "All-resolutions inference for brain imaging." Neuroimage 181 (2018): 786-796.

For permutation-based All-Resolutions Inference see:

Examples

## Not run: 
library(remotes)
install_github("angeella/fMRIdata")
library(fMRIdata)
data(Auditory_clusterTH3_2)
data(Auditory_copes)
data(Auditory_mask)
auditory_out <- pARIbrain(copes = Auditory_copes, 
clusters = Auditory_clusterTH3_2, mask = Auditory_mask, 
alpha = 0.05, silent = TRUE)
auditory_out$out

## End(Not run)
## Not run: 
library(remotes)
install_github("angeella/fMRIdata")
library(fMRIdata)
data(Auditory_clusterTH3_2)
data(Auditory_copes)
data(Auditory_mask)
auditory_out <- pARIbrain(copes = Auditory_copes, 
clusters = Auditory_clusterTH3_2, mask = Auditory_mask, 
alpha = 0.05, silent = TRUE)
auditory_out$out

## End(Not run)

Permutation-based All-Resolutions Inference for Gene Expression Data

Description

This function computes the lower bound for the number of true discoveries within each cluster (pathways) of Gene Expression Data.

Usage

pARIgene(X= NULL, pathways, alpha = 0.05, family = "simes", delta = 0, 
B = 1000, test.type = "one_sample", complete = FALSE, iterative = FALSE, 
approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)
pARIgene(X= NULL, pathways, alpha = 0.05, family = "simes", delta = 0, 
B = 1000, test.type = "one_sample", complete = FALSE, iterative = FALSE, 
approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)

Arguments

`X`	Data matrix where rows represent the $m$ variables and columns the $n$ observations.
`pathways`	List of pathways where names indicates the name of the pathway.
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.
`family`	String character. Name of the family confidence envelope to compute the critical vector from `"simes"`, `"aorc"`, `"beta"`, `"higher.criticism"`, and `"power"`. Default to "simes".
`delta`	Numeric value. $\delta$ value. Please see the reference below. Default to 0.
`B`	Numeric value. Number of permutations, default to 1000.
`test.type`	Character string. Choose a type of tests among `"one_sample"`, i.e., one-sample t-tests, or `"two_samples"`, i.e., two-samples t-tests. Default `"one_sample"`.
`complete`	Boolean value. If `TRUE` the sets of critical vectors and the raw $p$ -values are returned. Default to `FALSE`.
`iterative`	Boolean value. If `iterative = TRUE`, the iterative method is applied (computationally demanding). Default to `FALSE`. Please see the reference below.
`approx`	Boolean value. Default to `TRUE`. If you are analyzing high dimensional data, we suggest to put `approx = TRUE` to speed up the computation time. Please see the reference below.
`ncomb`	Numeric value. If `approx = TRUE`, you must decide how many random sub collections (level of approximation) considered. Default to 100.
`step.down`	Boolean value. Default to `FALSE` If you want to compute the lambda calibration parameter using the step-down approach put `TRUE`. Please see the reference below.
`max.step`	Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when `step.down = TRUE`.
`...`	Further arguments

Value

by default returns a list with the following objects:

discoveries: lower bound for the number of true discoveries in the set selected
ix: selected variables

If complete = TRUE the raw pvalues and cv critical vector are also returned.

Author(s)

Angela Andreella

References

For the general framework of All-Resolutions Inference see:

Goeman, Jelle J., and Aldo Solari. "Multiple testing for exploratory research. " Statistical Science 26.4 (2011): 584-597.

For permutation-based All-Resolutions Inference see:

Permutation Test

Description

Performs permutation-based two-sample t-tests.

Usage

permTest(X, B = 1000, alternative = "two.sided", seed = NULL, 
mask = NULL, rand = FALSE, label = NULL)
permTest(X, B = 1000, alternative = "two.sided", seed = NULL, 
mask = NULL, rand = FALSE, label = NULL)

Arguments

`X`	Data matrix where rows represent the $m$ variables and columns the $n$ observations.
`B`	Numeric value. Number of permutations, default to 1000.
`alternative`	Character string. It refers to the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"lower"`.
`seed`	Integer value. If you want to specify the seed. Default to to `NULL`
`mask`	NIfTI file or character string. 3D array of logical values (i.e. `TRUE/FALSE` in/out of the brain). Alternatively it may be a (character) NIfTI file name. If `mask=NULL`, it is assumed that none of the voxels have to be excluded.
`rand`	Boolean value. Default to `FALSE`. If `rand = TRUE`, the $p$ -values are computed by `rowRanks`.
`label`	Numeric/character vector. Labels of the observations, if `NULL` the columns's name are considered. Default to `NULL`.

