Description Usage Arguments Value References See Also Examples
This function summarizes the results of contmixvar1
, corrvar
, or
corrvar2
. The inputs are either the simulated variables or inputs for those functions. See their
documentation for more information. If summarizing result from contmixvar1
, mixture parameters may be
entered as vectors instead of lists.
1 2 3 4 5 6 7  summary_var(Y_cat = NULL, Y_cont = NULL, Y_comp = NULL, Y_mix = NULL,
Y_pois = NULL, Y_nb = NULL, means = NULL, vars = NULL, skews = NULL,
skurts = NULL, fifths = NULL, sixths = NULL, mix_pis = list(),
mix_mus = list(), mix_sigmas = list(), mix_skews = list(),
mix_skurts = list(), mix_fifths = list(), mix_sixths = list(),
marginal = list(), lam = NULL, p_zip = 0, size = NULL, prob = NULL,
mu = NULL, p_zinb = 0, rho = NULL)

Y_cat 
a matrix of ordinal variables 
Y_cont 
a matrix of continuous nonmixture variables 
Y_comp 
a matrix of components of continuous mixture variables 
Y_mix 
a matrix of continuous mixture variables 
Y_pois 
a matrix of Poisson variables 
Y_nb 
a matrix of Negative Binomial variables 
means 
a vector of means for the 
vars 
a vector of variances for the 
skews 
a vector of skewness values for the 
skurts 
a vector of standardized kurtoses (kurtosis  3, so that normal variables have a value of 0)
for the 
fifths 
a vector of standardized fifth cumulants for the 
sixths 
a vector of standardized sixth cumulants for the 
mix_pis 
a list of length 
mix_mus 
a list of length 
mix_sigmas 
a list of length 
mix_skews 
a list of length 
mix_skurts 
a list of length 
mix_fifths 
a list of length 
mix_sixths 
a list of length 
marginal 
a list of length equal to 
lam 
a vector of lambda (mean > 0) constants for the Poisson variables (see 
p_zip 
a vector of probabilities of structural zeros (not including zeros from the Poisson distribution) for the
zeroinflated Poisson variables (see 
size 
a vector of size parameters for the Negative Binomial variables (see 
prob 
a vector of success probability parameters for the NB variables; order the same as in 
mu 
a vector of mean parameters for the NB variables (*Note: either 
p_zinb 
a vector of probabilities of structural zeros (not including zeros from the NB distribution) for the zeroinflated NB variables
(see 
rho 
the target correlation matrix which must be ordered
1st ordinal, 2nd continuous nonmixture, 3rd components of continuous mixtures, 4th regular Poisson, 5th zeroinflated Poisson,
6th regular NB, 7th zeroinflated NB; note that 
A list whose components vary based on the type of simulated variables.
If ordinal variables are produced:
ord_sum
a list, where the ith element contains a data.frame with target and simulated cumulative probabilities for ordinal variable Y_i
If continuous variables are produced:
cont_sum
a data.frame summarizing Y_cont
and Y_comp
,
target_sum
a data.frame with the target distributions for Y_cont
and Y_comp
,
mix_sum
a data.frame summarizing Y_mix
,
target_mix
a data.frame with the target distributions for Y_mix
,
If Poisson variables are produced:
pois_sum
a data.frame summarizing Y_pois
If Negative Binomial variables are produced:
nb_sum
a data.frame summarizing Y_nb
Additionally, the following elements:
rho_calc
the final correlation matrix for Y_cat
, Y_cont
, Y_comp
, Y_pois
, and Y_nb
rho_mix
the final correlation matrix for Y_cat
, Y_cont
, Y_mix
, Y_pois
, and Y_nb
maxerr
the maximum final correlation error of rho_calc
from the target rho
.
See references for SimCorrMix
.
