context("oslda")
test_that("oslda: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(oslda(formula = Species ~ V1, data = iris, wf = "gaussian", bw = 10))
# wrong class
expect_error(oslda(formula = iris, data = iris, wf = "gaussian", bw = 10))
#expect_error(oslda(iris, data = iris, wf = "gaussian", bw = 10))
# target variable also in x
fit <- oslda(grouping = iris$Species, x = iris, wf = "gaussian", bw = 10) ## todo!!!
expect_warning(predict(fit))
expect_warning(oslda(Species ~ Species + Petal.Width, data = iris, wf = "gaussian", bw = 10)) ## warning, Species on RHS removed
# missing x
expect_error(oslda(grouping = iris$Species, wf = "gaussian", bw = 10))
## wrong method argument
# missing quotes
expect_error(oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, method = ML))
# method as vector
expect_error(oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, method = c("ML","unbiased")))
})
test_that("oslda throws a warning if grouping variable is numeric", {
data(iris)
# formula, data
expect_that(oslda(formula = Sepal.Length ~ ., data = iris, wf = "gaussian", bw = 10), gives_warning("'grouping' was coerced to a factor"))
# grouping, x
expect_that(oslda(grouping = iris[,1], x = iris[,-1], wf = "gaussian", bw = 10), gives_warning("'grouping' was coerced to a factor"))
})
test_that("oslda works if only one predictor variable is given", {
data(iris)
fit <- oslda(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 5)
predict(fit)
})
test_that("oslda: training data from only one class", {
data(iris)
expect_that(oslda(Species ~ ., data = iris, bw = 2, subset = 1:50), throws_error("training data from only one group given"))
expect_that(oslda(Species ~ ., data = iris, bw = 2, subset = 1), throws_error("training data from only one group given"))
expect_that(oslda(grouping = iris$Species, x = iris[,-5], bw = 2, subset = 1:50), throws_error("training data from only one group given"))
expect_that(oslda(grouping = iris$Species, x = iris[,-5], bw = 2, subset = 1), throws_error("training data from only one group given"))
})
test_that("oslda: subsetting works", {
data(iris)
# formula, data
expect_that(fit1 <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = 1:80), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(Species ~ ., data = iris[1:80,], wf = "gaussian", bw = 2), gives_warning("group virginica is empty"))
expect_equal(fit1[-13],fit2[-13])
expect_equal(fit1$N, 80)
# x, grouping
expect_that(fit1 <- oslda(grouping = iris$Species, x = iris[,-5], wf = "gaussian", bw = 2, subset = 1:80), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(grouping = iris$Species[1:80], x = iris[1:80,-5], wf = "gaussian", bw = 2), gives_warning("group virginica is empty"))
expect_equal(fit1[-13],fit2[-13])
expect_equal(fit1$N, 80)
# wrong specification of subset argument
expect_error(oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = iris[1:10,]))
