manf {fitdistcp} | R Documentation |
Blank function I use for setting up the man page information for the functions
Description
Blank function I use for setting up the man page information for the functions
Usage
manf(
dim,
vv,
ml_params,
nx,
nxx,
x,
xx,
t,
t1,
t2,
t3,
tt,
tt1,
tt2,
tt3,
tt2d,
tt3d,
t0,
t01,
t02,
t03,
t10,
t20,
t30,
n0,
n10,
n20,
p,
n,
y,
ics,
ta,
ta0,
muhat0,
v1,
v1hat,
v1h,
d1,
fd1,
v2,
v2hat,
v2h,
d2,
fd2,
v3,
v3hat,
v3h,
d3,
fd3,
v4,
v4hat,
v4h,
d4,
fd4,
v5,
v5hat,
v5h,
d5,
v6,
v6hat,
v6h,
d6,
minxi,
maxxi,
ximin,
ximax,
fdalpha,
kscale,
kloc,
kshape,
kdf,
kbeta,
alpha,
ymn,
slope,
mu,
sigma,
sigma1,
sigma2,
scale,
shape,
xi,
xi1,
xi2,
lambda,
log,
mm,
nn,
rr,
lddi,
lddi_k2,
lddi_k3,
lddi_k4,
lddd,
lddd_k2,
lddd_k3,
lddd_k4,
lambdad,
lambdad_cp,
lambdad_rhp,
lambdad_flat,
lambdad_rh_mle,
lambdad_rh_flat,
lambdad_jp,
lambdad_custom,
means,
waicscores,
logscores,
extramodels,
pdf,
predictordata,
nonnegslopesonly,
rnonnegslopesonly,
customprior,
prior,
params,
yy,
pp,
dlogpi,
debug,
centering,
aderivs
)
Arguments
dim |
number of parameters |
vv |
parameters |
ml_params |
parameters |
nx |
length of training data |
nxx |
length of training data |
x |
a vector of training data values |
xx |
a vector of training data values |
t |
a vector or matrix of predictors |
t1 |
a vector of predictors for the mean |
t2 |
a vector of predictors for the sd |
t3 |
a vector of predictors for the shape |
tt |
a vector of predictors |
tt1 |
a vector of predictors for the mean |
tt2 |
a vector of predictors for the sd |
tt3 |
a vector of predictors for the shape |
tt2d |
a matrix of predictors (nx by 2) |
tt3d |
a matrix of predictors (nx by 3) |
t0 |
a single value of the predictor (specify either |
t01 |
a single value of the predictor (specify either |
t02 |
a single value of the predictor (specify either |
t03 |
a single value of the predictor (specify either |
t10 |
a single value of the predictor for the mean (specify either |
t20 |
a single value of the predictor for the sd (specify either |
t30 |
a single value of the predictor for the shape (specify either |
n0 |
an index for the predictor (specify either |
n10 |
an index for the predictor for the mean (specify either |
n20 |
an index for the predictor for the sd (specify either |
p |
a vector of probabilities at which to generate predictive quantiles |
n |
number of random samples required |
y |
a vector of values at which to calculate the density and distribution functions |
ics |
initial conditions for the maximum likelihood search |
ta |
predictor residuals |
ta0 |
predictor residual at the point being predicted |
muhat0 |
muhat at the point being predicted |
v1 |
first parameter |
v1hat |
first parameter |
v1h |
first parameter |
d1 |
the delta used in the numerical derivatives with respect to the parameter |
fd1 |
the fractional delta used in the numerical derivatives with respect to the parameter |
v2 |
second parameter |
v2hat |
second parameter |
v2h |
second parameter |
d2 |
the delta used in the numerical derivatives with respect to the parameter |
fd2 |
the fractional delta used in the numerical derivatives with respect to the parameter |
v3 |
third parameter |
v3hat |
third parameter |
v3h |
third parameter |
d3 |
the delta used in the numerical derivatives with respect to the parameter |
fd3 |
the fractional delta used in the numerical derivatives with respect to the parameter |
v4 |
fourth parameter |
v4hat |
fourth parameter |
v4h |
fourth parameter |
d4 |
the delta used in the numerical derivatives with respect to the parameter |
fd4 |
the fractional delta used in the numerical derivatives with respect to the parameter |
v5 |
fifth parameter |
v5hat |
fifth parameter |
v5h |
fifth parameter |
d5 |
the delta used in the numerical derivatives with respect to the parameter |
v6 |
sixth parameter |
