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alexwesterbergalexwesterberg
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split ds.glmPredict smoke tests into -binomial, -poisson and -gaussian separated tests, for convenience. Also completed the binomial tests, which involved doing a glm model on a binary response variable unlike the other two families' tests.
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‎tests/testthat/test-smk-ds.glmPredict-binomial.R‎

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#-------------------------------------------------------------------------------
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# Copyright (c) 2019-2020 University of Newcastle upon Tyne. All rights reserved.
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#
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# This program and the accompanying materials
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# are made available under the terms of the GNU Public License v3.0.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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#-------------------------------------------------------------------------------
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#
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# Set up
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#
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context("ds.glmPredict::smk::binomial::setup")
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connect.studies.dataset.cnsim(list("LAB_TSC", "LAB_TRIG", "DIS_AMI", "DIS_DIAB", "GENDER"))
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test_that("setup", {
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ds_expect_variables(c("D"))
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})
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#
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# Tests
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#
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context("ds.glmPredict::smk::binomial")
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test_that("simple glmPredict, binomial, without newobj, se.fit=FALSE",{
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glmSLMA.res <- ds.glmSLMA('D$DIS_DIAB~D$LAB_TRIG', family="binomial", newobj="binomial.glmslma.obj")
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expect_length(glmSLMA.res, 9)
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expect_equal(glmSLMA.res$num.valid.studies, 3)
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expect_length(glmSLMA.res$validity.check, 1)
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expect_equal(glmSLMA.res$validity.check, "<binomial.glmslma.obj> appears valid in all sources")
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res <- ds.glmPredict("binomial.glmslma.obj", newdataname = NULL, output.type = "response", se.fit = FALSE, na.action = "na.pass")
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expect_length(res, 3)
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expect_equal(class(res), "list")
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expect_length(res$sim1, 1)
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expect_length(res$sim1$safe.list, 10)
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expect_equal(class(res$sim1$safe.list), "list")
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expect_equal(res$sim1$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim1$safe.list$newdfname))
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expect_equal(res$sim1$safe.list$output.type, "response")
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expect_true(is.null(res$sim1$safe.list$dispersion))
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expect_equal(res$sim1$safe.list$fit.Ntotal, 1801)
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expect_equal(res$sim1$safe.list$fit.Nvalid, 1801)
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expect_equal(res$sim1$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim1$safe.list$fit.mean, 0.01388118, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.sd, 0.1228967, tolerance = 1e-7)
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expect_length(res$sim1$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim1$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim1$safe.list$fit.quantiles[[1]], 0.004276445, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[2]], 0.005286237, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[3]], 0.007428756, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[4]], 0.011330492, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[5]], 0.016775371, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[6]], 0.024259416, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[7]], 0.029864696, tolerance = 1e-7)
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expect_length(res$sim2, 1)
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expect_length(res$sim2$safe.list, 10)
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expect_equal(class(res$sim2$safe.list), "list")
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expect_equal(res$sim2$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim2$safe.list$newdfname))
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expect_equal(res$sim2$safe.list$output.type, "response")
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expect_true(is.null(res$sim2$safe.list$dispersion))
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expect_equal(res$sim2$safe.list$fit.Ntotal, 2526)
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expect_equal(res$sim2$safe.list$fit.Nvalid, 2526)
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expect_equal(res$sim2$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim2$safe.list$fit.mean, 0.01306413, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.sd, 0.1058779, tolerance = 1e-7)
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expect_length(res$sim2$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim2$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim2$safe.list$fit.quantiles[[1]], 0.004114235, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[2]], 0.005070055, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[3]], 0.007363168, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[4]], 0.010906288, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[5]], 0.015909292, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[6]], 0.022784761, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[7]], 0.027798164, tolerance = 1e-7)
