Nothing
context("Mean of model")
test_that("Checking mean errors",{
#Confidence > 1
expect_error({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = runif(10,20,60),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = sample(c(0,1), 10, replace = TRUE),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height,
datasvy$age, datasvy$sex, days = 3)
#Calculate survey mean and variance for 25 days
model_mean(model_weight, design = design, days = 1, confidence = 2)
})
#Negative confidence
expect_error({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = runif(10,20,60),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = sample(c(0,1), 10, replace = TRUE),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height,
datasvy$age, datasvy$sex, days = 3)
#Calculate survey mean and variance for 25 days
model_mean(model_weight, design = design, days = 1, confidence = -2)
})
#Adult bmi to calculate bmi category
expect_error({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = runif(10,20,60),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = sample(c(0,1), 10, replace = TRUE),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height,
datasvy$age, datasvy$sex, days = 3)
#Calculate survey mean and variance for 25 days
model_mean(model_weight, design = design, days = 1, meanvars = "BMI_Category")
})
#Different value meanvar
expect_error({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = runif(10,20,60),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = sample(c(0,1), 10, replace = TRUE),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height,
datasvy$age, datasvy$sex, days = 3)
#Calculate survey mean and variance for 25 days
model_mean(model_weight, design = design, days = 1, meanvars = "I_am_bored")
})
#Check that time is on list
expect_error({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = runif(10,20,60),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = sample(c(0,1), 10, replace = TRUE),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height,
datasvy$age, datasvy$sex, days = 3)
#Calculate survey mean and variance for 25 days
timelist <- which(names(model_weight) == "Time")
model_mean(model_weight[-timelist], design = design, days = 1)
})
})
test_that("Checking mean warnings",{
# Warning for taking time to calculate
expect_warning({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = runif(10,20,60),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = sample(c(0,1), 10, replace = TRUE),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height, datasvy$age, datasvy$sex, days = 51)
#Calculate survey mean and variance for 25 days
model_mean(model_weight, design = design, days = 1:51)
})
expect_warning({
#Antropometric data
datasvy <- data.frame(
id = 1,
age = 30,
sex = "female",
weight = 60,
height = 1.89)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height, datasvy$age, datasvy$sex, days = 1)
#Calculate survey mean and variance for 25 days
bw <- model_mean(model_weight, days = 0)
})
})
test_that("Test mean object",{
#Check results are groupped
expect_true({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = rep(30,10),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = rep(1, 10),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Group to calculate means
group <- datasvy$group
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height, datasvy$age, datasvy$sex, days = 2)
#Calculate survey mean and variance for 25 days
all(model_mean(model_weight, design = design, group = group, days = 1:2)$group == 1)
})
#Check results are groupped
expect_true({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = rep(30,10),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
group = c(rep(1, 5), rep(2, 5)),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Group to calculate means
group <- datasvy$group
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height, datasvy$age, datasvy$sex, days = 2)
#Calculate survey mean and variance for 25 days
length(unique(model_mean(model_weight, design = design, group = group, days = 1:2)$group)) == 2
})
#Check results are correct
expect_true({
#Antropometric data
probs <- runif(10, 20, 60)
datasvy <- data.frame(
id = 1:10,
age = rep(30,10),
sex = sample(c("male","female"),10, replace = TRUE),
weight = runif(10,60,80),
height = runif(10,160,180),
svyw = probs/sum(probs))
#Create survey design using survey package
design <- svydesign(id = ~id, weights = datasvy$svyw,
data = datasvy)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height, datasvy$age, datasvy$sex, days = 2)
#Calculate survey mean and variance for 25 days
bw <- model_mean(model_weight, design = design, days = 0)
hall <- bw[which(bw[,which(colnames(bw) == "variable")] == "Body_Weight"),which(colnames(bw) == "mean")]
#Check that the mean is correct
abs(weighted.mean(datasvy$weight, datasvy$svyw) - hall) < 0.0001
})
#Check results are correct for only 1 individual
expect_warning(expect_true({
#Antropometric data
datasvy <- data.frame(
id = 1,
age = 30,
sex = "female",
weight = 60,
height = 1.89)
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(datasvy$weight, datasvy$height, datasvy$age, datasvy$sex, days = 1)
#Calculate survey mean and variance for 25 days
bw <- model_mean(model_weight, days = 0)
hall <- bw[which(bw[,which(colnames(bw) == "variable")] == "Body_Weight"),which(colnames(bw) == "mean")]
#Check that the mean is correct
abs(hall - datasvy$weight) < 0.0001
}))
})
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