Description Usage Arguments Value See Also Examples
imposes missing values, and performs analysis of incomplete data
1 2 3 4 5 6 | miss_gen_an(dt, m2, b_trt = 0, b_y = 0, b_x1 = 0, b_x2 = 0,
b_ty = 0, do = 0.1, seed, dt_out = FALSE, sing_anal = TRUE,
mice_anal = FALSE, bw_anal = FALSE, mu_C = 1, sd_C = 0,
mu_T = 1, sd_T = 0, ci_method = wald_ci, seed_mice,
alpha = 0.025, n_mi, m_mi, method = c("wald", "wn", "fm"),
pro_wnmi = FALSE)
|
dt |
dataframe, on which missing values are imposed |
m2 |
numeric, margin |
b_trt |
numeric, group missingness parameter, Default: 0 |
b_y |
numeric, outcome missingness parameter, Default: 0 |
b_x1 |
numeric, covariate x1 missingness parameter, Default: 0 |
b_x2 |
numeric, covariate x2 missingness parameter, Default: 0 |
b_ty |
numeric, covariatet treatment by group interaction, Default: 0 |
do |
numeric, target drop-out rate, Default: 0.1 |
seed |
numeric, seed specification before deleting observations |
dt_out |
logic, determines if the incomplete dataset needs to be returned (if returned, no analysis will be done), Default: FALSE |
sing_anal |
logic, determines if single value analysis needs to be performed (complete case analysis, best and worst case scenario imputation), Default: TRUE |
mice_anal |
logic, determines if multiple imputation via MICE needs to be performed (nested imputation), Default: FALSE |
bw_anal |
logic, determines if the following single value imputation needs to be performed: best case scenario for group 'c' and worst case scenario for 't', Default: FALSE |
mu_C |
numeric, mean parameter for k multiplier normal distribution for group 'c', Default: 1 |
sd_C |
numeric, stadnard deviation parameter for k multiplier normal distribution for group 'c', Default: 0 |
mu_T |
Pnumeric, mean parameter for k multiplier normal distribution for group 't',Default: 1 |
sd_T |
numeric, stadnard deviation parameter for k multiplier normal distribution for group 't', Default: 0 |
ci_method |
ci function name, Default: wald_ci |
seed_mice |
numeric, Default: seed_mice |
alpha |
numeric, one-sided alpha, Default: 0.025 |
n_mi |
numeric, number of multiple imputed patient level data |
m_mi |
numeric, number of multiple imputed models |
method |
character, method to construct confidence interval, Default: c('wald','wn','fm') |
pro_wnmi |
logic, specifies whether proper wn cobminaiton rules should be used, if TRUE, then bin2mi package needs to be used, Default: FALSE |
list
1 2 3 4 5 6 | t <- sim_cont(
p_C = 0.6, p_T = 0.5, n_arm = 200,
mu1 = 4, mu2 = 100, sigma1 = 1,
sigma2 = 20, r12 = -0.3,
b1 = 0.1, b2 = -0.01)
miss_gen_an(t, m2 = 0.1 , b_x1 = 0.5, do = 0.1, seed = 1555, seed_mice = 1555, alpha = 0.025)
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