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expect_length(res$sim3, 1)
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expect_length(res$sim3$safe.list, 10)
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expect_equal(class(res$sim3$safe.list), "list")
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expect_equal(res$sim3$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim3$safe.list$newdfname))
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expect_equal(res$sim3$safe.list$output.type, "response")
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expect_true(is.null(res$sim3$safe.list$dispersion))
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expect_equal(res$sim3$safe.list$fit.Ntotal, 3473)
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expect_equal(res$sim3$safe.list$fit.Nvalid, 3473)
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expect_equal(res$sim3$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim3$safe.list$fit.mean, 0.01612439, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.sd, 0.08681195, tolerance = 1e-7)
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expect_length(res$sim3$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim3$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim3$safe.list$fit.quantiles[[1]], 0.007566798, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[2]], 0.008984660, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[3]], 0.011332598, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[4]], 0.014793185, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[5]], 0.019366204, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[6]], 0.024738972, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[7]], 0.029020811, tolerance = 1e-7)
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})
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test_that("simple glmPredict, binomial, with newobj, se.fit=FALSE", {
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glmSLMA.res <- ds.glmSLMA('D$DIS_DIAB~D$LAB_TRIG', family="binomial", newobj="binomial.glmslma.obj")
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expect_length(glmSLMA.res, 9)
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expect_equal(glmSLMA.res$num.valid.studies, 3)
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expect_length(glmSLMA.res$validity.check, 1)
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expect_equal(glmSLMA.res$validity.check, "<binomial.glmslma.obj> appears valid in all sources")
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res <- ds.glmPredict("binomial.glmslma.obj", output.type = "response", se.fit = FALSE, newobj="binomial.glm.predict.obj")
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expect_length(res, 3)
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expect_equal(class(res), "list")
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expect_length(res$sim1, 1)
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expect_length(res$sim1$safe.list, 10)
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expect_equal(class(res$sim1$safe.list), "list")
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expect_equal(res$sim1$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim1$safe.list$newdfname))
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expect_equal(res$sim1$safe.list$output.type, "response")
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expect_true(is.null(res$sim1$safe.list$dispersion))
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expect_equal(res$sim1$safe.list$fit.Ntotal, 1801)
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expect_equal(res$sim1$safe.list$fit.Nvalid, 1801)
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expect_equal(res$sim1$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim1$safe.list$fit.mean, 0.01388118, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.sd, 0.1228967, tolerance = 1e-7)
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expect_length(res$sim1$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim1$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim1$safe.list$fit.quantiles[[1]], 0.004276445, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[2]], 0.005286237, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[3]], 0.007428756, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[4]], 0.011330492, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[5]], 0.016775371, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[6]], 0.024259416, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[7]], 0.029864696, tolerance = 1e-7)
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expect_length(res$sim2, 1)
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expect_length(res$sim2$safe.list, 10)
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expect_equal(class(res$sim2$safe.list), "list")
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expect_equal(res$sim2$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim2$safe.list$newdfname))
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expect_equal(res$sim2$safe.list$output.type, "response")
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expect_true(is.null(res$sim2$safe.list$dispersion))
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expect_equal(res$sim2$safe.list$fit.Ntotal, 2526)
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expect_equal(res$sim2$safe.list$fit.Nvalid, 2526)
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expect_equal(res$sim2$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim2$safe.list$fit.mean, 0.01306413, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.sd, 0.1058779, tolerance = 1e-7)
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expect_length(res$sim2$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim2$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim2$safe.list$fit.quantiles[[1]], 0.004114235, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[2]], 0.005070055, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[3]], 0.007363168, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[4]], 0.010906288, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[5]], 0.015909292, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[6]], 0.022784761, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[7]], 0.027798164, tolerance = 1e-7)
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expect_length(res$sim3, 1)
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expect_length(res$sim3$safe.list, 10)
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expect_equal(class(res$sim3$safe.list), "list")
