| Title: | Invariance Partial Pruning Test |
|---|---|
| Description: | An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. 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. |
| Authors: | Xinkai Du [aut, cre, cph] |
| Maintainer: | Xinkai Du <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.0 |
| Built: | 2025-03-31 07:19:37 UTC |
| Source: | https://github.com/xinkaidupsy/ivpp |
This function generates a (multi-group) panel GVAR model. Currently generating temporal and contemporaneous networks
gen_panelGVAR( n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0.5, n_group = 1 )gen_panelGVAR( n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0.5, n_group = 1 )
n_node |
an integer denoting the number of nodes |
p_rewire_temp |
a numeric value between 0-1 denoting the extent of group difference in the temporal network |
p_rewire_cont |
a numeric value between 0-1 denoting the extent of group difference in the contemporaneous network |
n_group |
an integer denoting the number of groups |
beta can be transposed to obtain the temporal network;
PDC is the partial directed correlation matrix, which is a standardized version of temporal network;
kappa is the precision matrix denoting conditional (in)dependence,
which is a inverse of covariance matrix denoting the (dependence) among variables;
kappa can be further standardized to the contemporaneous networks (omega_zeta_within)
A list of beta, PDC, kappa and contemporaneous networks
Xinkai Du Maintainer: Xinkai Du [email protected]
library(IVPP) # Generate the network net_ls <- gen_panelGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_group = 2)library(IVPP) # Generate the network net_ls <- gen_panelGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_group = 2)
This function generates time-series GVAR model for multiple individuals that demonstrates difference or simularity. Currently generating temporal and contemporaneous networks
gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0.5, n_persons = 1)gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0.5, n_persons = 1)
n_node |
an integer denoting the number of nodes |
p_rewire_temp |
a numeric value between 0-1 denoting the extent of individual difference in the temporal network |
p_rewire_cont |
a numeric value between 0-1 denoting the extent of individual difference in the contemporaneous network |
n_persons |
an integer denoting the number of individuals to generate tsGVAR for |
beta can be transposed to obtain the temporal network;
PDC is the partial directed correlation matrix, which is a standardized version of temporal network;
kappa is the precision matrix denoting conditional (in)dependence,
which is a inverse of covariance matrix denoting the (dependence) among variables;
kappa can be further standardized to the contemporaneous networks (omega_zeta_within)
A list of beta, PDC, kappa and contemporaneous networks
Xinkai Du Maintainer: Xinkai Du [email protected]
library(IVPP) # Generate the network net_ls <- gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_persons = 2)library(IVPP) # Generate the network net_ls <- gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_persons = 2)
This function implements the IVPP algorithm to compare networks in the multi-group panelGVAR models. The IVPP algorithm is a two-step procedure that first conducts an global invariance test of network difference and then performs partial pruning for the specific edge-level differences. Currently supports the comparison of temporal and contemporaneous networks.
IVPP_panelgvar( data, vars, idvar, beepvar, groups, global = TRUE, g_test_net = c("both", "temporal", "contemporaneous"), net_type = c("saturated", "sparse"), partial_prune = FALSE, prune_net = c("both", "temporal", "contemporaneous"), prune_alpha = 0.01, p_prune_alpha = 0.01, estimator = "FIML", standardize = c("none", "z", "quantile"), ncores = 1, ... )IVPP_panelgvar( data, vars, idvar, beepvar, groups, global = TRUE, g_test_net = c("both", "temporal", "contemporaneous"), net_type = c("saturated", "sparse"), partial_prune = FALSE, prune_net = c("both", "temporal", "contemporaneous"), prune_alpha = 0.01, p_prune_alpha = 0.01, estimator = "FIML", standardize = c("none", "z", "quantile"), ncores = 1, ... )
data |
A data frame containing the long-formatted panel data |
vars |
A character vector of variable names |
idvar |
A character string specifying subject IDs |
beepvar |
A character string specifying the name of wave (time) variable |
groups |
A character string specifying the name of group variable |
global |
A logical value default to TRUE. If FALSE, the global invariance test is skipped. |
g_test_net |
A character vector specifying the network you want to test group-equality on in the global invariance test. Specify "both" if you want to test on both temporal or contemporaneous networks. Specify "temporal" if you want to test only on the temporal network. Specify "contemporaneous" if you want to test only on the contemporaneous network. See the Details section for more information. |
net_type |
A character vector specifying the type of networks to be compared in the global test. Specify "saturated" if you want to estimate and compare the saturated networks. Specify "sparse" if you want to estimate and compare the pruned networks. |
partial_prune |
