Coerce an Object of Class simculta to a Data Frame
Source: R/manCULTA-method-simculta.R
as.data.frame.simculta.RdCoerce an Object of Class simculta to a Data Frame
Usage
# S3 method for class 'simculta'
as.data.frame(x, ...)Examples
x <- GenCULTA2Profiles(
n = 10,
m = 6,
common_trait_loading = matrix(
data = c(1, 1.25, 1.50, 1.75),
ncol = 1
),
common_state_loading = matrix(
data = c(1, 1.5, 1.75, 2.00),
ncol = 1
),
mu_t = NULL,
psi_t = NULL,
mu_p = NULL,
psi_p = NULL,
theta = diag(4),
mu_profile = cbind(
c(-3, -3, -3, -3),
c(3, 3, 3, 3)
),
mu_x = 0,
sigma_x = 1,
nu_0 = -3.563,
kappa_0 = 0.122,
alpha_0 = -3.586,
beta_00 = 2.250,
gamma_00 = 0.063,
gamma_10 = 0.094,
phi_0 = 0.311,
phi_1 = 0,
psi_s0 = 0.151,
psi_s = 0.290
)
as.data.frame(x)
#> id covariate y1t0 y2t0 y3t0 y4t0 y1t1
#> 1 1 -0.004620768 3.1080489 0.9977467 1.9215219 1.9913586 1.735842
#> 2 2 0.760242168 0.2148366 3.0801909 2.2184058 3.0055116 1.046777
#> 3 3 0.038990913 0.9357182 1.0261230 2.9676951 2.0899550 1.218255
#> 4 4 0.735072142 1.6861361 1.8079013 5.8638165 4.4365411 4.894756
#> 5 5 -0.146472627 4.3736118 4.5153714 4.0347804 4.5687132 4.724019
#> 6 6 -0.057887335 -0.3134992 -0.7459390 0.5908366 0.7720265 1.191869
#> 7 7 0.482369466 4.7593152 4.5588281 5.6519466 3.9214528 2.367309
#> 8 8 0.992943637 2.2281491 3.9195557 1.2356924 -3.3121952 1.484396
#> 9 9 -1.246395498 4.4981812 3.7879362 2.6990034 3.0419821 2.827395
#> 10 10 -0.033487525 4.6622691 3.1374319 3.6806854 4.1330110 5.409956
#> y2t1 y3t1 y4t1 y1t2 y2t2 y3t2 y4t2
#> 1 3.3132902 0.8480728 0.6353742 3.45234853 4.6961437 3.59359462 3.1308017
#> 2 0.8543377 2.3989481 2.7637442 1.36956914 0.7266768 -0.03581078 2.9290683
#> 3 -0.6529763 3.9881195 1.3368407 1.19912011 1.3169599 3.95629368 0.8505573
#> 4 2.7343161 4.5287947 5.2618782 0.05458483 2.9742862 5.84936334 3.6466179
#> 5 5.0922759 1.3350222 4.2709694 3.84755520 4.6749262 3.02810194 4.9069587
#> 6 1.7741249 -0.1773161 0.7073458 0.65736841 1.0160142 -0.19055801 -1.2846326
#> 7 1.0347139 3.7434848 2.1976113 3.64496018 5.3416135 7.51682691 4.7614086
#> 8 3.8106610 1.6964474 -0.6895025 2.50317989 2.4757882 2.11031173 -4.1017037
#> 9 5.0742026 2.7981488 2.9945713 2.77875823 2.3588352 2.16833467 3.9011940
#> 10 4.1790619 6.6055497 4.4502146 5.11115263 3.8761384 5.73296149 7.1324909
#> y1t3 y2t3 y3t3 y4t3 y1t4 y2t4 y3t4
#> 1 3.6606035 4.2484156 2.464539 4.3682827 2.0073919 2.9807503 2.7819935
#> 2 0.9284153 1.6185724 2.118673 5.2652824 0.6396574 2.6909793 3.3474468
#> 3 1.0863579 -0.6222904 1.240434 -0.6683905 2.5199385 2.3624915 5.4429970
#> 4 3.0584306 3.3694957 3.891540 6.3831308 0.5614583 0.9992108 3.0465904
#> 5 1.9252602 2.0809743 1.030734 3.9444482 3.7363469 4.6899855 3.9401640
#> 6 0.8610324 4.6884660 3.031159 0.7146361 -0.8399775 -1.0492283 0.8369085
#> 7 2.2412320 2.5362055 2.930550 1.3697794 3.5420678 3.6801061 4.3657132
#> 8 1.9701487 3.7960511 4.932898 -1.1731110 3.0874226 2.5875204 3.2208770
#> 9 3.7694387 5.4419680 2.641453 4.9425494 3.9605453 4.5688973 0.8046080
#> 10 5.3584433 2.9613678 6.241951 6.7827407 3.1331554 3.3444586 6.6483184
#> y4t4 y1t5 y2t5 y3t5 y4t5
#> 1 1.797176 3.4469161 5.1526509 2.3515011 2.604708276
#> 2 3.951945 1.0914945 1.7710993 3.3119473 5.651994999
#> 3 2.722895 1.3155782 -0.5115318 3.5265271 1.581773524
#> 4 3.335513 0.2614324 2.2514080 3.6468394 1.740718838
#> 5 4.615780 0.8992359 3.8539116 4.3006589 3.534691886
#> 6 -2.156528 0.8719246 -1.3303913 0.5612111 -0.004986769
#> 7 5.120022 3.6043052 2.8104507 2.6920887 1.596079397
#> 8 -1.587495 2.1609117 4.8926099 4.5102523 0.463074095
#> 9 2.674186 4.2170706 5.2917078 3.3805426 3.902502867
#> 10 4.160614 3.7891796 2.9959138 4.6544914 5.727958749