# Computation of the regime switch time in a (two-way or three-way) panel threshold regression model. A measure for computing the time at which a regime changes, i.e. the change point. ```r cpoint(data., nameI=NA, nameT=NA, nameJ=NA, nameY=NA, thresholds) ``` ## Arguments - `data.`: A (two-way or three-way) panel dataset. It can be either a $N\times T$ matrix of the dependent variable(s) where the row represents the statistical unit ($i$) and the column the time ($t$) or a `data.frame` containing columns `nameI`, `nameT`, `nameJ`, and `nameY`, which should all be specified. - `nameI`: If data. is a `data.frame`, the name of the (numerical) variable that identifies the statistical units. Otherwise, it is `NULL`. - `nameT`: If data. is a `data.frame`, the name of the (numerical) time variable. Otherwise, it is `NULL`. - `nameJ`: If data. is a `data.frame`, the name of the (numerical/categorical) variable that indicates the third dimension. Otherwise, it is `NULL`. - `nameY`: If data. is a `data.frame`, the name of the (numerical) dependent variable. Otherwise, it is `NULL`. - `thresholds`: The vector of estimated threshold value(s). If data. is a `matrix`, the object should contain one value $\gamma$. If data. is a `data.frame`, the vector should contain one threshold value per each value of j, i.e. $\gamma_j$. ## Details `cpoint` computes the change point on the dependent variable ($Y$) for each statistical unit i given the time series of $Y$ and the estimated threshold value $\gamma_{ij}$. If the observed value $Y$ is greater (lower) than the estimated threshold parameter, $t=4$ belongs to the upper (lower) regime, and the change point is identified as the time $t$ after which the longest sequence of days in the lower (upper) regime is observed. ## Returns An object of S4 class "cpoint", which is a `matrix` reporting the change point per each statistical unit $i$ and each $j$. ## References Di Lascio, F.M.L. and Perazzini, S. (202x) A three-way dynamic panel threshold regression model for change point detection in bioimpedance data. WP BEMPS . Di Lascio, F.M.L. and Perazzini, S. (2022) Change point detection in fruit bioimpedance using a three-way panel model. Book of short papers - SIS2022, p.1184-1189, Eds. A. Balzanella, M. Bini, C. Cavicchia, R. Verde. Pearson. ISBN: 978-88-9193-231-0. ## Note The change point is detected in $t>4$ since the criterion has been inspired by the (two-way or three-way) dynamic panel threshold regression model that requires at least 6 times of observation: four lags of the dependent and independent variables are used as instruments and two more are necessary to identify the regime switch (i.e., one per regime). ## Author(s) Francesca Marta Lilja Di Lascio Selene Perazzini ## See Also See also `banana`, `ptm2`, and `ptm3`. ## Examples ```r # Import data data(banana) ## Example 1: Application to a three-way panel data # Select data for all the three levels of the third way bioimp.df2 <- banana # Estimation: three-way ptm on bioimpedance data with the lagged # dependent variable as transition variable estimates2 <- ptm3(data.=bioimp.df2, nameI="i", nameT="t", nameJ="j", nameY="bioimpedance", nameTV=NULL, nameXexo="weight", nameIV=NULL, trimrate=0.4, ngrid=100, h0=1.5, Iweight=FALSE, test.lin=FALSE) # Change point computation using a data frame (unique input choice) CP3 <- cpoint(data.=bioimp.df2, nameI="i", nameT="t", nameJ="j", nameY="bioimpedance", thresholds=estimates2@"threshold"[,2]) ## NOT RUN ### Example 2: Application to a two-way panel data # ## Select data for a specific level of the third way #bioimp.df <- banana[which(banana$j==1),] # ## Prepare input matrix for ptm2 #bioimp.m <- matrix(bioimp.df$bioimpedance, # ncol=length(unique(bioimp.df$t)), byrow=TRUE) # ## Estimation: two-way ptm on bioimpedance data with the lagged ## dependent variable as transition variable #estimates <- ptm2(Y=bioimp.m, TV=NULL, Xendo=NULL, Xexo=NULL, # IV=NULL, trimrate=0.4, ngrid=100, h0=1.5, # Iweight=FALSE, test.lin=FALSE) # ## Change point computation using a data frame as input #CP1 <- cpoint(data.=bioimp.df, nameI="i", nameT="t", nameJ="j", # nameY="bioimpedance", # thresholds=estimates@"threshold"[[1]]) # ## Change point computation using a data matrix as input #CP2 <- cpoint(data.=bioimp.m, nameI=NA, nameT=NA, nameJ=NA, # nameY=NA, thresholds=estimates@"threshold"[[1]]) ## ```

cpoint function