contmixvar1
, corrvar
, corrvar2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90  # Using normal mixture variable from contmixvar1 example
Nmix < contmixvar1(n = 1000, "Polynomial", means = 0, vars = 1,
mix_pis = c(0.4, 0.6), mix_mus = c(2, 2), mix_sigmas = c(1, 1),
mix_skews = c(0, 0), mix_skurts = c(0, 0), mix_fifths = c(0, 0),
mix_sixths = c(0, 0))
Nsum < summary_var(Y_comp = Nmix$Y_comp, Y_mix = Nmix$Y_mix,
means = 0, vars = 1, mix_pis = c(0.4, 0.6), mix_mus = c(2, 2),
mix_sigmas = c(1, 1), mix_skews = c(0, 0), mix_skurts = c(0, 0),
mix_fifths = c(0, 0), mix_sixths = c(0, 0))
## Not run:
# 2 continuous mixture, 1 binary, 1 zeroinflated Poisson, and
# 1 zeroinflated NB variable
n < 10000
seed < 1234
# Mixture variables: Normal mixture with 2 components;
# mixture of Logistic(0, 1), Chisq(4), Beta(4, 1.5)
# Find cumulants of components of 2nd mixture variable
L < calc_theory("Logistic", c(0, 1))
C < calc_theory("Chisq", 4)
B < calc_theory("Beta", c(4, 1.5))
skews < skurts < fifths < sixths < NULL
Six < list()
mix_pis < list(c(0.4, 0.6), c(0.3, 0.2, 0.5))
mix_mus < list(c(2, 2), c(L[1], C[1], B[1]))
mix_sigmas < list(c(1, 1), c(L[2], C[2], B[2]))
mix_skews < list(rep(0, 2), c(L[3], C[3], B[3]))
mix_skurts < list(rep(0, 2), c(L[4], C[4], B[4]))
mix_fifths < list(rep(0, 2), c(L[5], C[5], B[5]))
mix_sixths < list(rep(0, 2), c(L[6], C[6], B[6]))
mix_Six < list(list(NULL, NULL), list(1.75, NULL, 0.03))
Nstcum < calc_mixmoments(mix_pis[[1]], mix_mus[[1]], mix_sigmas[[1]],
mix_skews[[1]], mix_skurts[[1]], mix_fifths[[1]], mix_sixths[[1]])
Mstcum < calc_mixmoments(mix_pis[[2]], mix_mus[[2]], mix_sigmas[[2]],
mix_skews[[2]], mix_skurts[[2]], mix_fifths[[2]], mix_sixths[[2]])
means < c(Nstcum[1], Mstcum[1])
vars < c(Nstcum[2]^2, Mstcum[2]^2)
marginal < list(0.3)
support < list(c(0, 1))
lam < 0.5
p_zip < 0.1
size < 2
prob < 0.75
p_zinb < 0.2
k_cat < k_pois < k_nb < 1
k_cont < 0
k_mix < 2
Rey < matrix(0.39, 8, 8)
diag(Rey) < 1
rownames(Rey) < colnames(Rey) < c("O1", "M1_1", "M1_2", "M2_1", "M2_2",
"M2_3", "P1", "NB1")
# set correlation between components of the same mixture variable to 0
Rey["M1_1", "M1_2"] < Rey["M1_2", "M1_1"] < 0
Rey["M2_1", "M2_2"] < Rey["M2_2", "M2_1"] < Rey["M2_1", "M2_3"] < 0
Rey["M2_3", "M2_1"] < Rey["M2_2", "M2_3"] < Rey["M2_3", "M2_2"] < 0
# check parameter inputs
validpar(k_cat, k_cont, k_mix, k_pois, k_nb, "Polynomial", means,
vars, skews, skurts, fifths, sixths, Six, mix_pis, mix_mus, mix_sigmas,
mix_skews, mix_skurts, mix_fifths, mix_sixths, mix_Six, marginal, support,
lam, p_zip, size, prob, mu = NULL, p_zinb, rho = Rey)
# check to make sure Rey is within the feasible correlation boundaries
validcorr(n, k_cat, k_cont, k_mix, k_pois, k_nb, "Polynomial", means,
vars, skews, skurts, fifths, sixths, Six, mix_pis, mix_mus, mix_sigmas,
mix_skews, mix_skurts, mix_fifths, mix_sixths, mix_Six, marginal,
lam, p_zip, size, prob, mu = NULL, p_zinb, Rey, seed)
# simulate without the error loop
Sim1 < corrvar(n, k_cat, k_cont, k_mix, k_pois, k_nb, "Polynomial", means,
vars, skews, skurts, fifths, sixths, Six, mix_pis, mix_mus, mix_sigmas,
mix_skews, mix_skurts, mix_fifths, mix_sixths, mix_Six, marginal, support,
lam, p_zip, size, prob, mu = NULL, p_zinb, Rey, seed, epsilon = 0.01)
Summ1 < summary_var(Sim1$Y_cat, Y_cont = NULL, Sim1$Y_comp, Sim1$Y_mix,
Sim1$Y_pois, Sim1$Y_nb, means, vars, skews, skurts, fifths, sixths,
mix_pis, mix_mus, mix_sigmas, mix_skews, mix_skurts, mix_fifths,
mix_sixths, marginal, lam, p_zip, size, prob, mu = NULL, p_zinb, Rey)
Sim1_error < abs(Rey  Summ1$rho_calc)
summary(as.numeric(Sim1_error))
## End(Not run)

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