tests/testthat/test_midasr.R

context("Testing midas_r")
set.seed(1001)
n<-250
trend<-c(1:n)
x<-rnorm(4*n)
z<-rnorm(12*n)
fn_x <- nealmon(p=c(1,-0.5),d=8)
fn_z <- nealmon(p=c(2,0.5,-0.1),d=17)
y<-2+0.1*trend+mls(x,0:7,4)%*%fn_x+mls(z,0:16,12)%*%fn_z+rnorm(n)


test_that("midas_r with start=NULL is the same as lm",{
    eq_u1<-lm(y~trend+mls(x,k=0:7,m=4)+mls(z,k=0:16,m=12))
    
    eq_u2<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL)
    
    eq_u3<-midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12))
    
    expect_that(sum(abs(coef(eq_u1) - coef(eq_u2))), equals(0))
    expect_that(sum(abs(coef(eq_u3) - coef(eq_u2))), equals(0))
})

test_that("midas_r without weights gives the same summary as midas_u",{
    a <-summary(lm(y~trend+mls(x,k=0:7,m=4)+mls(z,k=0:16,m=12)))
    
    b <-summary(midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL), vcov = NULL)
    
    expect_that(sum(abs(coef(a) - coef(b))), equals(0))
    
})

test_that("midas_r without start throws an error",{
    expect_that(midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12)), throws_error())
    
})


test_that("midas_u picks up data from main R environment",{
    eq_u1<-midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12))
    
    eq_u2<-midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12), 
                   data = list(y = y, trend = trend, x = x, z = z))
    
    expect_that(sum(abs(coef(eq_u1) - coef(eq_u2))), equals(0))
    
})

test_that("midas_r picks up data from main R environment",{
    eq_u1<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL)
    
    eq_u2<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12), 
                   data = list(y = y, trend = trend, x = x, z = z), start = NULL)
    
    expect_that(sum(abs(coef(eq_u1) - coef(eq_u2))), equals(0))
    
})


test_that("NLS problem solution is close to the DGP", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    expect_that(sum(abs(coef(a) - c(2,0.1,c(1,-0.5),c(2,0.5,-0.1)))), is_less_than(1))
    expect_that(sum(abs(coef(a,midas=TRUE)-c(2,0.1,fn_x,fn_z))), is_less_than(1))
})

test_that("Deriv tests give positive results", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    dt<-deriv_tests(a)
    expect_that(dt$first, is_false())
    expect_that(dt$second, is_true())
    expect_that(sum(abs(dt$gradient))/nrow(a$model), is_less_than(0.002))
})

test_that("Updating Ofunction works", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    b <- update(a, Ofunction = "nls")
    c <- update(b, Ofunction = "optimx", method = c("Nelder-Mead", "BFGS", "spg"))
    
    expect_that(a$argmap_opt$Ofunction == "optim", is_true())
    expect_that(b$argmap_opt$Ofunction == "nls", is_true())
    expect_that(c$argmap_opt$Ofunction == "optimx", is_true())
    
    expect_that(inherits(b$opt, "nls"), is_true())
    expect_that(inherits(c$opt, "optimx"), is_true())
    
    expect_that(a$convergence == 0, is_true())
    expect_that(b$convergence == 0, is_true())
    expect_that(c$convergence == 0, is_true())
    
})

test_that("Updating Ofunction arguments  works", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    b <- update(a, method = "CG")
    
    expect_that(b$argmap_opt$method == "CG", is_true())
    
})


test_that("update works with start=NULL",{
    eq_u1<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL)
    
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    eq_u2 <- update(a, start = NULL)
    
    expect_that(sum(abs(coef(eq_u1) - coef(eq_u2))), equals(0))
})

test_that("updating gradient works",{
    
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    b <- update(a, weight_gradients = list(nealmon = nealmon_gradient))
    
    expect_that(sum(abs(b$term_info$x$gradient(c(1,0.1,0.1)) - nealmon_gradient(c(1,0.1,0.1),8))), equals(0))
})

test_that("updating data and starting values works",{
    dt <- list(y = y, x = x, z = z, trend = trend)
    spd <- split_data(dt, insample = 1:200, outsample = 201:250)     
    
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),
                 start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)),
                 data = dt    
                 )
    
    c <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),
                 start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)),
                 data = spd$indata    
    )
    b <- update(a, data = spd$indata, start = list(x=c(1,-0.5),z=c(2,0.5,-0.1), 
                                                   `(Intercept)`= c$start_opt["(Intercept)"],
                                                   trend = c$start_opt["trend"]))
    
        
    expect_that(sum(abs(coef(b) - coef(c))), equals(0))
})
        

test_that("Gradient passing works", {
    eq_r2 <- midas_r(y ~ trend + mls(x, 0:7, 4, nealmon) + 
                         mls(z, 0:16, 12, nealmon),
                     start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
                     weight_gradients=list(nealmon = nealmon_gradient))
    
    dt <- deriv_tests(eq_r2)
    expect_that(sum(abs(dt$gradient))/nrow(eq_r2$model), is_less_than(1e-3))
    })

test_that("Gradient passing works for default gradients", {
    eq_r2 <- midas_r(y ~ trend + mls(x, 0:7, 4, nealmon) + 
                         mls(z, 0:16, 12, nealmon),
                     start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
                     weight_gradients=list())
    
    expect_that(sum(abs(eq_r2$term_info$x$gradient(c(1,0.1,0.1)) - nealmon_gradient(c(1,0.1,0.1),8))), equals(0))
    
