context("sar_shape")
test_that("observed_shape info is correct", {
skip_on_cran()
data(galap)
fit <- sar_epm2(galap)
fit2 <- sar_power(galap)
fit3 <- sar_average(data = galap)
s3 <- summary(fit3)
x <- fit$observed_shape
x3 <- s3$Model_table$Shape[17]#epm2 again
#in R 3.6.3 it returns the s3..[17] as a factor but later
#versions of R return it as a character vector. The as.vector()
#is thus here to pass Travis test for past versions.
expect_match(as.vector(s3$Model_table$Model[17]), "epm2")
expect_match(x, "sigmoid")
expect_match(fit2$observed_shape, "convex up")
expect_match(x3, "sigmoid")
#convex down test
test <- data.frame("A" = c( 1, 2, 3, 5, 6, 7, 8, 12, 15, 20,
23, 26, 27, 30, 34, 37, 40, 50),
"R" = c(30, 25, 22, 19, 18, 17, 17, 15, 14, 13,
12, 12, 12, 12, 11, 11, 11, 10))
fit4 <- sar_power(test)
expect_match(fit4$observed_shape, "convex down")
fit5 <- sar_p1(test)
expect_match(fit4$observed_shape, "convex down")
#sigmoid test
test$R <- c(1, 1, 1, 2, 1, 2, 1, 4, 7, 9, 12, 14, 15, 16, 16, 17, 16,
16)
fit6 <- sar_weibull4(test)
fit7 <- sar_p1(test)
fit8 <- sar_loga(test)
fit9 <- sar_linear(test)
expect_match(fit6$observed_shape, "sigmoid")
expect_match(fit7$observed_shape, "sigmoid")
expect_match(fit8$observed_shape, "convex up")
expect_match(fit9$observed_shape, "linear")
})
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