cat(crayon::yellow("\ntest of prediction variance:"))

## two ranefs with estimated lambdas:
data("blackcap")
rngcheck <- ("sample.kind" %in% names (formals(RNGkind)))
if (rngcheck) suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding"  )) 
set.seed(123)
somegrp <- cbind(blackcap,grp=sample(2,14,replace=TRUE))
if (rngcheck) RNGkind("Mersenne-Twister", "Inversion", "Rejection"  )
#somegrp <- cbind(blackcap,grp=c(rep(1,7),rep(2,7))) ## to test cov mat with nonzero var of grp effect

{ # Checks that <processed>$phi.Fix was correctly set up (otherwise phi might still be fixed in fit but not considered as such in post-fit)
  chkphiFix <- corrHLfit(migStatus ~ 1 +  (1|grp) +Matern(1|longitude+latitude),data=somegrp,
                         fixed=list(nu=4,rho=0.4,phi=0.05))
  testthat::expect_true(attr(chkphiFix$phi.object$phi_outer,"type")=="fix")
  chkphiFix <- fitme(migStatus ~ 1 +  (1|grp) +Matern(1|longitude+latitude),data=somegrp,
                     fixed=list(nu=4,rho=0.4,phi=0.05))
  testthat::expect_true(attr(chkphiFix$phi.object$phi_outer,"type")=="fix")
  chkphiFix <- HLCor(migStatus ~ 1 +  (1|grp) +Matern(1|longitude+latitude),data=somegrp,
                     fixed=list(nu=4,rho=0.4,phi=0.05))
  testthat::expect_true(attr(chkphiFix$phi.object$phi_outer,"type")=="fix")
  chkphiFix <- HLfit(migStatus ~ 1 +  (1|grp) ,data=somegrp,
                     fixed=list(nu=4,rho=0.4,phi=0.05))
  testthat::expect_true(attr(chkphiFix$phi.object$phi_outer,"type")=="fix")
  ## I added a similar test in test-mv-extra
}

tworanefs <- corrHLfit(migStatus ~ 1 +  (1|grp) +Matern(1|longitude+latitude),data=somegrp,
                       ranFix=list(nu=4,rho=0.4,phi=0.05))
# expected <- c("Gibraltar"=0.04880579, "CapeVerde"=0.04884620, "SouthernFrance"=0.04197530, 
#               "LaPalma"=0.03337928, "Madeira"=0.04360190)   # before change of convergence crit in HLfit_body v3.13.17
expected <- c("Gibraltar"=0.04941506, "CapeVerde"=0.04891898, "SouthernFrance"=0.04479092, 
              "LaPalma"=0.03877590, "Madeira"=0.04361358)
p1 <- get_predVar(tworanefs)[1:5]
crit <- diff(range(p1 - expected))
testthat::test_that(paste0("get_predVar(tworanefs)[1:5]: criterion was ",signif(crit,4)," >1e-8"),
                    testthat::expect_true(crit<1e-8) 
)
p1p <- get_predVar(tworanefs,newdata=somegrp[1:5,])
testthat::expect_true(diff(range(p1p -p1))<1e-10)
ranefstwo <- corrHLfit(migStatus ~ 1  +Matern(1|longitude+latitude)+  (1|grp),data=somegrp,
                       ranFix=list(nu=4,rho=0.4,phi=0.05))
p2 <- get_predVar(ranefstwo,newdata=somegrp[1:5,])
testthat::expect_true(diff(range(p1-p2))<1e-10)

twolambda <- corrHLfit(migStatus ~ 1 +  Matern(1|longitude+latitude) +Matern(1|longitude+latitude),data=blackcap,
                       ranFix=list(corrPars=list("1"=list(nu=4,rho=0.4),"2"=list(nu=4,rho=0.4)),phi=0.05))
## but in that case the exact loglamInfo is exactly singular! 
## (this makes sense: ones cannot separately estimate the two lambda's)
## any attempt at computing a non-singular loglamInfo will amplify floating point error.
onelambda <- corrHLfit(migStatus ~ 1 + Matern(1|longitude+latitude),data=blackcap,
                       ranFix=list(nu=4,rho=0.4,phi=0.05))
if ("dgCMatrix" %in% how(twolambda, verbose=FALSE)$MME_method) { # if QRmethod="sparse" was requested
  testthat::expect_true(diff(range(get_predVar(twolambda)[1:5]-get_predVar(onelambda)[1:5]))<1e-5)
} else {
  crit <- diff(range(get_predVar(twolambda)[1:5]-get_predVar(onelambda)[1:5]))
  try(testthat::test_that(paste0("singular 'twolambda' model: criterion was ",signif(crit,4)," >1e-7"),
                     testthat::expect_true(crit<1e-7) ## affected by .Rcpp_backsolve()
  ))
}