A logical value specifying whether to conduct partial pruning test or not. |
prune_net |
A character vector specifying the network you want to partial prune on. Only works when partial_prune = TRUE. |
prune_alpha |
A numeric value specifying the alpha level for the pruning (if net_type = "sparse"). |
p_prune_alpha |
A numeric value specifying the alpha level for the partial pruning (if partial_prune = TRUE). |
estimator |
A character string specifying the estimator to be used. Must be "FIML" |
standardize |
A character string specifying the type of standardization to be used. "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution. |
ncores |
A numeric value specifying the number of cores you want to use to run the analysis. Default to 1. |
... |
Additional arguments to be passed to the |
The comparison between the fully unconstrained (free) model and tempEq model is a test for group equality in temporal networks. The comparison between fully constrained model (bothEq) and tempEq is a test for group equality in contemporaneous networks. Similarly, the comparison between the free model and contEq model is a test for group equality in contemporaneous networks, and the comparison between bothEq and contEq is a test for group equality in temporal networks.
Xinkai Du Maintainer: Xinkai Du [email protected]
@return A list containing the results of IVPP and networks of all groups.
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 = 1) # 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 = 1) # 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 = 1) # 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 = 1)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 = 1) # 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 = 1) # 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 = 1) # 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 = 1)
This function implements the IVPP algorithm to compare networks in the multi-group panelGVAR models. The IVPP algorithm is a two-step procedure that first conducts an global invariance test of network difference and then performs partial pruning for the specific edge-level differences. Currently supports the comparison of temporal and contemporaneous networks.
IVPP_tsgvar( data, vars, idvar, dayvar, beepvar, global = TRUE, g_test_net = c("both", "temporal", "contemporaneous"), net_type = c("saturated", "sparse"), partial_prune = FALSE, prune_net = c("both", "temporal", "contemporaneous"), prune_alpha = 0.01, p_prune_alpha = 0.01, estimator = "FIML", standardize = c("none", "z", "quantile"), ncores = 1, ... )IVPP_tsgvar( data, vars, idvar, dayvar, beepvar, global = TRUE, g_test_net = c("both", "temporal", "contemporaneous"), net_type = c("saturated", "sparse"), partial_prune = FALSE, prune_net = c("both", "temporal", "contemporaneous"), prune_alpha = 0.01, p_prune_alpha = 0.01, estimator = "FIML", standardize = c("none", "z", "quantile"), ncores = 1, ... )
data |
A data frame containing the long-formatted panel data |
vars |
A character vector of variable names |
idvar |
A character string specifying the IDs of subjects you want to compare |
dayvar |
A character string specifying the name of day variable |
beepvar |
A character string specifying the name of variable indicating the measurement number at each day |
global |
A logical value default to TRUE. If FALSE, the global invariance test is skipped. |
g_test_net |
A character vector specifying the network you want to test group-equality on in the global invariance test. Specify "both" if you want to test on both temporal or contemporaneous networks. Specify "temporal" if you want to test only on the temporal network. Specify "contemporaneous" if you want to test only on the contemporaneous network. See the Details section for more information. |
net_type |
A character vector specifying the type of networks to be compared in the global test. Specify "saturated" if you want to estimate and compare the saturated networks. Specify "sparse" if you want to estimate and compare the pruned networks. |
partial_prune |
A logical value specifying whether to conduct partial pruning test or not. |
prune_net |
A character vector specifying the network you want to partial prune on. Only works when partial_prune = TRUE. |
prune_alpha |
A numeric value specifying the alpha level for the pruning (if net_type = "sparse"). |
p_prune_alpha |
A numeric value specifying the alpha level for the partial pruning (if partial_prune = TRUE). |
estimator |
A character string specifying the estimator to be used. Must be "FIML" |
standardize |
A character string specifying the type of standardization to be used. "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution. |
ncores |
A numeric value specifying the number of cores you want to use to run the analysis. Default to 1. |
... |
Additional arguments to be passed to the |
The comparison between the fully unconstrained (free) model and tempEq model is a test for group equality in temporal networks. The comparison between fully constrained model (bothEq) and tempEq is a test for group equality in contemporaneous networks. Similarly, the comparison between the free model and contEq model is a test for group equality in contemporaneous networks, and the comparison between bothEq and contEq is a test for group equality in temporal networks.
