From 9ced2154eb644ec95bcfa5dda95447136b75c79d Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Thu, 16 May 2024 14:33:12 +1000 Subject: [PATCH] can't yet handle id in smooth terms --- R/mvgam.R | 11 +- src/mvgam.dll | Bin 1091584 -> 1091584 bytes tests/testthat/Rplots.pdf | Bin 1986013 -> 1986060 bytes tests/testthat/test-mvgam.R | 241 +++++++++++++++++++----------------- 4 files changed, 135 insertions(+), 117 deletions(-) diff --git a/R/mvgam.R b/R/mvgam.R index 2a34b198..57a29009 100644 --- a/R/mvgam.R +++ b/R/mvgam.R @@ -854,9 +854,18 @@ mvgam = function(formula, # For monotonic smooths, need to determine which direction to place # coefficient constraints smooth_labs <- do.call(rbind, lapply(seq_along(ss_gam$smooth), function(x){ - data.frame(label = ss_gam$smooth[[x]]$label, class = class(ss_gam$smooth[[x]])[1]) + data.frame(label = ss_gam$smooth[[x]]$label, + class = class(ss_gam$smooth[[x]])[1], + id = ifelse(is.null(ss_gam$smooth[[x]]$id), + NA, ss_gam$smooth[[x]]$id)) })) + # Check for 'id' arguments, which are not yet supported + if(any(!is.na(smooth_labs$id))){ + stop('smooth terms with the "id" argument not yet supported by mvgam', + call. = FALSE) + } + if(any(smooth_labs$class == 'random.effect')){ re_smooths <- smooth_labs %>% dplyr::filter(class == 'random.effect') %>% diff --git a/src/mvgam.dll b/src/mvgam.dll index 6cae4db80194b9980474949f8bc4c3076f620986..4899c9f87b14a8ddd3a7a0538688b08beb485b02 100644 GIT binary patch delta 80 zcmV~$yA42K0EN-;d%x!}hD3!4EMN?ZD-sREh}H<|Zwoq|bH=+G?`m7jJJTw>~yA+vePRcYIca iBV1vGC#>*AAVLv|SR^79naD+PQC6*)Y;y-)`3*nGe diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf index 9a87d72ba2bee8cc6dc0789fac99e9c18e1411ff..0a278e1ac0b538551aa70610a0689796d2a64797 100644 GIT binary patch delta 113130 zcmYg%XIN9)^YyiWA_58uNWChE2n0dt1XM&y1Vm6u2u+aQI{}WONCznrq$|C50tun_ z-dhMg^w2{uZ}8sV{l6db1adNa_ROqVvre*ui**Z%bNeoOMOfgefanw9FzMU>`|~r@ z2YSH=deH}Z$p?D56iQZ#LP+ydZ~_FFQa&fr4p*&bhv>Vffm7POf)u8`bdk2<=Y^uj 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zd)K7S#`{}?RYJKtRXbo#WrmEgRz0`NROgZr5}!9q&63TtKqlcI7fT!6aD|=#M#*c8 z$guS#b2bkebjU=>Ggvc;dMyPsbd!W?S;$ZiUi1T9N?P0qOz9F4@<(|`c3H}!N;V+W zDhjp=9E>`(jUMIGoN)PoHnQ$Ma%3s0LxWjX;dhQX<~57DVH9%Pti3Y)-ddE!v8^bJ znAbfqnp&T;W~y`vVpn7f%vp6NW@DlX1{j6q%=AQ>n31hsx|V}cGbtVzY-ty4X(x_V zFql!YSq3XZjOVe+0vLG}aSm*YXa#mb-HXtxq-z$J9PXAp(;O{CVilNl6zVc5?8-7> zfiszR_*sl~I>q8m<47aYF!WS-BA%5<*LFc&Uto|C9YI0vbOeQy>e32}gVBY|kVT`z zT1%zsG#0^X6;_d?b%_%vEiIht`(`j-3?$MyvmK40?qd`zR6;q4OA&IZ9zo(_VTY(` zJG7u~G*n_*X|(?vBS5yDN-J2y2Zg_-r{h?vz`NLR>%k%Jn+iCy~e1!Ufr7% zjdN-^sF~NdLo`9@je%YL#JpxA#u8QU6%27XsIW#tl2vhmiPoxUjn!cVb4}YfbA7}n zQAIjpc45w^mwJH1kd&Vq%zKq%Nu@}F6(X6CI=wMiME>Y z(&V%8I0+^aRn%al22ZL0@z#)g4Cayw7noO~MqsF}VlA&?hn6qw0_^Uq@9X9brhXvO cR~RCF1ruvJU+!#e@54aUH$1#`*S5a@02Z&E5C8xG diff --git a/tests/testthat/test-mvgam.R b/tests/testthat/test-mvgam.R index 871b97e4..d1320658 100644 --- a/tests/testthat/test-mvgam.R +++ b/tests/testthat/test-mvgam.R @@ -1,5 +1,130 @@ context("mvgam") +test_that("family must be correctly specified", { + expect_error(mod <- mvgam(y ~ s(season), + trend_model = 'AR1', + data = beta_data$data_train, + family = 'besta', + run_model = FALSE), + 'family not recognized') +}) + +test_that("response variable must be specified", { + expect_error(mod <- mvgam( ~ s(season), + trend_model = 'AR1', + data = beta_data$data_train, + family = betar(), + run_model = FALSE), + 'response variable is missing from formula') +}) + +test_that("id to link smooths not allowed yet", { + expect_error(mod <- mvgam(y ~ s(time, id = 1) + + s(time, by = series, id = 1), + data = beta_data$data_train, + family = betar(), + run_model = FALSE), + 'smooth terms with the "id" argument not yet supported by mvgam') +}) + +test_that("response variable must follow family-specific restrictions", { + expect_error(mod <- mvgam(y ~ s(season), + trend_model = 'AR1', + data = gaus_data$data_train, + family = lognormal(), + run_model = FALSE), + 'Values <= 0 not allowed for lognormal responses') + + expect_error(mod <- mvgam(y ~ s(season), + trend_model = 'AR1', + data = gaus_data$data_train, + family = poisson(), + run_model = FALSE), + 'Values < 0 not allowed for count family responses') +}) + +test_that("trend_model must be correctly specified", { + expect_error(mod <- mvgam(y ~ s(season), + trend_model = 'AR11', + data = beta_data$data_train, + family = betar(), + run_model = FALSE)) +}) + +test_that("outcome variable must be present in data", { + data = data.frame(out = rnorm(100), + temp = rnorm(100), + time = 1:100) + expect_error(mod <- mvgam(formula = y ~ dynamic(temp, rho = 20), + data = data, + family = gaussian(), + run_model = FALSE), + 'variable y not found in data') +}) + +test_that("series levels must match unique entries in series", { + levels(beta_data$data_train$series) <- paste0('series_', 1:6) + expect_error(mvgam(y ~ s(season), + trend_model = 'GP', + data = beta_data$data_train, + newdata = beta_data$data_test, + family = betar(), + run_model = FALSE)) +}) + +test_that("missing values not allowed in predictors", { + # Include missing vals in training data + simdat <- sim_mvgam() + simdat$data_train$season[4] <- NA + expect_error(mvgam(y ~ s(season), + trend_model = 'GP', + data = simdat$data_train, + newdata = simdat$data_test, + run_model = FALSE)) + + # Include missing vals in testing data + simdat <- sim_mvgam() + simdat$data_test$season[4] <- NA + expect_error(mvgam(y ~ s(season), + data = simdat$data_train, + newdata = simdat$data_test, + run_model = FALSE)) +}) + +test_that("all series must have observations for all unique timepoints", { + data <- sim_mvgam() + data$data_train <- data$data_train[-2,] + expect_error(mod <- mvgam(y ~ s(season), + trend_model = 'AR1', + data = data$data_train, + family = poisson(), + run_model = FALSE), + 'One or more series in data is missing observations for one or more timepoints') + + data <- sim_mvgam() + data$data_test <- data$data_test[-2,] + expect_error(mod <- mvgam(y ~ s(season), + trend_model = 'AR1', + data = data$data_train, + newdata = data$data_test, + family = poisson(), + run_model = FALSE), + 'One or more series in newdata is missing observations for one or more timepoints') +}) + +test_that("rho argument must be positive numeric", { + data = data.frame(out = rnorm(100), + temp = rnorm(100), + time = 1:100) + expect_error(mod <- mvgam(formula = out ~ dynamic(temp, rho = -1), + data = data, + family = gaussian(), + run_model = FALSE), + 'Argument "rho" in dynamic() must be a positive value', + fixed = TRUE) +}) + +# Skip remaining tests on CRAN as they are slightly time-consuming skip_on_cran() test_that("JAGS setups should work", { @@ -82,24 +207,6 @@ test_that("JAGS setups should work", { expect_true(inherits(mod, 'mvgam_prefit')) }) -test_that("family must be correctly specified", { - expect_error(mod <- mvgam(y ~ s(season), - trend_model = 'AR1', - data = beta_data$data_train, - family = 'besta', - run_model = FALSE), - 'family not recognized') -}) - -test_that("response variable must be specified", { - expect_error(mod <- mvgam( ~ s(season), - trend_model = 'AR1', - data = beta_data$data_train, - family = betar(), - run_model = FALSE), - 'response variable is missing from formula') -}) - test_that("prior_only