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expect_equal(res$sim3$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim3$safe.list$newdfname))
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expect_equal(res$sim3$safe.list$output.type, "response")
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expect_true(is.null(res$sim3$safe.list$dispersion))
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expect_equal(res$sim3$safe.list$fit.Ntotal, 3473)
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expect_equal(res$sim3$safe.list$fit.Nvalid, 3473)
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expect_equal(res$sim3$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim3$safe.list$fit.mean, 0.01612439, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.sd, 0.08681195, tolerance = 1e-7)
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expect_length(res$sim3$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim3$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim3$safe.list$fit.quantiles[[1]], 0.007566798, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[2]], 0.008984660, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[3]], 0.011332598, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[4]], 0.014793185, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[5]], 0.019366204, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[6]], 0.024738972, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[7]], 0.029020811, tolerance = 1e-7)
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})
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test_that("simple glmPredict, binomial, with newobj, se.fit=TRUE", {
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glmSLMA.res <- ds.glmSLMA('D$DIS_DIAB~D$LAB_TRIG', family="binomial", newobj="binomial.glmslma.obj")
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expect_length(glmSLMA.res, 9)
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expect_equal(glmSLMA.res$num.valid.studies, 3)
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expect_length(glmSLMA.res$validity.check, 1)
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expect_equal(glmSLMA.res$validity.check, "<binomial.glmslma.obj> appears valid in all sources")
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res <- ds.glmPredict("binomial.glmslma.obj", newdataname = NULL, output.type = "response", se.fit = TRUE, na.action = "na.pass", newobj="binomial.glm.predict.sefit.obj")
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expect_length(res, 3)
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expect_equal(class(res), "list")
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expect_length(res$sim1, 1)
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expect_length(res$sim1$safe.list, 17)
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expect_equal(class(res$sim1$safe.list), "list")
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expect_equal(res$sim1$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim1$safe.list$newdfname))
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expect_equal(res$sim1$safe.list$output.type, "response")
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expect_true(is.null(res$sim1$safe.list$dispersion))
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expect_equal(res$sim1$safe.list$fit.Ntotal, 1801)
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expect_equal(res$sim1$safe.list$fit.Nvalid, 1801)
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expect_equal(res$sim1$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim1$safe.list$fit.mean, 0.01388118, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.sd, 0.01510361, tolerance = 1e-7)
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expect_length(res$sim1$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim1$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim1$safe.list$fit.quantiles[[1]], 0.004276445, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[2]], 0.005286237, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[3]], 0.007428756, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[4]], 0.011330492, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[5]], 0.016775371, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[6]], 0.024259416, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$fit.quantiles[[7]], 0.029864696, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.Ntotal, 1801)
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expect_equal(res$sim1$safe.list$se.fit.Nvalid, 1801)
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expect_equal(res$sim1$safe.list$se.fit.Nmiss, 0)
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expect_equal(res$sim1$safe.list$se.fit.mean, 0.003569239, tolerance = 1e-8)
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expect_equal(res$sim1$safe.list$se.fit.sd, 0.00768495, tolerance = 1e-8)
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expect_length(res$sim1$safe.list$se.fit.quantiles, 7)
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expect_equal(class(res$sim1$safe.list$se.fit.quantiles), "numeric")
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[1]], 0.001747688, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[2]], 0.001918347, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[3]], 0.002191026, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[4]], 0.002585741, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[5]], 0.003353623, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[6]], 0.005213378, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$se.fit.quantiles[[7]], 0.007129711, tolerance = 1e-7)
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expect_equal(res$sim1$safe.list$residual.scale, 1, tolerance = 1e-7)
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expect_length(res$sim2, 1)
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expect_length(res$sim2$safe.list, 17)
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expect_equal(class(res$sim2$safe.list), "list")
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expect_equal(res$sim2$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim2$safe.list$newdfname))
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expect_equal(res$sim2$safe.list$output.type, "response")
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expect_true(is.null(res$sim2$safe.list$dispersion))
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expect_equal(res$sim2$safe.list$fit.Ntotal, 2526)
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expect_equal(res$sim2$safe.list$fit.Nvalid, 2526)
