tests/testthat/test_os_multinom.R
In schiffner/locClass: Collection of Local Classification Methods
context("osmultinom")
test_that("osmultinom: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(osmultinom(formula = Species ~ V1, data = iris, wf = "gaussian", bw = 10, trace = FALSE))
# wrong class
# FIXME
# expect_error(osmultinom(formula = iris, data = iris, wf = "gaussian", bw = 10, trace = FALSE)) # target variable is Sepal.Length
# expect_error(osmultinom(iris, data = iris, wf = "gaussian", bw = 10, trace = FALSE)) # target variable is Sepal.Length
# target variable also in x
expect_warning(osmultinom(Species ~ Species + Petal.Width, data = iris, wf = "gaussian", bw = 10, trace = FALSE)) ## warning, Species on RHS removed
})
# test_that("osmultinom throws a warning if y variable is numeric", {
# data(iris)
# # formula, data
# expect_that(osmultinom(formula = Sepal.Length ~ ., data = iris, wf = "gaussian", bw = 10, trace = FALSE), gives_warning("'y' was coerced to a factor"))
# # y, x
# expect_that(osmultinom(y = iris[,1], x = iris[,-1], wf = "gaussian", bw = 10, trace = FALSE), gives_warning("'y' was coerced to a factor"))
# })
test_that("osmultinom works if only one predictor variable is given", {
data(iris)
fit <- osmultinom(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 5, trace = FALSE)
predict(fit)
})
test_that("osmultinom: training data from only one class", {
data(iris)
expect_that(osmultinom(Species ~ ., data = iris, bw = 2, subset = 1:50, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
expect_that(osmultinom(Species ~ ., data = iris, bw = 2, subset = 1, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
iris2 <- iris[1:100,]
iris2$Species <- factor(iris2$Species, levels = unique(iris2$Species))
expect_that(osmultinom(Species ~ ., data = iris2, bw = 2, subset = 1:50, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
expect_that(osmultinom(Species ~ ., data = iris2, bw = 2, subset = 1, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
})
test_that("osmultinom: subsetting works", {
data(iris)
# formula, data
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = 1:80, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = iris[1:80,], wf = "gaussian", bw = 2, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-28],fit2[-28])
# wrong specification of subset argument
expect_error(osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = iris[1:10,], trace = FALSE))
expect_error(fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = FALSE, trace = FALSE))
expect_error(fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 0, trace = FALSE))
expect_error(osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = -10:50, trace = FALSE))
})
test_that("osmultinom: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_that(osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail, trace = FALSE), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(28,34)], fit2[-28])
### NA in y
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_that(osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail, trace = FALSE), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(28,34)], fit2[-28])
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_that(osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.fail, trace = FALSE), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.omit, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 11:60, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(28,34)], fit2[-28])
})
test_that("osmultinom: try all weight functions", {
Wts = runif(n = 18, -0.5, 0.5)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(5), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(bw = 5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "epanechnikov", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = epanechnikov(bw = 5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "triangular", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = triangular(5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "biweight", bw = 5, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = biweight(5), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "optcosine", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = optcosine(5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "cosine", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = cosine(5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
})
test_that("osmultinom: local solution with rectangular window function and large bw and global solution coincide", {
library(nnet)
Wts = runif(n = 18, -0.5, 0.5)
## formula
fit1 <- multinom(formula = Species ~ ., data = iris, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(20), Wts = Wts, trace = FALSE)
pred1 <- predict(fit1, type = "probs")
pred2 <- predict(fit2, type = "probs")
expect_equal(pred1, pred2)
pred1 <- predict(fit1, type = "class")
pred2 <- predict(fit2, type = "class")
expect_equivalent(pred1, pred2)
})
test_that("osmultinom: arguments related to weighting misspecified", {
# bw, k not required
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), k = 30, bw = 0.5, trace = FALSE), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
fit2 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), trace = FALSE)
expect_equal(fit1[-28], fit2[-28])
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), bw = 0.5, trace = FALSE), gives_warning("argument 'bw' is ignored"))