## todo
# expect_error(fit <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = FALSE)) #???
# expect_error(fit <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 0)) #???
##
expect_error(oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = -10:50))
})
test_that("oslda: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_that(oslda(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- oslda(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(13, 16)], fit2[-13])
## x, grouping
# na.fail
expect_that(oslda(grouping = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- oslda(grouping = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(grouping = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-13], fit2[-13])
### NA in grouping
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_that(oslda(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- oslda(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(13, 16)], fit2[-13])
## x, grouping
# na.fail
expect_that(oslda(grouping = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- oslda(grouping = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(grouping = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-13], fit2[-13])
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_that(oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(13, 16)], fit2[-13])
## x, grouping
# na.fail
expect_that(oslda(grouping = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = subset, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- oslda(grouping = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = subset, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- oslda(grouping = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-13], fit2[-13])
})
test_that("oslda: try all weight functions", {
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = gaussian(5))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "gaussian", bw = 5)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = gaussian(5))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, k = 30)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = gaussian(bw = 5, k = 30))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "gaussian", bw = 5, k = 30)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = gaussian(5, 30))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "epanechnikov", bw = 5, k = 30)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = epanechnikov(bw = 5, k = 30))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "epanechnikov", bw = 5, k = 30)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = epanechnikov(5, 30))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 30)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 30))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "rectangular", bw = 5, k = 30)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = rectangular(5, 30))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "triangular", bw = 5, k = 30)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = triangular(5, k = 30))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "triangular", bw = 5, k = 30)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = triangular(5, 30))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "biweight", bw = 5)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = biweight(5))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "biweight", bw = 5)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = biweight(5))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "optcosine", bw = 5, k = 30)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = optcosine(5, k = 30))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "optcosine", bw = 5, k = 30)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = optcosine(5, 30))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "cosine", bw = 5, k = 30)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = cosine(5, k = 30))
fit3 <- oslda(x = iris[,-5], grouping = iris$Species, wf = "cosine", bw = 5, k = 30)
fit4 <- oslda(x = iris[,-5], grouping = iris$Species, wf = cosine(5, 30))
expect_equal(fit1[-c(6, 13)], fit2[-c(6, 13)])
expect_equal(fit3[-c(6, 13)], fit4[-c(6, 13)])
expect_equal(fit2[-c(2,13:15)], fit4[-c(2,13:14)])
set.seed(120)
pred1 <- predict(fit1)
set.seed(120)
pred2 <- predict(fit2)
set.seed(120)
pred3 <- predict(fit3)
set.seed(120)
pred4 <- predict(fit4)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
})
test_that("oslda: local solution with rectangular window function and large bw and global solution coincide", {
library(MASS)
# unbiased
fit1 <- wlda(formula = Species ~ ., data = iris, method = "unbiased")
pred1 <- predict(fit1)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = rectangular(20), method = "unbiased")
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit3 <- lda(Species ~ ., data = iris)
pred3 <- predict(fit3, newdata = iris)
names(pred3$class) <- names(pred2$class)
expect_equal(pred2$class, pred3$class)
expect_equal(pred2$posterior, pred3$posterior)
# ML
fit1 <- wlda(formula = Species ~ ., data = iris, method = "ML")
pred1 <- predict(fit1)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = rectangular(20), method = "ML")
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit3 <- lda(Species ~ ., data = iris, method = "mle")
pred3 <- predict(fit3, newdata = iris)
names(pred3$class) <- names(pred2$class)
expect_equal(pred2$class, pred3$class)
expect_equal(pred2$posterior, pred3$posterior)
})
test_that("oslda: arguments related to weighting misspecified", {
# bw, k not required
expect_that(fit1 <- oslda(Species ~ ., data = iris, wf = gaussian(0.5), k = 30, bw = 0.5), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
fit2 <- oslda(Species ~ ., data = iris, wf = gaussian(0.5))
expect_equal(fit1[-13], fit2[-13])
expect_that(fit1 <- oslda(Species ~ ., data = iris, wf = gaussian(0.5), bw = 0.5), gives_warning("argument 'bw' is ignored"))
fit2 <- oslda(Species ~ ., data = iris, wf = gaussian(0.5))
expect_equal(fit1[-13], fit2[-13])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$adaptive, FALSE)