})


test_that("Gradient passing works for nls", {
    eq_r2 <- midas_r(y ~ trend + mls(x, 0:7, 4, nealmon) + 
                         mls(z, 0:16, 12, nealmon),
                     start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
                     weight_gradients=list(nealmon = nealmon_gradient), Ofunction = "nls")
    
    dt <- deriv_tests(eq_r2)
    expect_that(sum(abs(dt$gradient))/nrow(eq_r2$model), is_less_than(1e-3))
})


test_that("Term info gathering works", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    expect_that(names(coef(a)), is_equivalent_to(c("(Intercept)", "trend", "x1", "x2", "z1", "z2", "z3")))         
    expect_that(length(coef(a)), equals(7))
    expect_that(length(coef(a, midas = TRUE)), equals(27))
    expect_that(names(a$term_info), is_equivalent_to(c("(Intercept)", "trend", "x", "z")))
    
    lgs <- lapply(a$term_info,"[[","lag_structure")
    expect_that(lgs[["(Intercept)"]], is_equivalent_to(0))    
    expect_that(lgs[["trend"]], is_equivalent_to(0))
    expect_that(lgs[["x"]], is_equivalent_to(0:7))    
    expect_that(lgs[["z"]], is_equivalent_to(0:16))
    
    expect_that(a$term_info[["x"]]$weight(coef(a, term_names = "x")), 
                is_equivalent_to(nealmon(coef(a, term_names = "x"), 8)))
    
    expect_that(a$term_info[["z"]]$weight(coef(a, term_names = "z")), 
                is_equivalent_to(nealmon(coef(a, term_names = "z"), 17)))
    
})


test_that("AR* model works", {
    a <- midas_r(y ~ trend + mls(y, c(1,4), 1, "*") + mls(x, 0:7, 4, nealmon) 
                 + mls(z, 0:16, 12, nealmon), start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    
    cfx <- coef(a, term_names = "x")
    cfb <- a$term_info$x$weight(cfx)
    mcfx <- coef(a, midas = TRUE, term_names = "x")
    cfy <- coef(a, midas = TRUE, term_names = "y")
    expect_that(sum(abs(mcfx[1:4] - cfb[1:4])), equals(0))
    expect_that(sum(abs(c(cfb[5:8],rep(0,4))-cfb*cfy[1]-mcfx[5:12])), equals(0))
    expect_that(sum(abs(-cfb*cfy[2]-mcfx[13:20])), equals(0))
})

test_that("AR* model works with gradient", {
    a <- midas_r(y ~ trend + mls(y, c(1,4), 1, "*") + mls(x, 0:7, 4, nealmon) 
                 + mls(z, 0:16, 12, nealmon), start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), weight_gradients = list())
    b <- midas_r(y ~ trend + mls(y, c(1,4), 1, "*") + mls(x, 0:7, 4, nealmon) 
                 + mls(z, 0:16, 12, nealmon), start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
    
    expect_that(sum(abs(b$gradD(coef(a)) - a$gradD(coef(a)))), is_less_than(1e-9))
})


test_that("Midas_r_simple works", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), Ofunction = "optimx")
    fn_a <- function(p, d) {
            c(nealmon(p[1:2],d=8),nealmon(p[3:5], d=17))
            }
    s <- midas_r_simple(y,cbind(mls(x, 0:7, 4), mls(z, 0:16, 12)), cbind(1, trend), fn_a, 
                            startx = c(1, -0.5, 2, 0.5, -0.1), startz = a$start_opt[1:2])
    
    expect_that(sum(abs(coef(s)[c(6:7,1:5)]-coef(a)))
, is_less_than(1e-11))
})

test_that("Midas_r_simple gradient works", {
    a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), Ofunction = "optimx", weight_gradients = list())
    fn_a <- function(p, d) {
        c(nealmon(p[1:2],d=8),nealmon(p[3:5], d=17))
    }
    gr_fn_a <- function(p, d) {
        gr1 <- nealmon_gradient(p[1:2], d=8)
        gr2 <- nealmon_gradient(p[3:5], d=17)
        cbind(rbind(gr1, matrix(0, nrow = 17, ncol = 2)),
            rbind(matrix(0, nrow = 8, ncol = 3), gr2))
    }
    
    s <- midas_r_simple(y,cbind(mls(x, 0:7, 4), mls(z, 0:16, 12)), cbind(1, trend), fn_a, 
                        startx = c(1, -0.5, 2, 0.5, -0.1), startz = a$start_opt[1:2], 
                        grw = gr_fn_a)
    
    expect_that(sum(abs(coef(s)[c(6:7,1:5)]-coef(a)))
                , is_less_than(1e-11))
    expect_that(sum(abs(s$gradient(coef(s))[c(6:7,1:5)]-a$gradient(coef(s)[c(6:7,1:5)]))),
                is_less_than(1e-10))
    expect_that(sum(abs(s$gradD(coef(s))[c(26:27,1:25),c(6:7,1:5)]-a$gradD(coef(s)[c(6:7,1:5)]))),
                equals(0))
    
})

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midasr documentation built on May 29, 2017, 4:12 p.m.