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IVPP

R-CMD-check Lifecycle: stable CRAN status CRAN Downloads Codecov test coverage

An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (in prep) to comparing idiographic and panel network models. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning.

Installation

To install from CRAN:

install.packages("IVPP")

You can install the development version of IVPP from GitHub with:

# install.packages("devtools")
devtools::install_github("xinkaidupsy/IVPP")

Example

An example that uses IVPP to compare panelGVAR models:

library(IVPP)

# Generate the network
net_ls <- gen_panelGVAR(n_node = 6,
                        p_rewire_temp = 0.5,
                        p_rewire_cont = 0.5,
                        n_group = 2)

# Generate the data
data <- sim_panelGVAR(temp_base_ls = net_ls$temporal,
                      cont_base_ls = net_ls$omega_zeta_within,
                      n_person = 200,
                      n_time = 3,
                      n_group = 2,
                      n_node = 6)

# global test on both nets
omnibus_both <- IVPP_panelgvar(data,
                               vars = paste0("V",1:6),
                               idvar = "subject",
                               beepvar = "time",
                               groups = "group",
                               g_test_net = "both",
                               net_type = "sparse",
                               partial_prune = FALSE,
                               ncores = 2)

# global test on temporal
omnibus_temp <- IVPP_panelgvar(data,
                               vars = paste0("V",1:6),
                               idvar = "subject",
                               beepvar = "time",
                               groups = "group",
                               g_test_net = "temporal",
                               net_type = "sparse",
                               partial_prune = FALSE,
                               ncores = 2)

# global test on cont
omnibus_cont <- IVPP_panelgvar(data,
                               vars = paste0("V",1:6),
                               idvar = "subject",
                               beepvar = "time",
                               groups = "group",
                               g_test_net = "contemporaneous",
                               net_type = "sparse",
                               partial_prune = FALSE,
                               ncores = 2)

# partial prune on both networks
pp_both <- IVPP_panelgvar(data,
                          vars = paste0("V",1:6),
                          idvar = "subject",
                          beepvar = "time",
                          groups = "group",
                          global = FALSE,
                          net_type = "sparse",
                          partial_prune = TRUE,
                          prune_net = "both",
                          ncores = 2)

An example that uses IVPP to compare N = 1 GVAR models

library(IVPP)

# Generate the network
net_ls <- gen_tsGVAR(n_node = 6,
                     p_rewire_temp = 0.5,
                     p_rewire_cont = 0.5,
                     n_persons = 2)

# Generate the data
data <- sim_tsGVAR(beta_base_ls = net_ls$beta,
                   kappa_base_ls = net_ls$kappa,
                   # n_person = 2,
                   n_time = 100)

# global test on temporal
omnibus_temp <- IVPP_tsgvar(data,
                            vars = paste0("V",1:6),
                            idvar = "id",
                            g_test_net = "temporal",
                            net_type = "sparse",
                            partial_prune = FALSE,
                            ncores = 2)

# global test on cont
omnibus_cont <- IVPP_tsgvar(data,
                            vars = paste0("V",1:6),
                            idvar = "id",
                            g_test_net = "contemporaneous",
                            net_type = "sparse",
                            partial_prune = FALSE,
                            ncores = 2)

# partial prune on both networks
pp_both <- IVPP_tsgvar(data,
                       vars = paste0("V",1:6),
                       idvar = "id",
                       global = FALSE,
                       net_type = "sparse",
                       partial_prune = TRUE,
                       prune_net = "both",
                       ncores = 2)

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.
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