require(glmMisrep) data <- data.frame( Y = c(0, 0, 2, 0, 3, 0, 36, 0, 2, 1, 0, 2, 6, 9, 0, 0, 0, 0, 7, 1, 1, 2, 50, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 1, 0, 1, 3, 176, 0, 0, 0, 0, 0, 2, 286, 0, 0, 0, 18), X1 = c(0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1), X2 = c(1.71870212, -0.55840901, 1.22589915, 0.53000107, 0.62571132, 0.02873955, 0.30989954, 1.35514993, 0.15587503, 0.27987513, 0.48892178, 0.35218767, 0.52382778, 1.58126751, -0.07855081, -0.57128802, -0.92500953, -2.48543328, 0.03810910, 0.39929906, -0.54854763, -0.10505694, 0.45120734, 0.32295222, -0.68595918, -0.66892486, 1.72253431, -0.28425276, -0.67719912, -0.39644260, -0.16843500, 0.90540261, -1.38574804, 0.14456841, 0.44142810, -1.89442541, -0.65961894, 2.13148776, 1.72410805, -1.60207312, -1.09525034, -1.31327168, -0.43378445, 1.33644956, 1.28938359, 0.90232362, -0.94112768, -0.61851917, 0.37033085, -0.47019541), X3 = c(0.6882029, 0.9934165, 0.9173388, 0.9406660, 0.5130041, 0.8590187, 0.4468488, 0.4186652, 0.5098278, 0.3339481, 0.6922477, 0.6793977, 0.4983724, 0.6079911, 0.7763041, 0.8529067, 0.8287771, 0.9125900, 0.4802076, 0.8981448, 0.3570093, 0.9209584, 0.4353817, 0.9426418, 0.9550002, 0.4869851, 0.9560156, 0.8247537, 0.1939687, 0.6103839, 0.7721900, 0.5980044, 0.8683831, 0.7004518, 0.8577210, 0.3576712, 0.9540088, 0.9880046, 0.4304899, 0.7416618, 0.6656063, 0.8920356, 0.6097593, 0.8008748, 0.8110432, 0.5967969, 0.6983106, 0.9471680, 0.9929186, 0.9059200), Sex = c("Female", "Male", "Male", "Female", "Male", "Female", "Male", "Male", "Female", "Female", "Male", "Male", "Male", "Female", "Male", "Male", "Female", "Female", "Male", "Female", "Female", "Male", "Female", "Male", "Male", "Male", "Male", "Female", "Female", "Female", "Male", "Male", "Male", "Male", "Female", "Female", "Female", "Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female", "Male", "Male", "Female", "Female", "Male"), Race = c("Black", "White", "White", "White", "White", "White", "Black", "Black", "Black", "Black", "Black", "Black", "White", "Other", "Black", "White", "Black", "White", "Other", "Black", "White", "Other", "Other", "White", "White", "White", "Black", "Other", "White", "Other", "White", "Black", "Other", "Black", "Other", "Other", "Other", "Black", "Other", "Black", "White", "Other", "Black", "White", "Black", "Other", "White", "Black", "Black", "Other"), V_star = c(0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0)) data$Race <- as.factor(data$Race) data$Sex <- as.factor(data$Sex) t1 <- tryCatch(poisRegMisrepEM(formula = y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6,0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # The response above is inappropriately specified (y, not Y) stopifnot( t1$message == "object 'y' not found" ) t2 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_Star", data = data, lambda = c(0.6,0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # Argument to 'v_star' is misspelled stopifnot( t2$message == "variable V_Star not present in dataframe" ) data$V_star <- ifelse(data$V_star == 1, yes = "yes", no = "no") t3 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6,0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # v* variable is type character (yes and no) stopifnot( t3$message == "v_star variable must be of class 'factor' or 'numeric'" ) data$V_star <- ifelse(data$V_star == "yes", yes = 1, no = 0) data$V_star[10] <- -1 t4 