tests/testthat/test-rsquared.R

In ejikeugba/gofcat: Goodness-of-Fit Measures for Categorical Response Models

require(nnet)
require(ordinal)
require(MASS)
require(mlogit)
require(dfidx)
require(VGAM)
require(serp)

# test data
vaso.new <- within(gofcat::vaso, {
  vaso[vaso==2] <- 0
  y <- vaso
})
retinopathy.new <- within(gofcat::retinopathy, {
  RET <- as.ordered(RET)
  SM <- as.factor(SM)
})
Fish <- dfidx::dfidx(gofcat::Fishing, varying = 2:9, shape = "wide", choice = "mode")


# test models
mdat <- data.frame(
  dose = c(0,2,4,6,8,10),
  mortality = as.integer(c(0,0,9,14,16,20)),
  survival = c(20,20,11,6,4,0)
)
fit <- glm( cbind(mortality, survival) ~ dose,
            data = mdat,
            family = "binomial")

gm <- glm(y ~ vol + rate, family=binomial(link = "logit"), data = vaso.new)
gx <- glm(y ~ vol + rate, family=binomial(link = "cauchit"), data = vaso.new)
fm <- formula(RET ~ SM + DIAB + GH + BP)

sm <- serp(ordered(RET) ~ SM + DIAB + GH + BP, link="logit", slope = "parallel",
           data = retinopathy.new)
capture <- capture.output(mm <- multinom(fm, data = retinopathy.new))
cm1 <- clm(fm, link="logit", data = retinopathy.new)
pm  <- polr(fm, method="logistic", data = retinopathy.new)
ml  <- mlogit(mode ~ price + catch, data = Fish)
xg1 <- vglm(fm, family = sratio(link = "logitlink"), data = retinopathy.new)
xg2 <- vglm(fm, family = VGAM::cumulative(parallel = TRUE, link = "clogloglink"),
            data = retinopathy.new)

js <- lm(y ~ vol + rate, data = vaso.new)
bs <- glm(y ~ vol + rate, family=binomial, data = vaso.new)

idx <- c("ugba", "mcfadden", "coxsnell", "nagelkerke", "aldrich", "veall",
         "mckelvey", "tjur", "efron")
lx <- list()


context("To check if Rsquared works properly on supported class of models")
test_that("Rsquared function works properly",
          {
            for(i in seq_along(idx))
              lx[[i]] <- Rsquared(gm, idx[i])
            expect_length(lx, 9L)
            expect_message(Rsquared("rtt"))
            expect_error(Rsquared(list(gm,gm)))

            expect_vector(Rsquared(sm, measure = "ugba")$sqrt.R2)
            expect_vector(nlev(model=sm, modeltype="serp"))
            expect_error(Rsquared(sm, measure = "tjur"))

            expect_vector(Rsquared(mm, measure = "coxsnell")$R2)
            expect_vector(nlev(model=mm, modeltype="multinom"))
            expect_error(Rsquared(mm, measure = "efron"))

            expect_vector(Rsquared(cm1, measure = "ugba")$log.R2)
            expect_error(Rsquared(cm1, measure = "efron"))

            expect_vector(Rsquared(pm, measure = "ugba")$sqrt.R2)
            expect_error(Rsquared(pm, measure = "tjur"))

            expect_error(Rsquared(ml, measure = "efron"))
            expect_vector(nlev(model=ml, modeltype="mlogit"))
            expect_vector(hosmerlem(ml)[[1]])

            expect_error(Rsquared(xg1, measure = "mcfadden"))
            expect_equal(modtype(model=xg2, measure="ugba", call.fn="Rsquared"), "vglm")
            expect_vector(modelInfo(model=ml, modeltype="mlogit", measure="mcfadden")$k)
            expect_error(Rsquared(gx, measure = "mckelvey"))

            expect_equal(class(hosmerlem(fit)), "hosmerlem")
            expect_equal(class(Rsquared(fit)), "Rsquared")

            expect_error(print.Rsquared(js))
            lx <- list()
            expect_output(
              for(i in seq_along(idx))
                lx[[i]] <- print.Rsquared(Rsquared(bs, measure=idx[i]))
            )
          })
rm(retinopathy.new, vaso.new, Fish, gm, fm, sm, mm, cm1, pm, ml, idx, lx,
   xg1, xg2, js, bs, pneumo, mdat, fit)