Estimate GMM via nodewise regression and hypothesis tests.

Description

Estimate Gaussian Graphical Model with nodewise regression, selecting edges with standard hypothesis tests and the Bonferroni-Holm Correction.

Usage

OLS(data, pbar = TRUE, correction = TRUE,
    ci.level = 0.95, rulereg = "and")

Arguments

Value

The function returns a list with the following entries:

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>

References

Hochberg, Y., & Tamhane, A. C. (1987). Multiple comparison procedures. John Wiley & Sons, Inc..

Examples

# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
out <- OLS(data = data)

## Not run: 

# Fit GGM to PTSD data
out <- OLS(data = ptsd_data)


## End(Not run)

Datasets included in inet package

Description

The package includes a dataset with measurements of 17 PTSD symptoms taken from 344 individuals. See McNally et al. (2015) for more details.

Author(s)

Jonas Haslbeck

References

McNally, R. J., Robinaugh, D. J., Wu, G. W., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3(6), 836-849.

Internal inet functions

Description

Internal inet functions.

Details

These are internal functions.

Value

The only internal function is one that performs input checks for the estimation functions. They return informative errors, if the inputs are not specified properly.

Author(s)

Jonas Haslbeck

Estimate GGM with nodewise regression and the lasso.

Description

Estimate a Gaussian Graphical Model with lasso-regularized nodewise regression, where the regularization parameter is selected with cross-validation. This is a wrapper around the function cv.glmnet() from the glmnet package.

Usage

lasso(data, pbar = TRUE, nfolds = 10, rulereg = "and")

Arguments

Value

The function returns a list with the following entries:

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>

References

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.

Examples

# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
set.seed(1)
out <- lasso(data = data)

## Not run: 

# Fit GGM to PTSD data
set.seed(1)
out <- lasso(data = ptsd_data)


## End(Not run)

Estimate GGMs with the desparsified lasso.

Description

Estimate Gaussian Graphical Models using the desparsified lasso. This is a wrapper around the function lasso.proj of the hdi package.

Usage

lasso_dsp(data, betainit = "cv lasso", ci.level = 0.95,
          correction = TRUE, pbar = TRUE, rulereg = "and")

Arguments

Value

The function returns a list with the following entries:

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>; Lourens Waldorp <waldorp@uva.nl>

References

Hochberg, Y., & Tamhane, A. C. (1987). Multiple comparison procedures. John Wiley & Sons, Inc..

Buehlmann, P., Kalisch, M., & Meier, L. (2014). High-dimensional statistics with a view toward applications in biology. Annual Review of Statistics and Its Application, 1, 255-278.

Examples

# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
set.seed(1)
out <- lasso_dsp(data = data)

## Not run: 

# Fit GGM to PTSD data
set.seed(1)
out <- lasso_dsp(data = ptsd_data)


## End(Not run)

Estimate GGMs with the desparsified lasso using the bootstrap.

Description

Estimate Gaussian Graphical Models using the desparsified lasso using the bootstrap. This is a wrapper around the function lasso.proj of the hdi package.

Usage

    lasso_dsp_boot(data, betainit = "cv lasso", ci.level = 0.95,
                   correction = TRUE, B = 1000, pbar = TRUE,
                   rulereg = "and")

Arguments

Value

The function returns a list with the following entries:

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>; Lourens Waldorp <waldorp@uva.nl>

References

Hochberg, Y., & Tamhane, A. C. (1987). Multiple comparison procedures. John Wiley & Sons, Inc..

Bühlmann, P., Kalisch, M., & Meier, L. (2014). High-dimensional statistics with a view toward applications in biology. Annual Review of Statistics and Its Application, 1, 255-278.

Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (No. 1). Cambridge university press.

Examples

# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
set.seed(1)
out <- lasso_dsp_boot(data = data, B=2)
# !!! NOTE: this is just for testing purposes; B should be a lot higher (default = 1000)

## Not run: 

# Fit GGM to PTSD data
set.seed(1)
out <- lasso_dsp_boot(data = ptsd_data)


## End(Not run)

Estimate GMM with inference via the multi-split method.

Description

Estimate Gaussian Graphical Models with inference base don the multi-split method. This is a wrapper of the function multi.split of the hdi package.

Usage

lasso_ms(data, B = 50, fraction = 0.5, ci.level = 0.95,
         correction = TRUE, pbar = TRUE, rulereg = "and")

Arguments

Value

The function returns a list with the following entries:

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>; Lourens Waldorp <waldorp@uva.nl>

References

Hochberg, Y., & Tamhane, A. C. (1987). Multiple comparison procedures. John Wiley & Sons, Inc..

Wasserman, L., & Roeder, K. (2009). High dimensional variable selection. Annals of statistics, 37(5A), 2178.

Meinshausen, N., Meier, L., & Bühlmann, P. (2009). P-values for high-dimensional regression. Journal of the American Statistical Association, 104(488), 1671-1681.

Examples

# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
set.seed(1)
out <- lasso_ms(data = data, B=2)
# !!! NOTE: this is just for testing purposes; B should a lot higher (default = 50)

## Not run: 

# Fit GGM to empirical PTSD data
set.seed(1)
out <- lasso_ms(data = ptsd_data)


## End(Not run)

Plot point estimates and confidence intervals

Description

Plot point estimates and confidence intervals for models estimated with the lasso_ms, lasso_dsp, lasso_dsp_boot and OLS functions.

Usage

## S3 method for class 'inet'
plot(x, labels = NULL, order = FALSE, subset = NULL,
          cex.labels = 0.80, cex.axis = 0.75, ...)

Arguments

Value

Plots a figure showing point estimates and confidence intervals for all interaction parameters.

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>

Examples

# Quick toy example
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
out <- OLS(data = data)

# point estimates + CIs; show 3 largest effects only
plot(out, labels = colnames(ptsd_data),
     order=TRUE, subset = 1:3)

## Not run: 

# Fit GGM to empirical PTSD data
set.seed(1)
out <- lasso_dsp(data = ptsd_data)

# Plot first 20 edges
plot(out, labels = colnames(ptsd_data),
     order=TRUE, subset = 1:20)


## End(Not run)