expect_that(fit1 <- oslda(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, k = 30), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
expect_that(fit2 <- oslda(Species ~ ., data = iris, wf = function(x) exp(-x), k = 30), gives_warning("argument 'k' is ignored"))
expect_equal(fit1[-13], fit2[-13])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
expect_that(fit1 <- oslda(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5), gives_warning("argument 'bw' is ignored"))
fit2 <- oslda(Species ~ ., data = iris, wf = function(x) exp(-x))
expect_equal(fit1[-13], fit2[-13])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
# missing quotes
fit <- oslda(formula = Species ~ ., data = iris, wf = gaussian) ## error because length(weights) and nrow(x) are different
expect_error(predict(fit))
# bw, k missing
expect_that(oslda(formula = Species ~ ., data = iris, wf = gaussian()), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(oslda(formula = Species ~ ., data = iris, wf = gaussian(), k = 10), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(oslda(Species ~ ., data = iris), throws_error("either 'bw' or 'k' have to be specified"))
# bw < 0
expect_that(oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = -5), throws_error("'bw' must be positive"))
expect_that(oslda(formula = Species ~ ., data = iris, wf = "cosine", k = 10, bw = -50), throws_error("'bw' must be positive"))
# bw vector
expect_that(oslda(formula = Species ~., data = iris, wf = "gaussian", bw = rep(1, nrow(iris))), gives_warning("only first element of 'bw' used"))
# k < 0
expect_that(oslda(formula = Species ~ ., data = iris, wf = "gaussian", k =-7, bw = 50), throws_error("'k' must be positive"))
# k too small
#fit <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", k = 5, bw = 0.005)
#expect_equal(length(is.na(predict(fit)$class)), 150)
# k too large
expect_that(oslda(formula = Species ~ ., data = iris, k = 250, wf = "gaussian", bw = 50), throws_error("'k' is larger than 'n'"))
# k vector
expect_that(oslda(formula = Species ~., data = iris, wf = "gaussian", k = rep(50, nrow(iris))), gives_warning("only first element of 'k' used"))
})
test_that("oslda: weighting schemes work", {
## wf with finite support
# fixed bw
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = rectangular(bw = 5))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "rectangular", k = 50)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = rectangular(k = 50))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$k, 50)
expect_equal(fit1$bw, NULL)
expect_true(fit1$nn.only)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 50))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, 50)
expect_true(fit1$nn.only)
expect_true(!fit1$adaptive)
# nn.only not needed
expect_that(oslda(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, nn.only = TRUE), gives_warning("argument 'nn.only' is ignored"))
# nn.only has to be TRUE if bw and k are both given
expect_that(oslda(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, nn.only = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
## wf with infinite support
# fixed bw
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 0.5)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", k = 50)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = gaussian(k = 50))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(fit1$adaptive)
# adaptive bw, all obs
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, nn.only = FALSE)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = gaussian(k = 50, nn.only = FALSE))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, FALSE)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50)
fit2 <- oslda(formula = Species ~ ., data = iris, wf = gaussian(bw = 1, k = 50))
expect_equal(fit1[-c(6,13)], fit2[-c(6,13)])
expect_equal(fit1$bw, 1)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(!fit1$adaptive)
# nn.only has to be TRUE if bw and k are both given
expect_that(oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, nn.only = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
})
#=================================================================================================================
context("predict.oslda")
test_that("predict.oslda works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
pred <- predict(fit)
expect_equal(names(pred$class), rownames(iris)[ran])
expect_equal(rownames(pred$posterior), rownames(iris)[ran])
## formula, data, newdata
fit <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
pred <- predict(fit, newdata = iris[-ran,])
expect_equal(names(pred$class), rownames(iris)[-ran])
expect_equal(rownames(pred$posterior), rownames(iris)[-ran])
## grouping, x
fit <- oslda(x = iris[,-5], grouping = iris$Species, wf = "gaussian", bw = 2, subset = ran)
pred <- predict(fit)
expect_equal(names(pred$class), rownames(iris)[ran])
expect_equal(rownames(pred$posterior), rownames(iris)[ran])
## grouping, x, newdata
fit <- oslda(x = iris[,-5], grouping = iris$Species, wf = "gaussian", bw = 2, subset = ran)
pred <- predict(fit, newdata = iris[-ran,-5])
expect_equal(names(pred$class), rownames(iris)[-ran])
expect_equal(rownames(pred$posterior), rownames(iris)[-ran])
})
test_that("predict.oslda: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
# y, x
fit <- oslda(x = iris[,-5], grouping = iris$Species, wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[,-5])
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- oslda(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
# y, x
fit <- oslda(x = iris[,-5], grouping = iris$Species, wf = "gaussian", bw = 2, subset = ran)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,-5])
expect_equal(pred1, pred2)
})
test_that("predict.oslda works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
expect_that(fit <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 1:100), gives_warning("group virginica is empty"))
expect_equal(length(fit$counts), 2)
a <- rep(50, 2)
names(a) <- names(fit$counts)
expect_equal(fit$counts, a)
expect_equal(fit$N, 100)