Value

Returns a list with the following objects:

Test: Vector with length equals $m$ . Observed two-samples t-tests, one for each $m$ variable
Test_H0: Matrix with dimensions $m \times B-1$ . Test statistics under the null hypothesis
pv: Vector with length equals $m$ . Observed $p$ -values, one for each $m$ variable
pv_H0: Matrix with dimensions $m \times B-1$ . $p$ -values under the null hypothesis

Author(s)

Angela Andreella

Examples

X <- matrix(rnorm(100*20), ncol=20)
X[,1:10] <- X[,1:10] + rnorm(100*10, mean = 5)
out <- permTest(X = X, alternative = "two.sided", label = c(rep(1,10),rep(0,10)))
X <- matrix(rnorm(100*20), ncol=20)
X[,1:10] <- X[,1:10] + rnorm(100*10, mean = 5)
out <- permTest(X = X, alternative = "two.sided", label = c(rep(1,10),rep(0,10)))

Plot permutation $p$ -values distribution

Description

Create a plot of permutation-based $p$ -values with corresponding specified critical vectors.

Usage

plotNullDistribution(P=NULL,family="simes",alpha = 0.05, 
path = getwd(), name = "plot", delta = 0,
copes=NULL,mask=NULL, alternative = "two.sided", rand = FALSE, B = 1000)
plotNullDistribution(P=NULL,family="simes",alpha = 0.05, 
path = getwd(), name = "plot", delta = 0,
copes=NULL,mask=NULL, alternative = "two.sided", rand = FALSE, B = 1000)

Arguments

`P`	Matrix of $p$ -values with dimensions $m \times B$ where $m$ is the number of variables and $B$ the number of permutations used instead of the data matrix `X`. Default to `NULL`.
`family`	String character. Name of the family confidence envelope to compute the critical vector from `"simes"`, `"aorc"`, `"beta"`, `"higher.criticism"`, and `"power"`. Default to "simes". If more than one critical vector are considered, it must be a vector.
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.
`path`	Character string. Path to save the plot. The path does not must end with `/`. Default to `getwd()`.
`name`	Character string. The name of file that will be used to save the plot. Default to "plot".
`delta`	Numeric value. $\delta$ value. Please see the reference below. Default to 0. If more than one critical vector are considered, `delta` must be a vector having length equals to the length of the vector specified in `family`.
`copes`	List of NIfTI file. The list of copes, i.e., contrasts maps, one for each subject used to compute the statistical tests.
`mask`	NIfTI file or character string. 3D array of logical values (i.e. `TRUE/FALSE` in/out of the brain). Alternatively it may be a (character) NIfTI file name. If `mask=NULL`, it is assumed that none of the voxels have to be excluded.
`alternative`	Character string. It refers to the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"lower"`.
`rand`	Boolean value. Default to `FALSE`. If `rand = TRUE`, the $p$ -values are computed by `rowRanks`.
`B`	Numeric value. Number of permutations, default to 1000.

Value

Save a plot in path with name specified in name describing the $p$ -values null distribution with critical value curve and observed $p$ -values in red.

Author(s)

Angela Andreella

References

Examples

## Not run: 
db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
plotNullDistribution(P = pv)

## End(Not run)

## Not run: 
db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
plotNullDistribution(P = pv)

## End(Not run)

Permutatation-based one-sample t-tests

Description

Performs sign-flipped one-sample t-tests.

Usage

signTest(X, B = 1000, alternative = "two.sided", seed = NULL, mask = NULL, rand = FALSE)
signTest(X, B = 1000, alternative = "two.sided", seed = NULL, mask = NULL, rand = FALSE)

Arguments

`X`	Data matrix where rows represent the $m$ variables and columns the $n$ observations.
`B`	Numeric value. Number of permutations, default to 1000.
`alternative`	Character string. It refers to the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"lower"`.
`seed`	Integer value. If you want to specify the seed. Default to to `NULL`
`mask`	NIfTI file or character string. 3D array of logical values (i.e. `TRUE/FALSE` in/out of the brain). Alternatively it may be a (character) NIfTI file name. If `mask=NULL`, it is assumed that none of the voxels have to be excluded.
`rand`	Boolean value. Default to `FALSE`. If `rand = TRUE`, the $p$ -values are computed by `rowRanks`.

Value

Returns a list with the following objects:

Test: Vector with length equals $m$ . Observed two-samples t-tests, one for each $m$ variable
Test_H0: Matrix with dimensions $m \times B-1$ . Test statistics under the null hypothesis
pv: Vector with length equals $m$ . Observed $p$ -values, one for each $m$ variable
pv_H0: Matrix with dimensions $m \times B-1$ . $p$ -values under the null hypothesis

Author(s)

Angela Andreella

Examples

X <- matrix(rnorm(100*20), ncol=20)
out <- signTest(X = X, alternative = "two.sided") 
X <- matrix(rnorm(100*20), ncol=20)
out <- signTest(X = X, alternative = "two.sided")

simulate normal distributed data

Description

Simulate normal distributed data.

