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Type 'q()' to quit R. > ops <- options("crayon.enabled" = FALSE) > library(testthat) > require("covr") Loading required package: covr > > test_check("FRK") Loading required package: FRK Attaching package: 'FRK' The following object is masked from 'package:stats': simulate | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% Maximum EM iterations reached Intercept 0.1053754 Formula: z ~ 1 Assumed response distribution: gaussian Specified link function: identity Method of model fitting: EM Number of datasets: 1 Number of basis functions: 18 Class of basis functions: Basis Number of BAUs [extract using object@BAUs]: 138 Number of observations [extract using object@Z]: 50 Mean obs. variance at BAU level [extract using object@Ve]: 0.01 Fine-scale variance proportionality constant [extract using object@sigma2fshat]: 0.07175991 Dimensions of C in Z = C*Y + e [extract using object@Cmat]: c(50L, 138L) Dimensions of S in Y = X*alpha + S*eta + delta [extract using object@S]: c(50L, 18L) Number of covariates: 1 Formula: z ~ 1 Assumed response distribution: gaussian Specified link function: identity Method of model fitting: EM Number of datasets: 1 Number of basis functions: 18 Class of basis functions: Basis Number of BAUs [extract using object@BAUs]: 138 Number of observations [extract using object@Z]: 50 Mean obs. variance at BAU level [extract using object@Ve]: 0.01 Fine-scale variance proportionality constant [extract using object@sigma2fshat]: 0.07175991 Dimensions of C in Z = C*Y + e [extract using object@Cmat]: c(50L, 138L) Dimensions of S in Y = X*alpha + S*eta + delta [extract using object@S]: c(50L, 18L) Number of covariates: 1 | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% Maximum EM iterations reached Averaging over polygons... | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% Maximum EM iterations reached Averaging over polygons... | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% Maximum EM iterations reached [1] "Entering test_basis.R" Number of basis functions: 50 Number of resolutions: 1 Regular: 0 Type of manifold: surface of sphere Dimension of manifold: 2 First basis function: function (s) { stopifnot(ncol(s) == dimensions(manifold)) dist_sq <- distance(manifold, s, mu)^2 exp(-0.5 * dist_sq/(std^2)) } Number of basis functions: 50 Number of resolutions: 1 Regular: 0 Type of manifold: surface of sphere Dimension of manifold: 2 First basis function: function (s) { stopifnot(ncol(s) == dimensions(manifold)) dist_sq <- distance(manifold, s, mu)^2 exp(-0.5 * dist_sq/(std^2)) } NOTE: Zero process variability is implicitly enforced in regions where basis functions are pruned. Please use the option prune carefully: regions of data paucity are generally not reflective of regions of low process variability. Please set prune = 0 if unsure what to do. Averaging over polygons... First set of basis functions ---------------------------- Number of basis functions: 10 Number of resolutions: 1 Regular: 0 Type of manifold: surface of sphere Dimension of manifold: 2 First basis function: function (s) { stopifnot(ncol(s) == dimensions(manifold)) y <- distance(manifold, s, c) (1 - (y/R)^2)^2 * (y < R) } Second set of basis functions ----------------------------- Number of basis functions: 3 Number of resolutions: 1 Regular: 1 Type of manifold: real_line Dimension of manifold: 1 First basis function: function (s) { stopifnot(ncol(s) == dimensions(manifold)) y <- distance(manifold, s, c) (1 - (y/R)^2)^2 * (y < R) } Total number of basis functions: 30 Regular: FALSE First set of basis functions ---------------------------- Number of basis functions: 10 Number of resolutions: 1 Regular: 0 Type of manifold: surface of sphere Dimension of manifold: 2 First basis function: function (s) { stopifnot(ncol(s) == dimensions(manifold)) y <- distance(manifold, s, c) (1 - (y/R)^2)^2 * (y < R) } Second set of basis functions ----------------------------- Number of basis functions: 3 Number of resolutions: 1 Regular: 1 Type of manifold: real_line Dimension of manifold: 1 First basis function: function (s) { stopifnot(ncol(s) == dimensions(manifold)) y <- distance(manifold, s, c) (1 - (y/R)^2)^2 * (y < R) } Total number of basis functions: 30 Regular: FALSE [1] "Entering test_domains.R" Type of manifold: surface of sphere Radius of sphere: 6371 Dimension of manifold: 2 Distance function: function (x1, x2 = NULL) dist_sphere(x1, x2, R = R) Type of manifold: surface of sphere Radius of sphere: 6371 Dimension of manifold: 2 Distance function: function (x1, x2 = NULL) dist_sphere(x1, x2, R = R) Type of manifold: plane Dimension of manifold: 2 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: plane Dimension of manifold: 2 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: real_line Dimension of manifold: 1 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: real_line Dimension of manifold: 1 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: STplane Dimension of manifold: 3 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: STplane Dimension of manifold: 3 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: STplane Dimension of manifold: 3 Distance function: function (x1, x2) distR(x1, x2) Type of manifold: STplane Dimension of manifold: 3 Distance function: function (x1, x2) distR(x1, x2) NOTE: Zero process variability is implicitly enforced in regions where basis functions are pruned. Please use the option prune carefully: regions of data paucity are generally not reflective of regions of low process variability. Please set prune = 0 if unsure what to do. Averaging over polygons... | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80%Minimum tolerance reached Constructing BAUs... Generating basis functions... Modelling using 819 basis functions. Constructing SRE model... Fitting SRE model... | | | 0% | |=================================== | 50% | |======================================================================| 100% Maximum EM iterations reached [ FAIL 0 | WARN 10 | SKIP 0 | PASS 218 ] [ FAIL 0 | WARN 10 | SKIP 0 | PASS 218 ] > options(ops) > > proc.time() user system elapsed 67.64 6.10 73.75