dreamer_data | R Documentation |
See the model definitions below for specifics for each model.
dreamer_data_linear( n_cohorts, doses, b1, b2, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_linear_binary( n_cohorts, doses, b1, b2, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_quad( n_cohorts, doses, b1, b2, b3, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_quad_binary( n_cohorts, doses, b1, b2, b3, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_loglinear( n_cohorts, doses, b1, b2, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_loglinear_binary( n_cohorts, doses, b1, b2, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_logquad( n_cohorts, doses, b1, b2, b3, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_logquad_binary( n_cohorts, doses, b1, b2, b3, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_emax( n_cohorts, doses, b1, b2, b3, b4, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_emax_binary( n_cohorts, doses, b1, b2, b3, b4, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_exp( n_cohorts, doses, b1, b2, b3, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_exp_binary( n_cohorts, doses, b1, b2, b3, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_beta( n_cohorts, doses, b1, b2, b3, b4, scale, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_beta_binary( n_cohorts, doses, b1, b2, b3, b4, scale, link, times, t_max, longitudinal = NULL, ... ) dreamer_data_independent( n_cohorts, doses, b1, sigma, times, t_max, longitudinal = NULL, ... ) dreamer_data_independent_binary( n_cohorts, doses, b1, link, times, t_max, longitudinal = NULL, ... )
n_cohorts |
a vector listing the size of each cohort. |
doses |
a vector listing the dose for each cohort. |
b1, b2, b3, b4 |
parameters in the models. See sections below for each parameter's interpretation in a given model. |
sigma |
standard deviation. |
times |
the times at which data should be simulated if a longitudinal model is specified. |
t_max |
the t_max parameter used in the longitudinal model. |
longitudinal |
a string indicating the longitudinal model to be used. Can be "linear", "itp", or "idp". |
... |
additional longitudinal parameters. |
link |
character vector indicating the link function for binary models. |
scale |
a scaling parameter (fixed, specified by the user) for the beta models. |
A dataframe of random subjects from the specified model and parameters.
dreamer_data_linear()
: generate data from linear dose response.
dreamer_data_linear_binary()
: generate data from linear binary dose response.
dreamer_data_quad()
: generate data from quadratic dose response.
dreamer_data_quad_binary()
: generate data from quadratic binary dose response.
dreamer_data_loglinear()
: generate data from log-linear dose response.
dreamer_data_loglinear_binary()
: generate data from binary log-linear dose response.
dreamer_data_logquad()
: generate data from log-quadratic dose response.
dreamer_data_logquad_binary()
: generate data from log-quadratic binary dose
response.
dreamer_data_emax()
: generate data from EMAX dose response.
dreamer_data_emax_binary()
: generate data from EMAX binary dose response.
dreamer_data_exp()
: generate data from exponential dose response.
dreamer_data_exp_binary()
: generate data from exponential binary dose response.
dreamer_data_beta()
: generate data from Beta dose response.
dreamer_data_beta_binary()
: generate data from binary Beta dose response.
dreamer_data_independent()
: generate data from an independent dose response.
dreamer_data_independent_binary()
: generate data from an independent dose response.
y \sim N(f(d), σ^2)
f(d) = b_1 + b_2 * d
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
1 / σ^2 \sim Gamma(shape, rate)
y \sim N(f(d), σ^2)
f(d) = b_1 + b_2 * d + b_3 * d^2
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2)
1 / σ^2 \sim Gamma(shape, rate)
y \sim N(f(d), σ^2)
f(d) = b_1 + b_2 * log(d + 1)
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
1 / σ^2 \sim Gamma(shape, rate)
y \sim N(f(d), σ^2)
f(d) = b_1 + b_2 * log(d + 1) + b_3 * log(d + 1)^2
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2)
1 / σ^2 \sim Gamma(shape, rate)
y \sim N(f(d), σ^2)
f(d) = b_1 + (b_2 - b_1) * d ^ b_4 / (exp(b_3 * b_4) + d ^ b_4)
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2)
b_4 \sim N(mu_b4, sigma_b4 ^ 2), (Truncated above 0)
1 / σ^2 \sim Gamma(shape, rate)
Here, b_1 is the placebo effect (dose = 0), b_2 is the maximum treatment effect, b_3 is the log(ED50), and b_4 is the hill or rate parameter.