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expect_equal(res$sim2$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim2$safe.list$fit.mean, 0.01306413, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.sd, 0.01121012, tolerance = 1e-7)
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expect_length(res$sim2$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim2$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim2$safe.list$fit.quantiles[[1]], 0.004114235, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[2]], 0.005070055, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[3]], 0.007363168, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[4]], 0.010906288, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[5]], 0.015909292, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[6]], 0.022784761, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$fit.quantiles[[7]], 0.027798164, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.Ntotal, 2526)
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expect_equal(res$sim2$safe.list$se.fit.Nvalid, 2526)
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expect_equal(res$sim2$safe.list$se.fit.Nmiss, 0)
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expect_equal(res$sim2$safe.list$se.fit.mean, 0.002968172, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.sd, 0.005111322, tolerance = 1e-7)
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expect_length(res$sim2$safe.list$se.fit.quantiles, 7)
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expect_equal(class(res$sim2$safe.list$se.fit.quantiles), "numeric")
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[1]], 0.001572552, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[2]], 0.001704822, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[3]], 0.001917358, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[4]], 0.002159206, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[5]], 0.002756816, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[6]], 0.004427053, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$se.fit.quantiles[[7]], 0.006137771, tolerance = 1e-7)
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expect_equal(res$sim2$safe.list$residual.scale, 1, tolerance = 1e-7)
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expect_length(res$sim3, 1)
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expect_length(res$sim3$safe.list, 17)
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expect_equal(class(res$sim3$safe.list), "list")
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expect_equal(res$sim3$safe.list$glm.object, "binomial.glmslma.obj")
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expect_true(is.null(res$sim3$safe.list$newdfname))
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expect_equal(res$sim3$safe.list$output.type, "response")
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expect_true(is.null(res$sim3$safe.list$dispersion))
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expect_equal(res$sim3$safe.list$fit.Ntotal, 3473)
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expect_equal(res$sim3$safe.list$fit.Nvalid, 3473)
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expect_equal(res$sim3$safe.list$fit.Nmiss, 0)
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expect_equal(res$sim3$safe.list$fit.mean, 0.01612439, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.sd, 0.007536315, tolerance = 1e-7)
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expect_length(res$sim3$safe.list$fit.quantiles, 7)
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expect_equal(class(res$sim3$safe.list$fit.quantiles), "numeric")
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expect_equal(res$sim3$safe.list$fit.quantiles[[1]], 0.007566798, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[2]], 0.008984660, tolerance = 1e-7)
292+
expect_equal(res$sim3$safe.list$fit.quantiles[[3]], 0.011332598, tolerance = 1e-7)
293+
expect_equal(res$sim3$safe.list$fit.quantiles[[4]], 0.014793185, tolerance = 1e-7)
294+
expect_equal(res$sim3$safe.list$fit.quantiles[[5]], 0.019366204, tolerance = 1e-7)
295+
expect_equal(res$sim3$safe.list$fit.quantiles[[6]], 0.024738972, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$fit.quantiles[[7]], 0.029020811, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.Ntotal, 3473)
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expect_equal(res$sim3$safe.list$se.fit.Nvalid, 3473)
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expect_equal(res$sim3$safe.list$se.fit.Nmiss, 0)
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expect_equal(res$sim3$safe.list$se.fit.mean, 0.002848933, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.sd, 0.002740361, tolerance = 1e-7)
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expect_length(res$sim3$safe.list$se.fit.quantiles, 7)
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expect_equal(class(res$sim3$safe.list$se.fit.quantiles), "numeric")
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[1]], 0.002102462, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[2]], 0.002106133, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[3]], 0.002132488, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[4]], 0.002197552, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[5]], 0.002626926, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[6]], 0.004153282, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$se.fit.quantiles[[7]], 0.005834279, tolerance = 1e-7)
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expect_equal(res$sim3$safe.list$residual.scale, 1, tolerance = 1e-7)
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})
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#
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# Shutdown
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#
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context("ds.glmPredict::smk::binomial::shutdown")
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test_that("shutdown", {
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ds_expect_variables(c("D", "binomial.glmslma.obj", "binomial.glm.predict.obj", "binomial.glm.predict.sefit.obj", "predict_glm" ))
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})
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disconnect.studies.dataset.cnsim()
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#
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# Done
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#
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context("ds.glmPredict::smk::binomial::done")

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