print.rpinpall {winputall} | R Documentation |
Fit Input Allocation Random Parameters Model
Description
Designed to fit a random parameters input allocation model proposed in Koutchade et al., (2024) https://hal.science/hal-04318163. It provides crops input cost for each individual at each time and can account for Weighted Panel Data.
Usage
## S3 method for class 'rpinpall'
print(x, error = FALSE, ...)
## S3 method for class 'rpinpall'
summary(object, ...)
## S3 method for class 'rpinpall'
plot(x, ...)
rpinpallEst(
data,
id_time,
total_input,
crop_acreage,
crop_indvar = NULL,
crop_rp_indvar = NULL,
weight = NULL,
distrib_method = c("lognormal", "normal", "censored-normal"),
sim_method = c("map_imh", "mhrw", "marg_imh", "mhrw_imh", "nuts", "variat",
"lapl_approx"),
calib_method = c("cmode", "cmean", "rscd", "estim-sim"),
saem_control = list(),
par_init = list()
)
Arguments
x |
An object produced by the function |
error |
logical. If TRUE, residuals are considered in variable input prediction |
... |
Other arguments |
object |
An object produced by the function |
data |
name of the data frame or matrix containing all the variables included in the model. |
id_time |
first (individual) and second (time) level variables allowing characterizing panel data. |
total_input |
variable (name) containing the total input used at farm level per ha to be allocated to the different crops. |
crop_acreage |
list of variables containing the acreage of the different crops. |
crop_indvar |
optional list of vector of (time-varying) variables specific to each crop used to control for observed (individual and/or temporal) characteristics in the estimation process. Default=NULL. |
crop_rp_indvar |
optional list of vector of (time-constant) variables specific to each crop used to control for observed time-constant characteristics in the estimation process. Default=NULL. |
weight |
optional variable containing weights of individual sample farms. Default=NULL (equal weight is given to each farm). Default=NULL. |
distrib_method |
assumption on the distribution of input use per crop (x_kit): "normal", "lognormal" or "censored-normal". Default="lognormal". |
sim_method |
method used to draw the random parameters in the simulation step of the SAEM algorithm in the estimation process: "map_imh" (independant Metropolis Hasting with Laplace approximation as proposal distribution), "marg_imh" (independant Metropolis Hasting with marginal distribution of random pararameters as proposal distribution), "mhrw" (Metropolis Hasting Random Walk), "mhrw_imh" (combined "imh" and "mhrw"), "nuts","variat" and"lapl_approx". Default= "map_imh". |
calib_method |
method used: "cmode" (conditional mode), "cmean" (conditional mean). Default="cmode". |
saem_control |
list of options for the SAEM algorithm. See 'Details |
par_init |
list of some parameters' initialization. |
Details
An SAEM algorithm is used to perform the estimation of input uses per crop. Different options can be specified by the user for this algorithm in the saem_control argument. The saem_control argument is list that can supply any of the following component.
-
nb_burn_saem
: Number of iterations of the burn-in phase where individual parameters are sampled from their conditional distribution using sim_method and the initial values for model parameters without update these parameters. Default=20. -
nb_SA
: Number of iterations in the exploration phase where algorithm explore parameters space without memory. The parameter that controls the convergence of the algorithm is set to 1. Default=200. -
nb_smooth
: Number of iterations in the smoothing phase.Default=200 and the parameter that controls the convergence of the algorithm is set to 0.85 by default. -
nb_RS
: Number of iterations where tempering approach is used -
tol
: Tolerance value for the convergence. Default 1.10-3. -
estim_rdraw
: Number of random draws using in the estimation process. Default=100 -
calib_rdraw
: Number of random draws using in the calibration process. Default=100 -
stde_rdraw
: Number of random draws using for computation of estimation standard errors. Default=100 -
p_SA
: Parameter determining step sizes in the Stochastic Approximation (SA) step. Must be comprise between 0 and 1. Default=0.85 -
doParallels
: Logical.If TRUE a parallel processing is used when more than 2 cores are available. Default=FALSE -
doTempering
: Logical. If TRUE the tempering approach proposed by (Allassonnière and Chevallier, 2021) is used to avoid convergence to local maxima. Default=TRUE -
doDiagEps
: = "2", -
showProgress
: Logical. If TRUE the evolution of the estimation process is displayed graphically at the bottom of the screen. Default=TRUE -
showIterConvLL
: Logical. If TRUE iteration number and convergence value are displayed during the estimation process. Default=FALSE
Value
Distribution of estimated crop input uses.
This function returns a list with the following components:
-
xit_pred
: matrix of predicted crop input used per ha. -
xit_pred_with_error
: matrix of predicted crop input used per ha. -
yit_predict
: vector of predicted totat input used. -
est_pop
list of results of estimation: estimated parameters. -
est_stde
list of parameters standard errors. -
call
: a copy of the function call. -
opt
: a list of saem algorithm control parameters. -
conv_ind_cll
: vector of convergence indicator. -
data_list
: a list of individual data used for estimation.
Functions
-
print(rpinpall)
: Displays the distribution of estimated crop input uses accounting for error by default -
summary(rpinpall)
: Displays a summary of estimated parameters -
plot(rpinpall)
: Plot the "global" convergence indicator
References
Koutchade, O. P., Carpentier A. and Femenia F. (2024).
Examples
data(my_winputall_data)
mydata <- my_winputall_data
fit <- rpinpallEst(data = my_winputall_data,
id_time = c("id","year"),
total_input = "tx",
crop_acreage = c("s_crop1","s_crop2","s_crop3"),
distrib_method = "lognormal",
sim_method = "map_imh",
calib_method = "cmode",
saem_control = list(nb_SA = 10, nb_smooth = 10, estim_rdraw = 10))
print(fit)
plot(fit)
summary(fit)
head(fit$xit_pred)