##### https://github.com/cran/sparseFLMM

Revision

**3d1203b48f7c62e32d347f479e8b295770f2f806**authored by Jona Cederbaum on**11 September 2020, 11:20:02 UTC**, committed by cran-robot on**11 September 2020, 11:20:02 UTC****1 parent**5f8586c

Tip revision:

**3d1203b48f7c62e32d347f479e8b295770f2f806**authored by**Jona Cederbaum**on**11 September 2020, 11:20:02 UTC****version 0.3.1** Tip revision:

**3d1203b**cov_estimation_tri.R

```
##################################################################################################################
# author: Jona Cederbaum
##################################################################################################################
##################################################################################################################
# description: function to estimate smooth auto-covariance operators using the triangle only and without symmetry
# constraint. Gives out auto-covariances evaluated on a pre-specified grid.
##################################################################################################################
estimate_cov_tri_fun <- function(index_upperTri, use_RI = FALSE, use_simple = FALSE, bf, method = "REML",
d_grid = 100, grid_row, grid_col, same_subject_grid, same_word_grid,
same_curve_grid, same_point_grid, bs, m, use_bam, t,
mp = TRUE, para_estim = FALSE, para_estim_nc,
weights = NULL, np = TRUE, use_discrete = TRUE){
if(use_bam == TRUE){
#######################
# covariance estimation
# using bam
#######################
################
# set cluster
# for estimation
# if specified
################
if(para_estim & !use_discrete){
if(detectCores() > 1){
nc_use <- min(detectCores(), para_estim_nc)
if(.Platform$OS.type=="unix"){
cl_estim <- makeForkCluster(nnodes = nc_use) # only runs on unix
}else{
cl_estim <- makeCluster(nc_use) # also runs on windows
}
}else{
cl_estim <- NULL
}
}else{
cl_estim <- NULL
}
if(!use_RI){
##################
# for crossed fRIs
if(use_discrete){
time_cov_estim <-
system.time(gam1 <- try(bam(cross_vec_bivariate ~ - 1 +
te(row_t_bivariate, col_t_bivariate, by = same_subject, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_word, k = bf[2], bs = c(bs, bs), m = m[[2]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_curve, k = bf[3], bs = c(bs, bs), m = m[[3]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri,
discrete = TRUE, nthreads = para_estim_nc, weights = weights)))
}else{
time_cov_estim <-
system.time(gam1 <- try(bam(cross_vec_bivariate ~ - 1 +
te(row_t_bivariate, col_t_bivariate, by = same_subject, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_word, k = bf[2], bs = c(bs, bs), m = m[[2]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_curve, k = bf[3], bs = c(bs, bs), m = m[[3]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri, cluster = cl_estim, weights = weights)))
}
}else{
if(!use_simple){
#############
# for one fRI
if(use_discrete){
time_cov_estim <-
system.time(gam1 <- try(bam(cross_vec_bivariate ~ 1 + te(row_t_bivariate,
col_t_bivariate, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_curve, k = bf[2], bs = c(bs, bs), m = m[[2]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri,
discrete = TRUE, nthreads = para_estim_nc, weights = weights)))
}else{
time_cov_estim <-
system.time(gam1 <- try(bam(cross_vec_bivariate ~ 1 + te(row_t_bivariate,
col_t_bivariate, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_curve, k = bf[2], bs = c(bs, bs), m = m[[2]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri, cluster = cl_estim, weights = weights)))
}
}else{
########################
# for independent curves
if(use_discrete){
time_cov_estim <-
system.time(gam1 <- try(bam(cross_vec_bivariate ~ 1 + te(row_t_bivariate,
col_t_bivariate, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri,
discrete = TRUE, nthreads = para_estim_nc, weights = weights)))
}else{
time_cov_estim <-
system.time(gam1 <- try(bam(cross_vec_bivariate ~ 1 + te(row_t_bivariate,
col_t_bivariate, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri, cluster = cl_estim, weights = weights)))
}
}
}
##########################
# stop cluster if existing
##########################
if(!is.null(cl_estim)) stopCluster(cl_estim)
}else{
#######################
# covariance estimation
# using gam
#######################
if(!use_RI){
##################
# for crossed fRIs
time_cov_estim <-
system.time(gam1 <- try(gam(cross_vec_bivariate ~ - 1 +
te(row_t_bivariate, col_t_bivariate, by = same_subject, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_word, k = bf[2], bs = c(bs, bs), m = m[[2]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_curve, k = bf[3], bs = c(bs, bs), m = m[[3]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri, weights = weights)))
