1d:T619,# Estimate the restricted Markov equivalence class using Temporal Greedy Equivalence Search Perform causal discovery using the temporal greedy equivalence search algorithm. ```r tges(score, verbose = FALSE) ``` ## Arguments - `score`: tiered scoring object to be used. At the moment only scores supported re `TemporalBIC` and `TemporalBDeu`. - `verbose`: indicates whether debug output should be printed. ## Returns `tges` returns a `tamat` object which is a matrix with a "order" attribute (a character vector listing the temporal order of the variables in the adjacency matrix). ## Examples ```r #Simulate Gaussian data set.seed(123) n <- 500 child_x <- rnorm(n) child_y <- 0.5*child_x + rnorm(n) child_z <- 2*child_x + child_y + rnorm(n) adult_x <- child_x + rnorm(n) adult_z <- child_z + rnorm(n) adult_w <- 2*adult_z + rnorm(n) adult_y <- 2*child_x + adult_w + rnorm(n) simdata <- data.frame(child_x, child_y, child_z, adult_x, adult_z, adult_w, adult_y) # Define order in prefix way prefix_order <- c("child", "adult") # Define TBIC score t_score <- new("TemporalBIC", order = prefix_order , data = simdata) # Run tges tges_pre <- tges(t_score) # Plot MPDAG plot(tges_pre) # Define order in integer way integer_order <- c(1,1,1,2,2,2,2) # Define TBIC score t_score <- new("TemporalBIC", order = integer_order , data = simdata) # Run tges tges_int <- tges(t_score) # Plot MPDAG plot(tges_int) ``` ## See Also `TemporalBIC` ## Author(s) Tobias Ellegaard Larsen1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584 10.3238 2.49584 10.3238C3.22353 10.375 3.60629 11.0711 3.60629 11.0711C4.25298 12.1788 5.30335 11.8588 5.71638 11.6732C5.78225 11.205 5.96962 10.8854 6.17658 10.704

tges function