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6,0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # v* variable must be binary stopifnot( t4$message == "v_star variable must contain two unique values" ) data$V_star[10] <- 0 data$V_star <- ifelse(data$V_star == 1, yes = 1, no = 2) t5 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6,0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # v* must be binary, but more specifically 0/1; stopifnot( t5$message == "v_star variable must be coded with ones and zeroes" ) data$V_star <- ifelse(data$V_star == 1, yes = 1, no = 0) t6 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.49, 0.52), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # Inappropriately specified lambda argument stopifnot( t6$message == "Lambda vector must sum to one" ) t7 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(1/3, 1/3, 1/3), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # Inappropriately specified lambda argument stopifnot( t7$message == "Lambda vector must contain two elements" ) data$X4 <- data$X2*0.3 t8 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + X4 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6, 0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) # Linearly dependent covariates/degenerate design matrix stopifnot( t8$message == "Linear dependencies exist in the covariates" ) t9 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race, v_star = "V_star", data = data, lambda = c(0.6, 0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), error = function(x) x ) #V_star variable absent from formula stopifnot( t9$message == "v_star variable must be specified in 'formula'" ) # EM algorithm should fail to converge within the specified number of attempts t10 <- tryCatch( capture.output(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6, 0.4), epsilon = 1e-08, maxit = 2, maxrestarts = 1)), error = function(x) x ) stopifnot( t10$message == "NOT CONVERGENT! Failed to converge after 1 attempts" ) # On the first attempt, fails to converge, and restarts with new mixing props. # Succeeds on the second attempt. msg <- capture.output( t11 <- poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6, 0.4), epsilon = 1e-08, maxit = 16, maxrestarts = 4, verb = TRUE), type = "message" ) stopifnot( any(msg == "Warning: Failed to converge. Restarting with new mixing proportions") ) msg <- capture.output( t12 <- poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star, v_star = "V_star", data = data, lambda = c(0.6, 0.4), epsilon = 1e-08, maxit = 10000, maxrestarts = 20), type = "message" ) # Output validation; # Output should be a list stopifnot( is.list(t12) ) # With 14 elements stopifnot( length(t12) == 14 ) # Fisher information matrix should be symmetric stopifnot( isSymmetric(t12$cov.estimates) ) # The returned list should have elements with the following names # and types stopifnot( all.equal(lapply(t12, class), lapply(list(y = 0.1, lambda = 0.2, params = 0.3, loglik = 0.4, posterior = 0.5, all.loglik = 0.6, cov.estimates = matrix(data = c(1,2,3,4), 2, 2), std.error = 0.7, z.values = 0.8, p.values = 0.9, ICs = 1.0, ft = "*", formula = y ~ x, v_star_name = "v*" ), class) ) ) # Verifying the function can correctly calculate things; stopifnot( all.equal(t12$lambda, 0.2140361, tolerance = 9e-7 ) ) stopifnot( all.equal(as.numeric(t12$params), c(-1.1486407, 1.9813423, 1.0606001, -4.0278727, 1.8886687, 2.1052795, -0.1815366, 2.2582766), tolerance = 9e-7 ) ) stopifnot( all.equal( t12$loglik, -77.17481, tolerance = 9e-7 ) ) stopifnot( all.equal( t12$posterior, c(9.131063e-01, 9.167867e-01, 9.987564e-01, 8.007038e-01, 1.000000e+00, 7.980578e-01, 1.761218e-22, 9.999998e-01, 5.707215e-02, 4.986677e-01, 9.591568e-01, 9.998818e-01, 1.596141e-04, 1.000000e+00, 8.834283e-01, 8.269226e-01, 8.267814e-01, 7.909421e-01, 9.995369e-01, 3.002507e-01, 7.724813e-01, 5.033734e-01, 1.413033e-29, 7.033365e-04, 9.184187e-01, 9.118807e-01, 9.752457e-01, 4.103920e-01, 8.589374e-01, 9.278243e-01, 8.652112e-01, 1.000000e+00, 9.998403e-01, 9.999676e-01, 5.418147e-01, 8.817133e-01, 3.273919e-01, 1.253503e-01, 2.099810e-102, 8.124995e-01, 8.350465e-01, 9.998171e-01, 9.066149e-01, 1.000000e+00, 4.335937e-01, 2.097168e-171, 8.364673e-01, 7.911482e-01, 7.981111e-01, 1.769426e-10), tolerance = 9e-7 ) ) stopifnot( all.equal( t12$all.loglik, c(-91.21048, -77.54635, -77.24049, -77.18672, -77.17811, -77.17606, -77.17532, -77.17502, -77.17490, -77.17484, -77.17482, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481), tolerance = 9e-7 ) ) stopifnot( all.equal(t12$cov.estimates, matrix(data = c(0.0114492285, -0.0004087145, 0.0016883055, -0.001674646, -0.01493787, 0.004194090, 0.004620007, 0.003212934, 0.0008121915, -0.0004087145, 0.1376023242, -0.0103938967, -0.021763262, -0.10804979, 0.001850089, -0.001523425, -0.033947804, -0.0502985311, 0.0016883055, -0.0103938967, 0.0153809909, 0.005402047, -0.02063417, 0.003663544, 0.001754054, 0.008152043, 0.0006989913, -0.0016746456, -0.0217632624, 0.0054020474, 0.012549504, 0.02752248, -0.006829300, -0.008499404, 0.001601437, 0.0045586028, -0.0149378678, -0.1080497865, -0.0206341716, 0.027522475, 0.26797912, -0.040935476, -0.039438494, -0.023606980, 0.0234374153, 0.0041940900, 0.0018500887, 0.0036635440, -0.006829300, -0.04093548, 0.024832614, 0.009305140, 0.004723165, -0.0062878481, 0.0046200066, -0.0015234254, 0.0017540537, -0.008499404, -0.03943849, 0.009305140, 0.033156029, 0.023238618, -0.0090039233, 0.0032129343, -0.0339478042, 0.0081520434, 0.001601437, -0.02360698, 0.004723165, 0.023238618, 0.095149535, 0.0125163473, 0.0008121915, -0.0502985311, 0.0006989913, 0.004558603, 0.02343742, -0.006287848, -0.009003923, 0.012516347, 0.0479783934), ncol = 9, nrow = 9, byrow = TRUE, dimnames = list( c("lambda", names(t12$params)), c("lambda", names(t12$params)) ) ), tolerance = 9e-7 ) ) stopifnot( all.equal(as.numeric(t12$std.error), c(0.1070011, 0.3709479, 0.1240201, 0.1120246, 0.5176670, 0.1575837, 0.1820880, 0.3084632, 0.2190397), tolerance = 9e-7) ) stopifnot( all.equal(as.numeric(t12$z.values), c(-3.0965016, 15.9759747, 9.4675669, -7.7808179, 11.9851800, 11.5618810, -0.5885196, 10.3098958), tolerance = 9e-7) ) stopifnot( all.equal(as.numeric(t12$p.values), c(1.958188e-03, 1.878943e-57, 2.864376e-21, 7.205715e-15, 4.249202e-33, 6.428496e-31, 5.561836e-01, 6.357023e-25), tolerance = 9e-7) ) stopifnot( all.equal(as.numeric(t12$ICs), c(172.3496, 176.8496, 189.5578), tolerance = 9e-7) ) stopifnot( t12$ft == "poisRegMisrepEM" ) stopifnot( class(t12$formula) == "formula" ) stopifnot( t12$v_star_name == "V_star" ) # Test S3 method for summarizing misrepEM objects; stopifnot( class(summary(t12)) == "summary.misrepEM" ) # Output needs to be a list stopifnot( is.list(summary(t12)) ) # of length 5 stopifnot( length(summary(t12)) == 5 ) # Whose elements are: (1) dataframe, and (2-5) 4 numeric vectors, which have the following names: stopifnot( all.equal(lapply(summary(t12), FUN = class), list(coefficients = "data.frame", ICs = "numeric", loglik = "numeric", lambda = "numeric", lambda_stderror = "numeric") ) ) # Test S3 predict method test_data <- data.frame(Y = c(0, 1, 64, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 4 , 2, 0, 2, 5, 0, 0, 1, 14, 0, 0, 0, 3, 1, 0, 4, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 3, 0, 0, 3, 81, 0, 0, 0, 29, 7), X1 = c(1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0), X2 = c(0.11386635, 1.47979040, 0.45636287, -0.41664853, 1.25417252, 2.10499692, -1.00587356, -0.55405844, 0.17479943, -0.29340898, -0.05440845, -0.07675026, -1.89324885, 0.59106707, -0.65140908, -1.28337223, -0.48737945, 1.64559981, -0.62364285, 0.79564381, 0.38869489, 1.61959941, -1.76141082, 0.37854440, 1.02329557, 0.74965937, 0.04413995, -1.33996273, 2.08891011, -0.36087621, 0.32225472, -1.67851814, 0.41262611, -0.44329044, -0.30112059, -0.03306856, 2.12324955, 0.54000803, -0.85397350, -0.40084273, 0.89826028, -0.04130717, -1.01038714, 0.83043124, 1.15626228, -1.54063266, -0.88519104, 0.19762001, -0.09330770, 0.54228214), X3 = c(0.9250996, 0.8397037, 0.8063966, 0.5005301, 0.5261619, 0.9748920, 0.7095294, 0.9362987, 0.4623964, 0.2624171, 0.7273491, 0.4308770, 0.1454707, 0.5222497, 0.9163159, 0.1999648, 0.7829910, 0.9217782, 0.5697253, 0.3976736, 0.8473057, 0.7532322, 0.8627066, 0.8478911, 0.3253753, 0.8730831, 0.5964159, 0.9204428, 0.3093212, 0.6506881, 0.8470737, 0.7918149, 0.1817754, 0.4743152, 0.8122764, 0.7745114, 0.8042454, 0.8288124, 0.5462465, 0.7267012, 0.8394176, 0.3295204, 0.6816037, 0.4984404, 0.4564431, 0.9894151, 0.8408505, 0.3310416, 0.5424446, 0.2656163), Sex = c("Male", "Female", "Male", "Female", "Female", "Female", "Male", "Female", "Male", "Female", "Male", "Female", "Female", "Female", "Male", "Female", "Male", "Male", "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Male", "Male", "Male", "Male", "Female", "Male", "Male", "Male", "Male", "Male", "Female", "Male", "Female", "Female", "Male", "Male", "Male", "Female", "Male", "Female", "Male", "Male", "Female", "Female", "Male"), Race = c("Black", "Black", "Other", "Other", "White", "Black", "Black", "Black", "Black", "White", "White", "Other", "Black", "Black", "Black", "White", "Black", "White", "White", "Black", "Black", "Other", "White", "Other", "Black", "Black", "White", "Black", "Other", "Other", "White", "Black", "Other", "Other", "Black", "Black", "Other", "White", "White", "White", "Other", "Black", "Other", "Other", "White", "Black", "Black", "Black", "Other", "White"), V_star = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1)) # Output needs to be a numeric vector stopifnot( is.vector(predict(t12, test_data)) && is.numeric(predict(t12, test_data)) ) stopifnot( all.equal(predict(t12, test_data), c(1.170566e+00, 1.063411e+00, 3.073438e+00, 4.579298e+00, 2.468377e+00, 1.650861e-01, 1.172834e-01, 8.335130e-02, 1.110133e+00, 1.383375e+00, 1.811205e+00, 1.198719e+00, 2.266651e-01, 6.927864e-01, 7.426417e-02, 6.225827e-01, 5.104829e-01, 5.022287e+00, 3.897401e-02, 3.053586e+00, 4.470380e-02, 1.434410e+01, 3.582593e-03, 2.626893e+00, 7.172219e-01, 2.832969e+00, 1.586127e+00, 3.518873e-02, 1.285648e+02, 1.235260e+00, 2.299033e-01, 2.991943e-01, 3.631766e+01, 1.522515e+01, 1.637273e-01, 1.293711e-01, 1.816255e+01, 3.420085e-01, 2.433543e-01, 4.245742e+00, 3.118107e+01, 1.507549e+00, 1.176431e+00, 1.579893e+01, 9.947078e+00, 1.562451e-01, 7.854286e-02, 2.920283e-01, 1.840087e+01, 1.019649e+01), tolerance = 9e-7) )