A list containing the results of IVPP and networks of all groups.
Xinkai Du Maintainer: Xinkai Du [email protected]
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 = 1) # 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 = 1) # 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 = 1)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 = 1) # 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 = 1) # 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 = 1)
This function generates data for the input (multi-group) panelGVAR model
sim_panelGVAR( temp_base_ls, beta_base_ls, cont_base_ls, n_node, n_person = 500, n_time = 3, n_group, mean_trend = 0, p_rewire_temp = 0, p_rewire_cont = 0, save_nets = FALSE )sim_panelGVAR( temp_base_ls, beta_base_ls, cont_base_ls, n_node, n_person = 500, n_time = 3, n_group, mean_trend = 0, p_rewire_temp = 0, p_rewire_cont = 0, save_nets = FALSE )
temp_base_ls |
a list of temporal networks of all groups |
beta_base_ls |
a list of beta matrices of all groups |
cont_base_ls |
a list of contemporaneous networks of all groups |
n_node |
number of nodes |
n_person |
an integer denoting the sample size of each group, default to 500 |
n_time |
number of waves, default to 3 |
n_group |
number of groups |
mean_trend |
a numeric value indicating the extent of mean trends in data, default to 0 |
p_rewire_temp |
a numeric value between 0 and 1 indicating the extent of non-stationarity in temporal networks, default to 0 |
p_rewire_cont |
a numeric value between 0 and 1 indicating the extent of non-stationarity in contemporaneous networks, default to 0 |
save_nets |
a logical value indicating whether to save the data-generating networks, default to FALSE |
A list of temporal and contemporaneous networks
Xinkai Du Maintainer: Xinkai Du [email protected]
library(IVPP) # Generate the network net_ls <- gen_panelGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_group = 3) # Generate the data data <- sim_panelGVAR(temp_base_ls = net_ls$temporal, cont_base_ls = net_ls$omega_zeta_within, n_person = 500, n_time = 4, n_group = 3, n_node = 6)library(IVPP) # Generate the network net_ls <- gen_panelGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_group = 3) # Generate the data data <- sim_panelGVAR(temp_base_ls = net_ls$temporal, cont_base_ls = net_ls$omega_zeta_within, n_person = 500, n_time = 4, n_group = 3, n_node = 6)
This function generates data for the input (multi-group) N = 1 GVAR model
sim_tsGVAR( temp_base_ls, beta_base_ls, cont_base_ls, kappa_base_ls, n_node, n_time = 50, n_person, save_nets = FALSE )sim_tsGVAR( temp_base_ls, beta_base_ls, cont_base_ls, kappa_base_ls, n_node, n_time = 50, n_person, save_nets = FALSE )
temp_base_ls |
a list of temporal networks of all individuals |
beta_base_ls |
a list of beta matrices of all individuals |
cont_base_ls |
a list of contemporaneous networks of all individuals |
kappa_base_ls |
a list of precision matricies of all individuals |
n_node |
number of nodes |
n_time |
number of measurements per person, default to 50 |
n_person |
number of individuals to generate data for |
save_nets |
a logical value indicating whether to save the data-generating networks, default to FALSE |
A list of temporal and contemporaneous networks
Xinkai Du Maintainer: Xinkai Du [email protected]
library(IVPP) # Generate the network net_ls <- gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_persons = 3) # Generate the data data <- sim_tsGVAR(beta_base_ls = net_ls$beta, kappa_base_ls = net_ls$kappa, # n_person = 3, n_time = 50)library(IVPP) # Generate the network net_ls <- gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0, n_persons = 3) # Generate the data data <- sim_tsGVAR(beta_base_ls = net_ls$beta, kappa_base_ls = net_ls$kappa, # n_person = 3, n_time = 50)