v6hat |
sixth parameter |
v6h |
sixth parameter |
d6 |
the delta used in the numerical derivatives with respect to the parameter |
minxi |
minimum value of shape parameter xi |
maxxi |
maximum value of shape parameter xi |
ximin |
minimum value of shape parameter xi |
ximax |
maximum value of shape parameter xi |
fdalpha |
the fractional delta used in the numerical derivatives with respect to probability, for calculating the pdf as a function of quantiles |
kscale |
the known scale parameter |
kloc |
the known location parameter |
kshape |
the known shape parameter |
kdf |
the known degrees of freedom parameter |
kbeta |
the known beta parameter |
alpha |
a vector of values of alpha (one minus probability) |
ymn |
the location parameter of the function of the predictor |
slope |
the slope of the function of the predictor |
mu |
the location parameter of the distribution |
sigma |
the sigma parameter of the distribution |
sigma1 |
first coefficient for the sigma parameter of the distribution |
sigma2 |
second coefficient for the sigma parameter of the distribution |
scale |
the scale parameter of the distribution |
shape |
the shape parameter of the distribution |
xi |
the shape parameter of the distribution |
xi1 |
first coefficient for the shape parameter of the distribution |
xi2 |
second coefficient for the shape parameter of the distribution |
lambda |
the lambda parameter of the distribution |
log |
logical for the density evaluation |
mm |
an index for which derivative to calculate |
nn |
an index for which derivative to calculate |
rr |
an index for which derivative to calculate |
lddi |
inverse observed information matrix |
lddi_k2 |
inverse observed information matrix, fixed shape parameter |
lddi_k3 |
inverse observed information matrix, fixed shape parameter |
lddi_k4 |
inverse observed information matrix, fixed shape parameter |
lddd |
third derivative of log-likelihood |
lddd_k2 |
third derivative of log-likelihood, fixed shape parameter |
lddd_k3 |
third derivative of log-likelihood, fixed shape parameter |
lddd_k4 |
third derivative of log-likelihood, fixed shape parameter |
lambdad |
derivative of the log prior |
lambdad_cp |
derivative of the log prior |
lambdad_rhp |
derivative of the log RHP prior |
lambdad_flat |
derivative of the log flat prior |
lambdad_rh_mle |
derivative of the log CRHP-MLE prior |
lambdad_rh_flat |
derivative of the log CRHP-FLAT prior |
lambdad_jp |
derivative of the log JP prior |
lambdad_custom |
custom value of the derivative of the log prior |
means |
logical that indicates whether to return analytical estimates for the distribution means (longer runtime) |
waicscores |
logical that indicates whether to return estimates for the waic1 and waic2 scores (longer runtime) |
logscores |
logical that indicates whether to return leave-one-out estimates estimates of the log-score (much longer runtime) |
extramodels |
logical that indicates whether to add three additional prediction models |
pdf |
logical that indicates whether to return density functions evaluated at quantiles specified by input probabilities |
predictordata |
logical that indicates whether to calculate and return predictordata |
nonnegslopesonly |
logical that indicates whether to disallow non-negative slopes |
rnonnegslopesonly |
logical that indicates whether to disallow non-negative slopes |
customprior |
a custom value for the slope of the log prior at the maxlik estimate |
prior |
logical indicating which prior to use |
params |
model parameters for calculating logf |
yy |
vector of samples |
pp |
vector of probabilities |
dlogpi |
gradient of the log prior |
debug |
debug flag |
centering |
indicates whether the routine should center the data or not |
aderivs |
logical for whether to use analytic derivatives (instead of numerical) |
Value
No return value