fit2 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), trace = FALSE)
expect_equal(fit1[-28], fit2[-28])
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 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, k = 30, trace = FALSE), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
expect_that(fit2 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), k = 30, trace = FALSE), gives_warning("argument 'k' is ignored"))
expect_equal(fit1[-28], fit2[-28])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, trace = FALSE), gives_warning("argument 'bw' is ignored"))
fit2 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), trace = FALSE)
expect_equal(fit1[-28], fit2[-28])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
# missing quotes
fit <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian, trace = FALSE) ## error because length(weights) and nrow(x) are different
expect_error(predict(fit))
# bw, k missing
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = gaussian(), trace = FALSE), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = gaussian(), k = 10, trace = FALSE), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(osmultinom(Species ~ ., data = iris, trace = FALSE), throws_error("either 'bw' or 'k' have to be specified"))
# bw < 0
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = -5, trace = FALSE), throws_error("'bw' must be positive"))
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "cosine", k = 10, bw = -50, trace = FALSE), throws_error("'bw' must be positive"))
# bw vector
expect_that(osmultinom(formula = Species ~., data = iris, wf = "gaussian", bw = rep(1, nrow(iris)), trace = FALSE), gives_warning("only first element of 'bw' used"))
# k < 0
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k =-7, bw = 50, trace = FALSE), throws_error("'k' must be positive"))
# k too small
#fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k = 5, bw = 0.005, trace = FALSE)
#expect_equal(length(is.na(predict(fit)$class)), 150)
# k too large
expect_that(osmultinom(formula = Species ~ ., data = iris, k = 250, wf = "gaussian", bw = 50, trace = FALSE), throws_error("'k' is larger than 'n'"))
# k vector
expect_that(osmultinom(formula = Species ~., data = iris, wf = "gaussian", k = rep(50, nrow(iris)), trace = FALSE), gives_warning("only first element of 'k' used"))
})
test_that("osmultinom: weighting schemes work", {
## wf with finite support
# fixed bw
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(bw = 5), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$k, 50)
expect_equal(fit1$bw, NULL)
expect_true(fit1$nn.only)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, 50)
expect_true(fit1$nn.only)
expect_true(!fit1$adaptive)
# nn.only not needed
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, nn.only = TRUE, trace = FALSE), gives_warning("argument 'nn.only' is ignored"))
# expect_that(osmultinom(y = class.ind(iris$Species), x = iris[,-5], wf = "rectangular", bw = 5, nn.only = TRUE, softmax = TRUE, trace = FALSE), gives_warning("argument 'nn.only' is ignored"))
# nn.only has to be TRUE if bw and k are both given
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, nn.only = FALSE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
# expect_that(osmultinom(y = class.ind(iris$Species), x = iris[,-5], wf = "rectangular", bw = 5, k = 50, nn.only = FALSE, softmax = TRUE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
## wf with infinite support
# fixed bw
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 0.5, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(fit1$adaptive)
# adaptive bw, all obs
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, nn.only = FALSE, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(k = 50, nn.only = FALSE), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, FALSE)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(bw = 1, k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 1)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(!fit1$adaptive)
# nn.only has to be TRUE if bw and k are both given
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, nn.only = FALSE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
# expect_that(osmultinom(y = class.ind(iris$Species), x = iris[,-5], wf = "gaussian", bw = 1, k = 50, nn.only = FALSE, softmax = TRUE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
})
#=================================================================================================================
context("predict.osmultinom")
test_that("predict.osmultinom works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred <- predict(fit, type = "probs")
expect_equal(rownames(pred), rownames(iris)[ran])
pred <- predict(fit, type = "class")
expect_equal(names(pred), rownames(iris)[ran])
## formula, data, newdata
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred <- predict(fit, newdata = iris[-ran,], type = "probs")
expect_equal(rownames(pred), rownames(iris)[-ran])
pred <- predict(fit, newdata = iris[-ran,], type = "class")
expect_equal(names(pred), rownames(iris)[-ran])
})
test_that("predict.osmultinom: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, trace = FALSE)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
pred1 <- predict(fit, type = "probs")
pred2 <- predict(fit, newdata = iris, type = "probs")
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
pred1 <- predict(fit, type = "probs")
