Title:	Variable Input Allocation Among Crops
Version:	1.0.1
Description:	Using a time-varying random parameters model developed in Koutchade et al., (2024) https://hal.science/hal-04318163, this package allows allocating variable input costs among crops produced by farmers based on panel data including information on input expenditure aggregated at the farm level and acreage shares. It also considers in fairly way the weighting data and can allow integrating time-varying and time-constant control variables.
License:	GPL (≥ 3)
Depends:	R (≥ 3.4.0)
Imports:	dplyr, future, future.apply, graphics, ks, LearnBayes, MASS, Matrix, matrixcalc, matrixStats, methods, plm, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.4.0), stats, utils
Suggests:	spelling, testthat (≥ 3.0.0)
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Biarch:	true
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.1
SystemRequirements:	GNU make
Language:	en-US
NeedsCompilation:	yes
Packaged:	2024-06-07 11:03:14 UTC; opkoutchade
Author:	Obafèmi Philippe Koutchade [aut, cre, cph], Fabienne Femenia [aut], Alain Carpentier [aut]
Maintainer:	Obafèmi Philippe Koutchade <obafemi-philippe.koutchade@inrae.fr>
Repository:	CRAN
Date/Publication:	2024-06-07 14:40:02 UTC

The 'winputall' package.

Description

Using a time-varying random parameters model, this package allows allocating variable input costs among crops produced by farmers based on panel data including information on input expenditure aggregated at the farm level and acreage shares. It also considers in fairly way the weighting data and can allow integrating time-varying and time-constant control variables.

Author(s)

Maintainer: Obafèmi Philippe Koutchade obafemi-philippe.koutchade@inrae.fr (ORCID) [copyright holder]

Authors:

• Fabienne Femenia fabienne.femenia@inrae.fr

• Alain Carpentier alain.carpentier@inrae.fr

References

Koutchade Obafèmi Philippe, Fabienne Femenia, Alain Carpentier (2024).Variable Input Allocation Among Crops: A Time-Varying Random Parameters Approach https://hal.science/hal-04318163

Example data

Description

An unbalanced panel data

Usage

my_winputall_data

Format

A data frame with 175 rows and 9 variables:

id

individual index

year

time index

tx

Total input uses

s_crop1

acreage of crop number 1

s_crop2

acreage of crop number 2

s_crop3

acreage of crop number 3

k

Capital

tempmoy

Temperature

precip

Annual precipitation

...

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

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_stdelist 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)

Random Parameters Transformation

Description

rp.model.fun is used to tranform the random parameters.

Usage

rp_model_fun(beta, opt)

Arguments

Value

the transformed beta