pred <- predict(fit, newdata = iris[-ran,])
expect_equal(nlevels(pred$class), 3)
expect_equal(ncol(pred$posterior), 2)
})
test_that("predict.oslda works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- oslda(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran)
expect_equal(ncol(fit$x), 1)
predict(fit, newdata = iris[-ran,])
})
test_that("predict.oslda works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
pred <- predict(fit, newdata = iris[5,])
expect_equal(length(pred$class), 1)
expect_equal(dim(pred$posterior), c(1, 3))
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) = "5"
expect_equal(pred$class, a)
pred <- predict(fit, newdata = iris[58,])
expect_equal(length(pred$class), 1)
expect_equal(dim(pred$posterior), c(1, 3))
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) = "58"
expect_equal(pred$class, a)
})
test_that("predict.oslda works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- oslda(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran)
expect_equal(ncol(fit$x), 1)
pred <- predict(fit, newdata = iris[5,])
expect_equal(length(pred$class), 1)
expect_equal(dim(pred$posterior), c(1, 3))
})
test_that("predict.oslda: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 50, subset = ran)
expect_that(pred <- predict(fit, newdata = irisna), gives_warning("NAs in test observation 1"))
expect_equal(all(is.na(pred$class[1:17])), TRUE)
expect_equal(all(is.na(pred$posterior[1:17,])), TRUE)
})
test_that("predict.oslda: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- oslda(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
})
#=================================================================================================================
## fixed bandwidth
# res <- oslda(Species ~ ., data = iris, wf = "biweight", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- oslda(Species ~ ., data = iris, wf = biweight(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "cauchy", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = cauchy(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "cosine", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = cosine(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "epanechnikov", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = epanechnikov(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "exponential", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = exponential(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "gaussian", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = gaussian(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "optcosine", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = optcosine(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "rectangular", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = rectangular(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "triangular", bw = 5)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = triangular(bw = 5))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# ## fixed bandwidth, knn
# res <- oslda(Species ~ ., data = iris, wf = "biweight", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- oslda(Species ~ ., data = iris, wf = biweight(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "cauchy", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = cauchy(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "cosine", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = cosine(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "epanechnikov", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = epanechnikov(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "exponential", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = exponential(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "gaussian", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = gaussian(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "optcosine", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = optcosine(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = rectangular(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "triangular", bw = 5, k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = triangular(bw = 5, k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# ## adaptive bandwidth, knn only
# res <- loclda(Species ~ ., data = iris, wf = "biweight", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = biweight(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "cauchy", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = cauchy(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "cosine", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = cosine(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "epanechnikov", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = eoanechnikov(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "exponential", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = exponential(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "gaussian", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = gaussian(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "optcosine", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = optcosine(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "rectangular", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = rectangular(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "triangular", k = 20)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = triangular(k = 20))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# ## adaptive bandwidth, all obs
# res <- loclda(Species ~ ., data = iris, wf = "exponential", k = 100, nn.only = FALSE)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = exponential(k = 100, nn.only = FALSE))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
# res <- loclda(Species ~ ., data = iris, wf = "gaussian", k = 100, nn.only = FALSE)
# pred1 <- predict(res, newdata = iris[1,])
# res <- loclda(Species ~ ., data = iris, wf = gaussian(k = 100, nn.only = FALSE))
# pred2 <- predict(res, newdata = iris[1,])
# all.equal(pred1, pred2)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.