# as_tcrossfac_list with newdata
blackfit <- fitme(migStatus ~ 1 +  (1|grp) +Matern(1|longitude+latitude),data=somegrp)
newdat <- with(somegrp,data.frame(latitude=latitude+5,longitude=longitude+5,grp=grp^2))
tcrossfac_list <- get_predVar(blackfit, newdata=newdat, variances=list(as_tcrossfac_list=TRUE))
var1 <- Reduce("+",lapply(tcrossfac_list,tcrossprod))
var2 <- get_predVar(blackfit, newdata=newdat, variances=list(cov=TRUE))
testthat::expect_true(diff(range(var1-var2))<1e-07) # difference is ~imprecision on eigenvalue of one Evar term not exactly symmPosDef


# examples from Booth & Hobert 1998 JASA

data("clinics")
fitobject <- HLfit(cbind(npos,nneg)~treatment+(1|clinic),family=binomial(),data=clinics,HLmethod="ML")
res <- sqrt(get_predVar(fitobject))
# res: cf row 2, table 6; some discrepancies (surely stemming from point estimates).
testthat::expect_equal(res[6],c("6"=0.7316108),tolerance=2e-4)

res <- get_respVar(fitobject,variances=list(cov=TRUE))
testthat::expect( !is.null(dim(res)),"is.null(dim(<get_respVar(fitobject,variances=list(cov=TRUE))>))") ## check that a matrix is returned
testthat::expect_equal(res[6,6],0.1061096,tolerance=2e-4) ## check that dmudeta scaling is taken in account.

if(requireNamespace("rsae", quietly = TRUE)) {
  data("landsat", package = "rsae")
  fitobject <- HLfit(HACorn ~ PixelsCorn + PixelsSoybeans + (1|CountyName),data=landsat[-33,],HLmethod="ML")
  newXandZ <- unique(data.frame(PixelsCorn=landsat$MeanPixelsCorn, PixelsSoybeans=landsat$MeanPixelsSoybeans, # replacement by Mean...
                                CountyName=landsat$CountyName))
  (res <- get_predVar(fitobject,newdata=newXandZ))
  testthat::expect_equal(res[12],c("32"=27.80684),tolerance=2e-4)
  (res <- get_predVar(fitobject,newdata=newXandZ,variances=list(disp=FALSE))) ## sum of cols nu and beta in Table 7
  testthat::expect_equal(res[12],c("32"=26.56215),tolerance=2e-4)
  
  ## comparison dense vs. spprec with use_spprec_qr on an REML non-spatial fit 
  # (bc spprec does not handle Matern and use_spprec_qr affects only REML)
  fitdense <- HLfit(HACorn ~ PixelsCorn + PixelsSoybeans + (1|CountyName),data=landsat[-33,],HLmethod="REML", 
                    control.HLfit=list(sparse_precision=FALSE))
  oldop2 <- spaMM.options(use_spprec_QR=TRUE)
  # trace(spaMM:::.calc_spprec_hatval_ZX_by_QR,tracer=quote(print(".calc_spprec_hatval_ZX_by_QR")))
  # trace(spaMM:::.calc_Md2hdvb2_info_spprec_by_QR,tracer=quote(print(".calc_Md2hdvb2_info_spprec_by_QR")))
  fitsparse <- HLfit(HACorn ~ PixelsCorn + PixelsSoybeans + (1|CountyName),data=landsat[-33,],HLmethod="REML")
  spaMM.options(oldop2)
  testthat::expect_true(diff(range(get_predVar(fitdense,newdata=newXandZ)-get_predVar(fitsparse,newdata=newXandZ)))<1e-8)
  testthat::expect_true(diff(range(get_predVar(fitdense,newdata=newXandZ,variances=list(disp=FALSE)) - 
                                   get_predVar(fitsparse,newdata=newXandZ,variances=list(disp=FALSE))))<1e-8)
} else {
  cat( "package 'rsae' not available, cannot run prediction variance test.\n" )
}

