setinitvalues {mlm4omics} | R Documentation

## The function to set initial values for parameters: setinitvalues().

### Description

Generate initial values for parameters

### Usage

    setinitvalues(npred, np, npred_miss, npred_sub, nmiss, nsid,
        censor_lim_upp = 0.008, ita_a = 1, ita_b = 1/10, g_mu = 0,
        g_sig = 1, alpha_mu_u = 0, alpha_mu_s = 1, alpha_theta_a = 1,
        alpha_theta_b = 1/10, beta2_theta_a = 1, beta2_theta_b = 1/10)

### Arguments

`npred` | number of predictors for the regression model
`np` | number of protein/metabolite units comprised of the response values (i.e. which represents peptides' ion-intensities used to construct protein/metabolite's abundance)
`npred_miss` | number of predictors for missingness
`npred_sub` | number of predictors for the second level units such as subjects
`nmiss` | number of observations with missing responses values
`nsid` | number of second level units i.e. subjects
`censor_lim_upp` | upper-limit of censored value of responses. The default value is 0.001 according to an experiment device. User can change it according to the data.
`ita_a` | shape parameter for gamma distributed prior-ita (std of response value). The default is set to 1.
`ita_b` | rate parameter for gamma distributed prior-ita. The default is set to 1/10. The default values of shape and rate parameters provide a reasonable wide range of initial value for ita. Users can change it accordingly.
`g_mu` | mean of normal distributed location parameter g for re-parameterising U (regression coefficient of model in the completed data). The default value is set to 0 for the mean of standard normal distribution.
`g_sig` | std of normal distributed location parameter g. The default is set to 1 for the std of normal distribution.
`alpha_mu_u` | mean of normal distributed location parameter alpha_mu for re-parameterising alpha (regression coefficient of logistic regression model for missing prob). The default is set to 0.
`alpha_mu_s` | std of normal distributed location parameter alpha_mu
`alpha_theta_a` | shape parameter of gamma-distributed dispersion parameter alpha_theta for re-parameterising alpha. Default value is set to 1 as a natural starting value.
`alpha_theta_b` | rate parameter of gamma-distributed alpha_theta. Default value uses 1/10 as for ita_b. Both default values of shape(_a) and rate(_b) of alpha_theta can be changed to give a wider range (_b=1/10) or a narrower range (_b=0.5).
`beta2_theta_a` | shape parameter of gamma-distributed beta2_theta for re-parameterizing beta2 (regression coefficient for second level units,i.e. subject). Default value uses 1.
`beta2_theta_b` | rate parameter of gamma distributed dispersion parameter beta2_theta. Default value used 1/10, same as for ita_b.

### Value

pVAR precision matrix for predictors in completed data model

U_latent standardized multinormal distributed latern variable to re-parameterise regression coefficient U.

g location parameter to re-parameterise U.

alpha_mu mean value for alpha(regression coefficient of model for missing probability).

alpha_latent standardized normal distributed latent variable to re-parameterize alpha.

beta2_latent standardized multivariate normal distributed latent variable to re-parameterising beta2.

beta2_mu mean of the multivariate normal distributed beta2

y_m_latent standardized normal distributed latent variable to re-parameterise response variable.

### Examples

    testexmp <- setinitvalues(npred=2,np=3,npred_miss=3,npred_sub=2,nmiss=10,
    nsid=30)

[Package mlm4omics version 1.2.0 Index]