pred2 <- predict(fit, newdata = iris[ran,], type = "probs")
expect_equal(pred1, pred2)
})
test_that("predict.osmultinom works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
expect_that(fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 1:100, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(ncol(fit$y), 1)
expect_equal(fit$n[3], 1)
expect_equal(fit$lev, c("setosa", "versicolor", "virginica"))
expect_equal(fit$lev1, c("setosa", "versicolor"))
pred <- predict(fit, type = "probs")
expect_equal(ncol(pred), 1)
pred <- predict(fit, type = "class")
expect_equal(nlevels(pred), 3)
pred <- predict(fit, newdata = iris[-ran,], type = "probs")
expect_equal(ncol(pred), 1)
pred <- predict(fit, newdata = iris[-ran,], type = "class")
expect_equal(nlevels(pred), 3)
})
test_that("predict.osmultinom works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
expect_equal(ncol(fit$x), 2) # + intercept
expect_equal(fit$coefnames, c("(Intercept)", "Petal.Width"))
predict(fit, newdata = iris[-ran,])
})
test_that("predict.osmultinom works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred <- predict(fit, newdata = iris[5,], type = "probs")
expect_equal(dim(pred), c(1, 3)) ## drop
pred <- predict(fit, newdata = iris[5,], type = "class")
expect_equal(length(pred), 1)
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) = "5"
expect_equal(pred, a)
pred <- predict(fit, newdata = iris[58,], type = "probs")
expect_equal(dim(pred), c(1, 3)) ##
pred <- predict(fit, newdata = iris[58,], type = "class")
expect_equal(length(pred), 1)
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) = "58"
expect_equal(pred, a)
})
test_that("predict.osmultinom works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
expect_equal(ncol(fit$x), 2)
expect_equal(fit$coefnames, c("(Intercept)", "Petal.Width"))
pred <- predict(fit, newdata = iris[5,], type = "probs")
expect_equal(dim(pred), c(1, 3))
pred <- predict(fit, newdata = iris[5,], type = "class")
expect_equal(length(pred), 1)
})
test_that("predict.osmultinom: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 50, subset = ran, trace = FALSE)
pred <- predict(fit, newdata = irisna, type = "probs")
expect_equal(all(is.na(pred[1:17,])), TRUE)
pred <- predict(fit, newdata = irisna, type = "class")
expect_equal(all(is.na(pred[1:17])), TRUE)
})
test_that("predict.osmultinom: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
})
schiffner/locClass documentation built on May 29, 2019, 3:39 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
context("osmultinom")
test_that("osmultinom: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(osmultinom(formula = Species ~ V1, data = iris, wf = "gaussian", bw = 10, trace = FALSE))
# wrong class
# FIXME
# expect_error(osmultinom(formula = iris, data = iris, wf = "gaussian", bw = 10, trace = FALSE)) # target variable is Sepal.Length
# expect_error(osmultinom(iris, data = iris, wf = "gaussian", bw = 10, trace = FALSE)) # target variable is Sepal.Length
# target variable also in x
expect_warning(osmultinom(Species ~ Species + Petal.Width, data = iris, wf = "gaussian", bw = 10, trace = FALSE)) ## warning, Species on RHS removed
})
# test_that("osmultinom throws a warning if y variable is numeric", {
# data(iris)
# # formula, data
# expect_that(osmultinom(formula = Sepal.Length ~ ., data = iris, wf = "gaussian", bw = 10, trace = FALSE), gives_warning("'y' was coerced to a factor"))
# # y, x
# expect_that(osmultinom(y = iris[,1], x = iris[,-1], wf = "gaussian", bw = 10, trace = FALSE), gives_warning("'y' was coerced to a factor"))
# })
test_that("osmultinom works if only one predictor variable is given", {
data(iris)
fit <- osmultinom(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 5, trace = FALSE)
predict(fit)
})
test_that("osmultinom: training data from only one class", {
data(iris)
expect_that(osmultinom(Species ~ ., data = iris, bw = 2, subset = 1:50, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
expect_that(osmultinom(Species ~ ., data = iris, bw = 2, subset = 1, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
iris2 <- iris[1:100,]
iris2$Species <- factor(iris2$Species, levels = unique(iris2$Species))
expect_that(osmultinom(Species ~ ., data = iris2, bw = 2, subset = 1:50, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
expect_that(osmultinom(Species ~ ., data = iris2, bw = 2, subset = 1, trace = FALSE), throws_error("need two or more classes to fit a multinom model"))
})
test_that("osmultinom: subsetting works", {
data(iris)
# formula, data
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = 1:80, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = iris[1:80,], wf = "gaussian", bw = 2, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-28],fit2[-28])
# wrong specification of subset argument
expect_error(osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = iris[1:10,], trace = FALSE))
expect_error(fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = FALSE, trace = FALSE))
expect_error(fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 0, trace = FALSE))
expect_error(osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = -10:50, trace = FALSE))
})
test_that("osmultinom: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_that(osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail, trace = FALSE), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(28,34)], fit2[-28])