works", { mod <- mvgam(y ~ s(season), trend_model = AR(p = 2), @@ -170,41 +277,6 @@ test_that("prior_only works", { mod$model_file, fixed = TRUE))) }) -test_that("response variable must follow family-specific restrictions", { - expect_error(mod <- mvgam(y ~ s(season), - trend_model = 'AR1', - data = gaus_data$data_train, - family = lognormal(), - run_model = FALSE), - 'Values <= 0 not allowed for lognormal responses') - - expect_error(mod <- mvgam(y ~ s(season), - trend_model = 'AR1', - data = gaus_data$data_train, - family = poisson(), - run_model = FALSE), - 'Values < 0 not allowed for count family responses') -}) - -test_that("trend_model must be correctly specified", { - expect_error(mod <- mvgam(y ~ s(season), - trend_model = 'AR11', - data = beta_data$data_train, - family = betar(), - run_model = FALSE)) -}) - -test_that("outcome variable must be present in data", { - data = data.frame(out = rnorm(100), - temp = rnorm(100), - time = 1:100) - expect_error(mod <- mvgam(formula = y ~ dynamic(temp, rho = 20), - data = data, - family = gaussian(), - run_model = FALSE), - 'variable y not found in data') -}) - test_that("time not required in data if this is a no trend model", { data = data.frame(out = rnorm(100), temp = rnorm(100)) @@ -215,39 +287,6 @@ test_that("time not required in data if this is a no trend model", { expect_true(inherits(mod, 'mvgam_prefit')) }) -test_that("rho argument must be positive numeric", { - data = data.frame(out = rnorm(100), - temp = rnorm(100), - time = 1:100) - expect_error(mod <- mvgam(formula = out ~ dynamic(temp, rho = -1), - data = data, - family = gaussian(), - run_model = FALSE), - 'Argument "rho" in dynamic() must be a positive value', - fixed = TRUE) -}) - -test_that("all series must have observations for all unique timepoints", { - data <- sim_mvgam() - data$data_train <- data$data_train[-2,] - expect_error(mod <- mvgam(y ~ s(season), - trend_model = 'AR1', - data = data$data_train, - family = poisson(), - run_model = FALSE), - 'One or more series in data is missing observations for one or more timepoints') - - data <- sim_mvgam() - data$data_test <- data$data_test[-2,] - expect_error(mod <- mvgam(y ~ s(season), - trend_model = 'AR1', - data = data$data_train, - newdata = data$data_test, - family = poisson(), - run_model = FALSE), - 'One or more series in newdata is missing observations for one or more timepoints') -}) - test_that("median coefs should be stored in the mgcv object", { expect_true(identical(unname(coef(mvgam:::mvgam_example2$mgcv_model)), coef(mvgam:::mvgam_example2)[,2])) @@ -277,36 +316,6 @@ test_that("empty obs formula allowed if trend_formula supplied", { fixed = TRUE))) }) -test_that("missing values not allowed in predictors", { - # Include missing vals in training data - simdat <- sim_mvgam() - simdat$data_train$season[4] <- NA - expect_error(mvgam(y ~ s(season), - trend_model = 'GP', - data = simdat$data_train, - newdata = simdat$data_test, - run_model = FALSE)) - - # Include missing vals in testing data - simdat <- sim_mvgam() - simdat$data_test$season[4] <- NA - expect_error(mvgam(y ~ s(season), - data = simdat$data_train, - newdata = simdat$data_test, - run_model = FALSE)) -}) - -test_that("series levels must match unique entries in series", { - # Include missing vals in training data - simdat <- sim_mvgam() - levels(simdat$data_train$series) <- paste0('series_', 1:6) - expect_error(mvgam(y ~ s(season), - trend_model = 'GP', - data = simdat$data_train, - newdata = simdat$data_test, - run_model = FALSE)) -}) - test_that("share_obs_params working", { # Standard beta mod <- mvgam(y ~ s(season, by = series),