Usage

simulateData(pi0,m,n, rho, seed = NULL, power = 0.8, alpha = 0.05)
simulateData(pi0,m,n, rho, seed = NULL, power = 0.8, alpha = 0.05)

Arguments

`pi0`	Numeric value in '[0,1]'. Proportion of true null hypothesis.
`m`	Numeric value. Number of variables.
`n`	Numeric value. Number of observations.
`rho`	Numeric value in '[0,1]'. Level of equi-correlation between pairs of variables.
`seed`	Integer value. If you want to specify the seed. Default to to `NULL`
`power`	Numeric value in '[0,1]'. Level of power. Default to 0.8.
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.

Value

Returns a matrix with dimensions $m \times n$ .

Author(s)

Angela Andreella

simulate normal distributed data

Description

Simulate normal distributed data with spatial correlation structure

theta ( $\theta$ ) describes how rapidly the correlation declines with respect to the distance between two voxels. The three-dimensional coordinates of the voxels are defined as all combinations of vector $c = (1, \dots, m1/3)$ , then $\Sigma_\theta = \exp(-\theta K)$ where $K$ is the matrix containing the euclidean distances between the three-dimensional coordinates' voxels. So, $m^{1/3}$ must be an integer value.

Usage

simulateSpatialData(pi0,m,n, theta, seed = NULL, power = 0.8, alpha = 0.05)
simulateSpatialData(pi0,m,n, theta, seed = NULL, power = 0.8, alpha = 0.05)

Arguments

`pi0`	Numeric value in '[0,1]'. Proportion of true null hypothesis.
`m`	Numeric value. Number of variables.
`n`	Numeric value. Number of observations.
`theta`	Numeric value in '[0,1]'. Level of correlation between pairs of variables. See details
`seed`	Integer value. If you want to specify the seed. Default to to `NULL`
`power`	Numeric value in '[0,1]'. Level of power. Default to 0.8.
`alpha`	Numeric value in '[0,1]'. $\alpha$ level to control the family-wise error rate. Default to 0.05.

Value

Returns a matrix with dimensions $m \times n$ .

Author(s)

Angela Andreella

Create Statistical Parametric Mapping (SPM)

Description

Creates the statistical parametric mapping in NIfTI format.

Usage

Statmap(copes, alternative = "two.sided", path = getwd(), 
name = "map", Pmap = FALSE, mask = NULL)
Statmap(copes, alternative = "two.sided", path = getwd(), 
name = "map", Pmap = FALSE, mask = NULL)

Arguments

`copes`	List of NIfTI file. The list of copes, i.e., contrasts maps, one for each subject used to compute the statistical tests.
`alternative`	Character string. It refers to the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"lower"`.
`path`	Character string. Path to save the plot. The path does not must end with `/`. Default to `getwd()`.
`name`	Character string. The name of file that will be used to save the plot. Default to "map".
`Pmap`	Boolean value. If `TRUE` the SPM of the $p$ -values is returned. Default to `FALSE`.
`mask`	NIfTI file or character string. 3D array of logical values (i.e. `TRUE/FALSE` in/out of the brain). Alternatively it may be a (character) NIfTI file name. If `mask=NULL`, it is assumed that none of the voxels have to be excluded.

Value

Save the Statistical Parametric Mapping Nifti file in path with name specified in name.

Author(s)

Angela Andreella

Examples

## Not run: 
library(fMRIdata)
data(Auditory_copes)
data(Auditory_mask)
Statmap(copes = Auditory_copes, mask = Auditory_mask)

## End(Not run)
## Not run: 
library(fMRIdata)
data(Auditory_copes)
data(Auditory_mask)
Statmap(copes = Auditory_copes, mask = Auditory_mask)

## End(Not run)

Package 'pARI'

Help Index

Critical vector

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Lower bound for the number of true discoveries

Description

Usage

Arguments

Value

Author(s)

References

Examples

Lambda calibration

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

True Discovery Proportion brain map

Description

Usage

Arguments

Value

Author(s)

Permutation-based All-Resolutions Inference

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Permutation-based All-Resolutions Inference for brain imaging.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Permutation-based All-Resolutions Inference for Gene Expression Data

Description

Usage

Arguments

Value

Author(s)

References

See Also

Permutation Test

Description

Usage

Arguments

Value

Author(s)

Examples

Plot permutation ppp-values distribution

Description

Usage

Arguments

Value

Author(s)

References

Examples

Permutatation-based one-sample t-tests

Description

Usage

Arguments

Value

Plot permutation $p$ -values distribution