### NA in y
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_that(osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail, trace = FALSE), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(28,34)], fit2[-28])
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_that(osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.fail, trace = FALSE), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.omit, trace = FALSE), gives_warning("group virginica is empty"))
expect_that(fit2 <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 11:60, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(28,34)], fit2[-28])
})
test_that("osmultinom: try all weight functions", {
Wts = runif(n = 18, -0.5, 0.5)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(5), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(bw = 5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "epanechnikov", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = epanechnikov(bw = 5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "triangular", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = triangular(5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "biweight", bw = 5, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = biweight(5), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "optcosine", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = optcosine(5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "cosine", bw = 5, k = 30, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = cosine(5, k = 30), Wts = Wts, trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
pred1 <- predict(fit1)
pred2 <- predict(fit2)
expect_equal(pred1, pred2)
})
test_that("osmultinom: local solution with rectangular window function and large bw and global solution coincide", {
library(nnet)
Wts = runif(n = 18, -0.5, 0.5)
## formula
fit1 <- multinom(formula = Species ~ ., data = iris, Wts = Wts, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(20), Wts = Wts, trace = FALSE)
pred1 <- predict(fit1, type = "probs")
pred2 <- predict(fit2, type = "probs")
expect_equal(pred1, pred2)
pred1 <- predict(fit1, type = "class")
pred2 <- predict(fit2, type = "class")
expect_equivalent(pred1, pred2)
})
test_that("osmultinom: arguments related to weighting misspecified", {
# bw, k not required
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), k = 30, bw = 0.5, trace = FALSE), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
fit2 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), trace = FALSE)
expect_equal(fit1[-28], fit2[-28])
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), bw = 0.5, trace = FALSE), gives_warning("argument 'bw' is ignored"))
fit2 <- osmultinom(Species ~ ., data = iris, wf = gaussian(0.5), trace = FALSE)
expect_equal(fit1[-28], fit2[-28])
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 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, k = 30, trace = FALSE), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
expect_that(fit2 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), k = 30, trace = FALSE), gives_warning("argument 'k' is ignored"))
expect_equal(fit1[-28], fit2[-28])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
expect_that(fit1 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, trace = FALSE), gives_warning("argument 'bw' is ignored"))
fit2 <- osmultinom(Species ~ ., data = iris, wf = function(x) exp(-x), trace = FALSE)
expect_equal(fit1[-28], fit2[-28])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
# missing quotes
fit <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian, trace = FALSE) ## error because length(weights) and nrow(x) are different
expect_error(predict(fit))
# bw, k missing
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = gaussian(), trace = FALSE), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = gaussian(), k = 10, trace = FALSE), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(osmultinom(Species ~ ., data = iris, trace = FALSE), throws_error("either 'bw' or 'k' have to be specified"))
# bw < 0
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = -5, trace = FALSE), throws_error("'bw' must be positive"))
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "cosine", k = 10, bw = -50, trace = FALSE), throws_error("'bw' must be positive"))
# bw vector
expect_that(osmultinom(formula = Species ~., data = iris, wf = "gaussian", bw = rep(1, nrow(iris)), trace = FALSE), gives_warning("only first element of 'bw' used"))
# k < 0
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k =-7, bw = 50, trace = FALSE), throws_error("'k' must be positive"))
# k too small
#fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k = 5, bw = 0.005, trace = FALSE)
#expect_equal(length(is.na(predict(fit)$class)), 150)
# k too large
expect_that(osmultinom(formula = Species ~ ., data = iris, k = 250, wf = "gaussian", bw = 50, trace = FALSE), throws_error("'k' is larger than 'n'"))
# k vector
expect_that(osmultinom(formula = Species ~., data = iris, wf = "gaussian", k = rep(50, nrow(iris)), trace = FALSE), gives_warning("only first element of 'k' used"))
})
test_that("osmultinom: weighting schemes work", {
## wf with finite support
# fixed bw
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(bw = 5), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$k, 50)
expect_equal(fit1$bw, NULL)
expect_true(fit1$nn.only)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, 50)
expect_true(fit1$nn.only)
expect_true(!fit1$adaptive)