y \sim N(f(d), σ^2)
f(d) = b_1 + b_2 * (1 - exp(- b_3 * d))
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2), (truncated to be positive)
1 / σ^2 \sim Gamma(shape, rate)
y \sim Binomial(n, f(d))
link(f(d)) = b_1 + b_2 * d
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
y \sim Binomial(n, f(d))
link(f(d)) = b_1 + b_2 * d + b_3 * d^2
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2)
y \sim Binomial(n, f(d))
link(f(d)) = b_1 + b_2 * log(d + 1)
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
y \sim Binomial(n, f(d))
link(f(d)) = b_1 + b_2 * log(d + 1) + b_3 * log(d + 1)^2
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2)
y \sim Binomial(n, f(d))
link(f(d)) = b_1 + (b_2 - b_1) * d ^ b_4 / (exp(b_3 * b_4) + d ^ b_4)
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2)
b_4 \sim N(mu_b4, sigma_b4 ^ 2), (Truncated above 0)
Here, on the link(f(d)) scale, b_1 is the placebo effect (dose = 0), b_2 is the maximum treatment effect, b_3 is the log(ED50), and b_4 is the hill or rate parameter.
y \sim Binomial(n, f(d))
link(f(d)) = b_1 + b_2 * (exp(b_3 * d) - 1)
b_1 \sim N(mu_b1, sigma_b1 ^ 2)
b_2 \sim N(mu_b2, sigma_b2 ^ 2)
b_3 \sim N(mu_b3, sigma_b3 ^ 2), (Truncated below 0)
y \sim N(f(d), σ^2)
f(d) = b_{1d}
b_{1d} \sim N(mu_b1[d], sigma_b1[d] ^ 2)
1 / σ^2 \sim Gamma(shape, rate)
y \sim Binomial(n, f(d))
link(f(d)) = b_{1d}
b_{1d} \sim N(mu_b1[d], sigma_b1[d]) ^ 2
Let f(d) be a dose response model. The expected value of the response, y, is:
E(y) = g(d, t)
g(d, t) = a + (t / t_max) * f(d)
a \sim N(mu_a, sigma_a)
Let f(d) be a dose response model. The expected value of the response, y, is:
E(y) = g(d, t)
g(d, t) = a + f(d) * ((1 - exp(- c1 * t))/(1 - exp(- c1 * t_max)))
a \sim N(mu_a, sigma_a)
c1 \sim Uniform(a_c1, b_c1)
Increasing-Decreasing-Plateau (IDP).
Let f(d) be a dose response model. The expected value of the response, y, is:
E(y) = g(d, t)
g(d, t) = a + f(d) * (((1 - exp(- c1 * t))/(1 - exp(- c1 * d1))) * I(t < d1) + (1 - gam * ((1 - exp(- c2 * (t - d1))) / (1 - exp(- c2 * (d2 - d1))))) * I(d1 <= t <= d2) + (1 - gam) * I(t > d2))
a \sim N(mu_a, sigma_a)
c1 \sim Uniform(a_c1, b_c1)
c2 \sim Uniform(a_c2, b_c2)
d1 \sim Uniform(0, t_max)
d2 \sim Uniform(d1, t_max)
gam \sim Uniform(0, 1)
set.seed(888) data <- dreamer_data_linear( n_cohorts = c(20, 20, 20), dose = c(0, 3, 10), b1 = 1, b2 = 3, sigma = 5 ) head(data) plot(data$dose, data$response) abline(a = 1, b = 3) # longitudinal data set.seed(889) data_long <- dreamer_data_linear( n_cohorts = c(10, 10, 10, 10), # number of subjects in each cohort doses = c(.25, .5, .75, 1.5), # dose administered to each cohort b1 = 0, # intercept b2 = 2, # slope sigma = .5, # standard deviation, longitudinal = "itp", times = c(0, 12, 24, 52), t_max = 52, # maximum time a = .5, c1 = .1 ) ## Not run: ggplot(data_long, aes(time, response, group = dose, color = factor(dose))) + geom_point() ## End(Not run)
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