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Add tests to test_emuFit_micro comparing closed-form MPLEs to MPLE obtained via numerical optimization #95

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Oct 24, 2024
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Add tests to test_emuFit_micro comparing closed-form MPLEs to MPLE obtained via numerical optimization #95

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100 changes: 100 additions & 0 deletions tests/testthat/test-emuFit_micro.R
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,106 @@ test_that("With or without 'working_constraint' we get same results", {
})



test_that("PL fit with categorical predictor matches analytical form of MPLE in this case,
and does NOT match MLE", {
set.seed(90333)
X <- cbind(1,rep(c(0,1),each = 20))
z <- rnorm(40)
J <- 10
p <- 2
n <- 40
b0 <- rnorm(J)
b1 <- seq(1,10,length.out = J)/5
b <- rbind(b0,b1)
Y <- matrix(NA,ncol = J, nrow = 40)

for(i in 1:40){
for(j in 1:J){
temp_mean <- exp(X[i,,drop = FALSE]%*%b[,j,drop = FALSE] + z[i])
Y[i,j] <- rpois(1, lambda = temp_mean)
}
}
pl_fit <- emuFit_micro_penalized(X,
Y,
B = matrix(rnorm(20),nrow = 2),
constraint_fn = function(x) mean(x),
maxit = 200,
tolerance = 1e-8,
verbose= FALSE)

ml_fit <- emuFit_micro(X,
Y,
B = matrix(rnorm(20),nrow = 2),
constraint_fn = function(x) mean(x),
maxit = 200,
tolerance = 1e-8,
verbose= FALSE)

cs_grp1 <- colSums(Y[1:20,])
cs_grp2 <- colSums(Y[21:40,])

bhat1 <- log(cs_grp1 + 0.5) - log(cs_grp1[1] + 0.5)
bhat2 <- log(cs_grp2 + 0.5) - log(cs_grp2[1] +0.5)
bhat2 <- bhat2 - bhat1
bhat1 <- bhat1 - mean(bhat1)
bhat2 <- bhat2 - mean(bhat2)
analytical_B <- rbind(bhat1,bhat2)

expect_true(max(abs(pl_fit$B - analytical_B))< 1e-7)
expect_true(max(abs(ml_fit - analytical_B))>0.01)
})


test_that("PL fit with categorical predictor matches analytical form of MPLE in this case,
and does NOT match MLE when group sizes are unequal", {
set.seed(90333)
X <- cbind(1,rep(c(0,0,0,1),each = 10))
z <- rnorm(40)
J <- 10
p <- 2
n <- 40
b0 <- rnorm(J)
b1 <- seq(1,10,length.out = J)/5
b <- rbind(b0,b1)
Y <- matrix(NA,ncol = J, nrow = 40)

for(i in 1:40){
for(j in 1:J){
temp_mean <- exp(X[i,,drop = FALSE]%*%b[,j,drop = FALSE] + z[i])
Y[i,j] <- rpois(1, lambda = temp_mean)
}
}
pl_fit <- emuFit_micro_penalized(X,
Y,
B = matrix(rnorm(20),nrow = 2),
constraint_fn = function(x) mean(x),
maxit = 200,
tolerance = 1e-8,
verbose= FALSE)

ml_fit <- emuFit_micro(X,
Y,
B = matrix(rnorm(20),nrow = 2),
constraint_fn = function(x) mean(x),
maxit = 200,
tolerance = 1e-8,
verbose= FALSE)

cs_grp1 <- colSums(Y[1:30,])
cs_grp2 <- colSums(Y[31:40,])

bhat1 <- log(cs_grp1 + 0.5) - log(cs_grp1[1] + 0.5)
bhat2 <- log(cs_grp2 + 0.5) - log(cs_grp2[1] +0.5)
bhat2 <- bhat2 - bhat1
bhat1 <- bhat1 - mean(bhat1)
bhat2 <- bhat2 - mean(bhat2)
analytical_B <- rbind(bhat1,bhat2)

expect_true(max(abs(pl_fit$B - analytical_B))< 1e-7)
expect_true(max(abs(ml_fit - analytical_B))>0.01)
})

test_that("We get same results with and without warm start", {
set.seed(4323)
X <- cbind(1,rep(c(0,1),each = 20))
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