# nn.only not needed
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, nn.only = TRUE, trace = FALSE), gives_warning("argument 'nn.only' is ignored"))
# expect_that(osmultinom(y = class.ind(iris$Species), x = iris[,-5], wf = "rectangular", bw = 5, nn.only = TRUE, softmax = TRUE, trace = FALSE), gives_warning("argument 'nn.only' is ignored"))
# nn.only has to be TRUE if bw and k are both given
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, nn.only = FALSE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
# expect_that(osmultinom(y = class.ind(iris$Species), x = iris[,-5], wf = "rectangular", bw = 5, k = 50, nn.only = FALSE, softmax = TRUE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
## wf with infinite support
# fixed bw
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 0.5, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(fit1$adaptive)
# adaptive bw, all obs
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, nn.only = FALSE, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(k = 50, nn.only = FALSE), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, FALSE)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, trace = FALSE)
fit2 <- osmultinom(formula = Species ~ ., data = iris, wf = gaussian(bw = 1, k = 50), trace = FALSE)
expect_equal(fit1[-c(22,28)], fit2[-c(22,28)])
expect_equal(fit1$bw, 1)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(!fit1$adaptive)
# nn.only has to be TRUE if bw and k are both given
expect_that(osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, nn.only = FALSE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
# expect_that(osmultinom(y = class.ind(iris$Species), x = iris[,-5], wf = "gaussian", bw = 1, k = 50, nn.only = FALSE, softmax = TRUE, trace = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
})
#=================================================================================================================
context("predict.osmultinom")
test_that("predict.osmultinom works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred <- predict(fit, type = "probs")
expect_equal(rownames(pred), rownames(iris)[ran])
pred <- predict(fit, type = "class")
expect_equal(names(pred), rownames(iris)[ran])
## formula, data, newdata
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred <- predict(fit, newdata = iris[-ran,], type = "probs")
expect_equal(rownames(pred), rownames(iris)[-ran])
pred <- predict(fit, newdata = iris[-ran,], type = "class")
expect_equal(names(pred), rownames(iris)[-ran])
})
test_that("predict.osmultinom: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, trace = FALSE)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
pred1 <- predict(fit, type = "probs")
pred2 <- predict(fit, newdata = iris, type = "probs")
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- osmultinom(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
pred1 <- predict(fit, type = "probs")
pred2 <- predict(fit, newdata = iris[ran,], type = "probs")
expect_equal(pred1, pred2)
})
test_that("predict.osmultinom works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
expect_that(fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 1:100, trace = FALSE), gives_warning("group virginica is empty"))
expect_equal(ncol(fit$y), 1)
expect_equal(fit$n[3], 1)
expect_equal(fit$lev, c("setosa", "versicolor", "virginica"))
expect_equal(fit$lev1, c("setosa", "versicolor"))
pred <- predict(fit, type = "probs")
expect_equal(ncol(pred), 1)
pred <- predict(fit, type = "class")
expect_equal(nlevels(pred), 3)
pred <- predict(fit, newdata = iris[-ran,], type = "probs")
expect_equal(ncol(pred), 1)
pred <- predict(fit, newdata = iris[-ran,], type = "class")
expect_equal(nlevels(pred), 3)
})
test_that("predict.osmultinom works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
expect_equal(ncol(fit$x), 2) # + intercept
expect_equal(fit$coefnames, c("(Intercept)", "Petal.Width"))
predict(fit, newdata = iris[-ran,])
})
test_that("predict.osmultinom works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
pred <- predict(fit, newdata = iris[5,], type = "probs")
expect_equal(dim(pred), c(1, 3)) ## drop
pred <- predict(fit, newdata = iris[5,], type = "class")
expect_equal(length(pred), 1)
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) = "5"
expect_equal(pred, a)
pred <- predict(fit, newdata = iris[58,], type = "probs")
expect_equal(dim(pred), c(1, 3)) ##
pred <- predict(fit, newdata = iris[58,], type = "class")
expect_equal(length(pred), 1)
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) = "58"
expect_equal(pred, a)
})
test_that("predict.osmultinom works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
expect_equal(ncol(fit$x), 2)
expect_equal(fit$coefnames, c("(Intercept)", "Petal.Width"))
pred <- predict(fit, newdata = iris[5,], type = "probs")
expect_equal(dim(pred), c(1, 3))
pred <- predict(fit, newdata = iris[5,], type = "class")
expect_equal(length(pred), 1)
})
test_that("predict.osmultinom: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 50, subset = ran, trace = FALSE)
pred <- predict(fit, newdata = irisna, type = "probs")
expect_equal(all(is.na(pred[1:17,])), TRUE)
pred <- predict(fit, newdata = irisna, type = "class")
expect_equal(all(is.na(pred[1:17])), TRUE)
})
test_that("predict.osmultinom: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- osmultinom(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, trace = FALSE)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.