# simple LM
set.seed(123)
if (TRUE) {
  n <- 100
  k <- rep(1:(n/10), each=10)
} else {
  n <- 100000 # used to profile .leveragesWrap()
  k <- rep(1:200, each=500) # design mat 100000*200
}
data.test <- data.frame(y=rnorm(n, mean=k), x=factor(k))
hlfit <- HLfit(y ~ x, data=data.test)
p <- predict(hlfit, newdata=data.frame(x=unique(data.test$x)), variances=list(respVar=TRUE))
testthat::expect_equal(length(attr(p, "residVar")),10L)
testthat::expect_equal(attr(p, "residVar")[1],c(`1`=0.8347247),tolerance=1e-6)
testthat::expect_equal(attr(p, "predVar")[1],c(`1`=0.08347247),tolerance=1e-6)
testthat::expect_equal(attr(p, "respVar")[1],c(`1`=0.918197),tolerance=1e-6)
## also tests that a single level of the factor in the newdata is not a problem
pp <- predict(hlfit, newdata=data.test[1,], variances=list(respVar=TRUE))
testthat::expect_equal(pp[1],c(`1`=1.074626),tolerance=1e-6)

# can capture errors missed by other tests: (cPredVar on fixed-effect model; gamma())
gammafit <- fitme(Sepal.Length ~ Petal.Length, data = iris, method = "REML", family=Gamma(log))
get_cPredVar(gammafit, nsim = 3)
v1 <- residVar(gammafit, which="var")
v2 <- attr(predict(gammafit,variances=list(residVar=TRUE)),"residVar")
testthat::expect_true(diff(range(v1-v2))<1e-7)
# distinct from            residVar(gammafit, which="phi")

## multiple tests with two ranefs 
set.seed(123)
data("Loaloa")
ll <- cbind(Loaloa,idx=sample(2,size=nrow(Loaloa),replace=TRUE))
hl <- HLCor(cbind(npos,ntot-npos)~1+Matern(1|longitude+latitude)+(1|idx),data=ll,
            family=binomial(),ranPars=list(nu=0.5,rho=1/0.7),HLmethod="PQL/L")
# verif single point input
p1 <- predict(hl,variances=list(respVar=TRUE))
p2 <- predict(hl,newdata=ll[1,],variances=list(respVar=TRUE))
testthat::expect_equal(attr(p1,"respVar")[1],attr(p2,"respVar")[1])
testthat::expect_equal(p1[1],p2[1])
# verif 'subsetting' mechanism including Evar (hence new levels of ranef) 
lll <- ll
lll$idx <- lll$idx+1
lll$latitude <- lll$latitude+1
p4 <- predict(hl,newdata=lll[1:101,],variances=list(respVar=TRUE))
p5 <- predict(hl,newdata=lll[101:102,],variances=list(respVar=TRUE))
testthat::expect_equal(attr(p4,"respVar")[101],attr(p5,"respVar")[1])
testthat::expect_equivalent(p4[101],p5[1]) ## _equivalent does not check names and other attributes
# verif binding with blocking
pp <- predict(hl, newdata = lll[1:6,], binding = "pred", blocksize=3)
#testthat::test_that("Fails if predict(hl, binding = < chr >, blocksize=.) does not return a data frame", 
                     testthat::expect_true(is.data.frame(pp))
#                     ) 



## dontrun example from help(predict):
## prediction with distinct given phi's in different locations:
data("blackcap")
set.seed(123)
varphi <- cbind(blackcap,logphi=runif(14))
vphifit <- corrHLfit(migStatus ~ 1 + Matern(1|longitude+latitude), 
                     resid.model = list(formula=~0+offset(logphi)),
                     data=varphi,  ranFix=list(nu=4,rho=0.4))
# eg  to catch catch problems with wrong application of a-la-Bates formula:
testthat::expect_equal(logLik(vphifit),c(p_bv=-20.04856),tolerance=1e-5) 
p1 <- suppressWarnings(get_respVar(vphifit,newdata=data.frame(latitude=1,longitude=1,logphi=1)))
testthat::expect_equal(p1,c(`1`=2.844421),tolerance=1e-5)
# for predVar computation, phi is not needed 
#     (and could have been specified through ranFix):  
p1 <- suppressWarnings(get_predVar(vphifit,newdata=data.frame(latitude=1,longitude=1)))
testthat::expect_equal(p1,c(`1`=0.1261386),tolerance=1e-5) ## exact value sensitive to spaMM_tol$Xtol_abs
# verif get_predCov_var_fix()
p1 <- get_predVar(vphifit,variances=list(cov=TRUE))[14,]
fix_X_ZAC.object <- preprocess_fix_corr(vphifit,fixdata=blackcap)
p2 <- get_predCov_var_fix(vphifit,newdata=blackcap[14,],fix_X_ZAC.object=fix_X_ZAC.object)
testthat::expect_true(max(abs(range(p1-p2)))<1e-12)