}else{
#########
#for RI
#########
if(!use_simple){
#############
# for one fRI
time_cov_estim <-
system.time(gam1 <- try(gam(cross_vec_bivariate ~ 1 +
te(row_t_bivariate, col_t_bivariate, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
te(row_t_bivariate, col_t_bivariate, by = same_curve, k = bf[2], bs = c(bs, bs), m = m[[2]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri, weights = weights)))
}else{
########################
# for independent curves
time_cov_estim <-
system.time(gam1 <- try(gam(cross_vec_bivariate ~ 1 +
te(row_t_bivariate, col_t_bivariate, k = bf[1], bs = c(bs, bs), m = m[[1]], mp = mp, np = np) +
same_point, method = method, data = index_upperTri, weights = weights)))
}
}
}
if(class(gam1)[1] != "try-error"){
##############################
# extract smoothing parameters
##############################
sp <- gam1$sp
if(use_RI){
###################
# extract intercept
###################
intercept <- as.numeric(coefficients(gam1)[1])
}
#################
# extract sigmasq
# and truncate
#################
sigmasq <- as.numeric(max(coefficients(gam1)["same_point"], 0))
#################################
# compute the integral of sigmasq
#################################
sigmasq_int <- (max(unlist(t))-min(unlist(t))) * sigmasq
##########################
# construct data frame for
# evaluation on grid
##########################
if(!use_RI){
##################
# for crossed fRIs
grid_data <- data.table(row_t_bivariate = grid_row, col_t_bivariate = grid_col,
same_subject = same_subject_grid, same_word = same_word_grid,
same_curve = same_curve_grid, same_point = same_point_grid)
##########################
# remove unnecessary stuff
##########################
grid_row <- NULL
grid_col <- NULL
same_subject_grid <- NULL
same_word_grid <- NULL
same_curve_grid <- NULL
same_point_grid <- NULL
}else{
###################################
# for one fRI or independent curves
grid_data <- data.table(row_t_bivariate = grid_row, col_t_bivariate = grid_col,
same_subject = same_subject_grid, same_curve = same_curve_grid,
same_point = same_point_grid)
##########################
# remove unnecessary stuff
##########################
grid_row <- NULL
grid_col <- NULL
same_subject_grid <- NULL
same_curve_grid <- NULL
same_point_grid <- NULL
}
####################
# evaluation on grid
####################
time_cov_pred_grid <-
system.time(grid_smooth <- predict(gam1, newdata = grid_data[row_t_bivariate <= col_t_bivariate, ],
na.omit = TRUE, type = "terms"))
#####################
# extract covariances
#####################
if(!use_RI){
##################
# for crossed fRIs
grid_mat_B <- matrix(NA, nrow = d_grid, ncol = d_grid)
grid_mat_B[lower.tri(grid_mat_B, diag = TRUE)] <- grid_smooth[, 2]
grid_mat_B <- t(grid_mat_B)
grid_mat_B[lower.tri(grid_mat_B, diag = TRUE)] <- grid_smooth[, 2]
grid_mat_C <- matrix(NA, nrow = d_grid, ncol = d_grid)
grid_mat_C[lower.tri(grid_mat_C, diag = TRUE)] <- grid_smooth[, 3]
grid_mat_C <- t(grid_mat_C)
grid_mat_C[lower.tri(grid_mat_C, diag = TRUE)] <- grid_smooth[, 3]
grid_mat_E <- matrix(NA, nrow = d_grid, ncol = d_grid)
grid_mat_E[lower.tri(grid_mat_E, diag = TRUE)] <- grid_smooth[, 4]
grid_mat_E <- t(grid_mat_E)
grid_mat_E[lower.tri(grid_mat_E, diag = TRUE)] <- grid_smooth[, 4]
}else{
###################################
# for one fRI or independent curves
grid_mat_B <- matrix(NA, nrow = d_grid, ncol = d_grid)
grid_mat_B[lower.tri(grid_mat_B, diag = TRUE)] <- grid_smooth[, 2] + intercept
grid_mat_B <- t(grid_mat_B)
grid_mat_B[lower.tri(grid_mat_B, diag = TRUE)] <- grid_smooth[, 2] + intercept
grid_mat_C <- matrix(rep(0, length = d_grid^2), ncol = d_grid, nrow = d_grid, byrow = TRUE)
if(!use_simple){
#############
# for one fRI
grid_mat_E <- matrix(NA, nrow = d_grid, ncol = d_grid)
grid_mat_E[lower.tri(grid_mat_E, diag = TRUE)] <- grid_smooth[, 3]
grid_mat_E <- t(grid_mat_E)
grid_mat_E[lower.tri(grid_mat_E, diag = TRUE)] <- grid_smooth[, 3]
}else{
grid_mat_E <- matrix(rep(0, length = d_grid^2), ncol = d_grid, nrow = d_grid, byrow = TRUE)
}
}
grid_smooth <- NULL
grid_data <- NULL
}else{
sigmasq <- NA
sigmasq_int <- NA
grid_mat_B <- NA
grid_mat_C <- NA
grid_mat_E <- NA
smooth_cov_y <- NA
sp <- NA
time_cov_pred_grid <- NA
time_cov_estim <- NA
warning("covariance estimation did not succeed")
}
gam1 <- NULL
###############
# return output
###############
out <- list(sigmasq = sigmasq, sigmasq_int = sigmasq_int, grid_mat_B = grid_mat_B,
grid_mat_C = grid_mat_C, grid_mat_E = grid_mat_E,
sp = sp, time_cov_estim = time_cov_estim,
time_cov_pred_grid = time_cov_pred_grid)
return(out)
}
####################################################################################
```

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