gen_psa_samp: Generate PSA Sample
In DARTH-git/dampack: Decision-Analytic Modeling Package

Description Usage Arguments Details Value See Also Examples

gen_psa_samp generates a data.frame of sampled parameter values from user-specified distributions to be used in a probabilistic sensitivity analysis (PSA)

gen_psa_samp(
  params = NULL,
  dists = c("normal", "log-normal", "truncated-normal", "beta", "gamma", "dirichlet",
    "bootstrap", "constant", "triangle"),
  parameterization_types = c("mean, sd", "a, b", "shape, scale",
    "value, mean_prop, sd", "value, n", "value, alpha", "mean, sd, ll, ul", "val",
    "meanlog, sdlog", "ll, ul, mode"),
  dists_params = NULL,
  nsamp = 100
)

`params`	string vector with the names of parameters to be generated by `gen_psa_samp` and used by a user-defined function in `run_psa` to calculate outcomes.
`dists`	string vector with the distributions from which `params` will be drawn.
`parameterization_types`	string vector with parameterization types for each `dists`
`dists_params`	list of input parameters required to by specific `dists` and `parameterization_types` to fully describe distribution and generate parameter samples.
`nsamp`	number of sets of parameter values to be generated

Length of vectors params, dists, parameterization_types, and list dists_params must all be the same. The nth element of dists, parameterization_types, and dists_params all define the distribution that will be used to draw samples of the corresponding nth element of the params vector.

For a given element of params:

If dists == "normal", parameterization_types can only be "mean, sd", and the corresponding element of list dists_params must be the the vector c(mean, sd)
If dists == "log-normal", parameterization_types can be either "mean, sd" or "meanlog, sdlog", and the corresponding element of list dists_params must be either the the vector c(mean, sd) or c(meanlog, sdlog). Use "mean, sd" if you have sample mean and sample standard deviation of an empirical sample of the random variable, and use "meanlog, sdlog" if you want to directly specify the parameters of the log-normal distribution as specified by rlnorm
If dists == "truncated-normal", parameterization_types can only be "mean, sd, ll, ul", and dists_params must be the vector c(mean, sd, ll, ul), where ll is the lower limit of the distribution and ul is the upper limit of the distribution. If either the lower limit or the upper limit does not exist, simply specify NA in the corresponding position of the dists_params vector.
If dists == "beta", parameterization_types can be "mean, sd" or "a, b" and the corresponding element of list dists_params must be the the vector c(mean, sd) or c(a, b), respectively.
If dists == "gamma", parameterization_types can be "mean, sd" or "shape, scale" and the corresponding element of list dists_params must be the the vector c(mean, sd) or c(shape, scale), respectively.
If dists == "dirichlet", parameterization_types can be "value, mean_prop, sd", "value, n", or "value, alpha".
- If parameterization_types == "value, mean_prop, sd", then the corresponding element of list dists_params must be a data.frame where the first column is a string vector of the the different multinomial outcomes. These multinomial outcomes will become column names in the data.frame returned by gen_psa_samp, and therefore the strings in this column should correspond to variable names used in FUN for run_psa. The second and third columns of this dists_params should be numerical vectors containing the sample means and sample standard errors for each of the multinomial outcomes.
- If parameterization_types == "value, n", then dists_params must be a data.frame with the first column being a string vector of the multinomial outcomes, and the second column being a vector of the observed number of each multinomial outcome in a sample.
- If parameterization_types == "value, alpha", then dists_params must be a data.frame with the first column being a string vector of the multinomial outcomes, and the second column must be a numerical vector of the alpha parameter values for each multinomial outcome in the dirichlet distribution.
If dists == "bootstrap", parameterization_types can only be "value, weight", and dists_params must be a data.frame with the first column being a numerical vector containing all of the bootstrap sample values, and the second column being an integer vector designating the sampling weights of each bootstrap sample value. For example, the number of rows in the dists_params data.frame is the number of individuals in the population to be sampled from (with replacement) or the number of values an empirical distribution (e.g. a histogram). If each individual value in the sample is unique and should be weighted equally, set each weight to 1. If the sample distribution resembles a histogram, the weights should be equal to the number of observations for each unique value in the empirical distribution.
If dists == "constant", parameterization_types can only be "val", and dists_params must be a single numerical value.

A dataframe with samples of parameters for a probabilistic sensitivity analysis (PSA)

run_psa

#define parameter names
params <- c("normal_param", "lognorm_param", "truncnorm_param", "beta_param",
            "gamma_param", "dirichlet_param", "bootstrap_param")

#indicate parent distribution types for each parameter
dists <- c("normal", "log-normal", "truncated-normal", "beta", "gamma", "dirichlet", "bootstrap")

#indicate which type of parameterization is used for each parent distribution
parameterization_types <- c("mean, sd", "mean, sd", "mean, sd, ll, ul", "mean, sd", "mean, sd",
                          "value, mean_prop, sd", "value, weight")

#provide distribution parameters that fully define each parent distribution, and
#ensure that these distribution parameters match the form expected by each combination of dists
#and parameterization_types
dists_params <- list(c(1, 2), c(1, 3), c(1, 0.1, NA, 1), c(.5, .2), c(100, 1),
                   data.frame(value = c("level1", "level2", "level3"),
                              mean_prop = c(.1, .4, .5), sd = c(.05, .01, .1)),
                   data.frame(value = c(1, 2, 4, 6, 7, 8),
                              weight = c(1, 1, 1, 1, 1, 4)))

#generate 100 samples of parameter values to be used in a probabilistic sensitivity analysis
gen_psa_samp(params = params,
             dists = dists,
             parameterization_types = parameterization_types,
             dists_params = dists_params,
             nsamp = 100)