test_that('imputation simulating low abundant proteins',{
### n is big
set.seed(1)
df <- data.frame(A=rnorm(10000, 0, sd = 4), B = rnorm(10000, 1, sd = 3))
sam <- sample(1:10000, 1000)
df[sam, 1] <- NA
df <- impute_gaussian(df, width = 0.3, shift = -1.8)
# expect lower mean
expect_equal(mean(df[df$imputed==1, ]$A), -7.36754, tolerance = 0.00001)
expect_equal(mean(df[df$imputed==0, ]$A), -0.02089915, tolerance = 0.00001)
## n is small
set.seed(1)
df <- data.frame(A=rnorm(2000, 0, sd = 4), B = rnorm(2000, 1, sd = 3))
sam <- sample(1:2000, 100)
df[sam, 1] <- NA
df <- impute_gaussian(df, width = 0.5, shift = -1.8)
# expect lower mean
expect_equal(mean(df[df$imputed==1, ]$A), -7.67628, tolerance = 0.00001)
expect_equal(mean(df[df$imputed==0, ]$A), 0.009893299, tolerance = 0.00001)
})
test_that('imputation without shifting',{
## n is small
set.seed(2)
df <- data.frame(A=rnorm(2000, 0, sd = 4), B = rnorm(2000, 1, sd = 3))
sam <- sample(1:2000, 100)
df[sam, 1] <- NA
df <- impute_gaussian(df, width = 0.5, shift = 0)
# expect same mean
expect_equal(mean(df[df$imputed==1, ]$A), 0.2834652, tolerance = 0.00001)
expect_equal(mean(df[df$imputed==0, ]$A), 0.154915, tolerance = 0.00001)
})
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