# Another check of get_predCov_var_fix, but with rho mapping:
checkmap <- fitme(migStatus ~ 1 + Matern(1|longitude+latitude+means),
                  data=blackcap, fixed=list(nu=0.5,phi=0.1),
                  init=list(rho=c(1,1)), control.dist=list(rho.mapping=c(1,1,2)))
p1 <- get_predVar(checkmap,variances=list(cov=TRUE))[14,]
fix_X_ZAC.object <- preprocess_fix_corr(checkmap,fixdata=blackcap)
p2 <- get_predCov_var_fix(checkmap,newdata=blackcap[14,],fix_X_ZAC.object=fix_X_ZAC.object)
testthat::expect_true(max(abs(range(p1-p2)))<1e-12)

# # verif calc_logdisp_cov runs after outer optimisation => now tested in many other places
# fitfit <- fitme(migStatus ~ 1 + Matern(1|longitude+latitude),
#                      data=blackcap,  fixed=list(nu=4,rho=0.4))
# get_respVar(fitfit,newdata=data.frame(latitude=1,longitude=1))

# check corret handling of offset in predictions
check_off <- cbind(blackcap,off=runif(14))
voff <- corrHLfit(migStatus ~ 1 + offset(off) + Matern(1|longitude+latitude), 
                  data=check_off,  ranFix=list(nu=4,rho=0.4))
p1 <- predict(voff)
p2 <- predict(voff, newdata=check_off)
testthat::expect_true(max(abs(range(p1-p2)))<1e-12)
#
voff <- corrHLfit(migStatus ~ 0 + offset(off) + Matern(1|longitude+latitude), 
                  data=check_off,  ranFix=list(nu=4,rho=0.4))
p1 <- predict(voff)
p2 <- predict(voff, newdata=check_off)
testthat::expect_true(max(abs(range(p1-p2)))<1e-12)
#
cat("(Check formula w/o fixed effect, Note expected:)")
voff <- corrHLfit(migStatus ~ offset(off) + Matern(1|longitude+latitude), 
                  data=check_off,  ranFix=list(nu=4,rho=0.4))
p3 <- predict(voff)
p4 <- predict(voff, newdata=check_off)
testthat::expect_true(max(abs(range(p3-p4)))<1e-12)
testthat::expect_true(max(abs(range(p1-p4)))<1e-12)

# Check of different syntaxes to get naive term 
# Note that naive depend on the L matrix root and thus not comparable across equivalent fits (e.g. sparse/dense prec)
fitobject <- corrHLfit(migStatus ~ 1 + Matern(1|longitude+latitude),data=blackcap,
                       ranFix=list(nu=1,rho=0.1, phi=0.1)) 
(naive1 <- get_predVar(fitobject,which="naive"))
(naive2 <- get_predVar(fitobject,newdata=fitobject$data, which="naive"))
fix_X_ZAC.object <- preprocess_fix_corr(fitobject,fixdata=fitobject$data)
(naive3 <- diag(get_predCov_var_fix(fitobject,newdata=blackcap,fix_X_ZAC.object=fix_X_ZAC.object,variances=list(predVar=TRUE,naive=TRUE,cov=TRUE))))
(naive4 <- (get_predCov_var_fix(fitobject,newdata=blackcap,fix_X_ZAC.object=fix_X_ZAC.object,variances=list(predVar=TRUE,naive=TRUE,cov=FALSE))))
testthat::expect_true(max(abs(range(naive2-naive1)))<1e-12)
testthat::expect_true(max(abs(range(naive3-naive1)))<1e-12)
testthat::expect_true(max(abs(range(naive4-naive1)))<1e-12)

# as_tcrossfac_list
fit1 <- HLfit(x1~1+(1|x2),data=data.frame(x1=c(1,10,1),x2=c(1,1,2)), family=poisson)
tcrossfac_list <- get_predVar(fit1, variances=list(as_tcrossfac_list=TRUE))
var1 <- tcrossprod(tcrossfac_list[[1L]])+ tcrossprod(tcrossfac_list[[2L]])
var2 <- get_predVar(fit1, variances=list(cov=TRUE))
testthat::expect_true(diff(range(var1-var2))<1e-12)