require(lsttheory)
########### dataset: d_lst_es ###########
dsub <- d_lst_es[,1:9]
m1 <- lsttheory_es(model = 6, ntimepoints = 3, nperiods = 3, data = dsub)
actual_coefs <- c(m1@varcomp$rel[[1]],
m1@varcomp$spe[[2]],
m1@varcomp$con[[4]],
m1@varcomp$timepoint[[7]])
expected_coefs <- c(0.8328468, 0.2842702, 0.5781508, 3.0000000)
expect_equal(actual_coefs, expected_coefs, tolerance=1e-5)
############ simple models using lsttheory_es with d_lst_es ###################
m1 <- lst_models_es(traitmodel="singletrait", ntimepoints=9, data=d_lst_es,
nperiods=3, ar=FALSE, equiv="invar")
actual_coefs <- c(m1@varcomp$rel[[1]],
m1@varcomp$spe[[2]],
m1@varcomp$con[[4]],
m1@varcomp$timepoint[[7]])
expected_coefs <- c(0.8772938, 0.6057496, 0.2715442, 3.0000000)
expect_equal(actual_coefs, expected_coefs, tolerance=1e-5)
m2 <- lst_models_es(traitmodel="day-specific", ntimepoints=9, data=d_lst_es,
nperiods=3, ar=FALSE)
actual_coefs <- c(m2@varcomp$rel[[1]],
m2@varcomp$spe[[2]],
m2@varcomp$con[[4]],
m2@varcomp$timepoint[[7]])
expected_coefs <- c(0.8809884, 0.1932521, 0.6877363, 3.0000000)
expect_equal(actual_coefs, expected_coefs, tolerance=1e-5)
expect_warning(
m3 <- lst_models_es(traitmodel="indicator-specific", ntimepoints=9, data=d_lst_es,
nperiods=3, ar=FALSE)
)
actual_coefs <- c(m3@varcomp$rel[[1]],
m3@varcomp$spe[[2]],
m3@varcomp$con[[4]],
m3@varcomp$timepoint[[7]])
expected_coefs <- c(0.8773542, 0.6060529, 0.2724302, 3.000000)
expect_equal(actual_coefs, expected_coefs, tolerance=1e-5)
expect_warning(
m4 <- lst_models_es(traitmodel="day-and-indicator-specific", ntimepoints=9, data=d_lst_es,
nperiods=3, ar=FALSE)
)
actual_coefs <- c(m4@varcomp$rel[[1]],
m4@varcomp$spe[[2]],
m4@varcomp$con[[4]],
m4@varcomp$timepoint[[7]])
expected_coefs <- c(0.8810094, 0.1940352, 0.6884224, 3.0000000)
expect_equal(actual_coefs, expected_coefs, tolerance=1e-5)
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