getJacobian_wrap | R Documentation |
Calculating the Jacobian for a given set of non-linear parameters and mean utilities.
getJacobian_wrap(blp_data, par_theta2, printLevel = 1)
blp_data |
data object created by the function |
par_theta2 |
matrix with column and rownames providing the evaluation point (see details), |
printLevel |
level of output information (default = 1) |
NA's in par_theta2
entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero.
If only unobserved heterogeneity is used (no demographics), the column name of par_theta2
must be "unobs_sd".
With demographics the colnames must match the names of provided demographics (as in demographic_draws
) and "unobs_sd".
Row names of par_theta2
must match random coefficients as specified in model
. Constants must be named "(Intercept)".
Returns a matrix with the jacobian (products in rows, parameters in columns).
K<-2 #number of random coefficients data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20, Xlin = c("price", "x1", "x2", "x3", "x4", "x5"), Xexo = c("x1", "x2", "x3", "x4", "x5"), Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10), true.parameters = list(Xlin.true.except.price = rep(0.2,5), Xlin.true.price = -0.2, Xrandom.true = rep(2,K), instrument.effects = rep(2,10), instrument.Xexo.effects = rep(1,5)), price.endogeneity = list( mean.xi = -2, mean.eita = 0, cov = cbind( c(1,0.7), c(0.7,1))), printlevel = 0, seed = 234234 ) model <- as.formula("shares ~ price + x1 + x2 + x3 + x4 + x5 | x1 + x2 + x3 + x4 + x5 | 0+ x1 + x2 | iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" ) blp_data <- BLP_data(model = model, market_identifier="cdid", product_id = "prod_id", productData = data, integration_method = "MLHS" , integration_accuracy = 40, integration_seed = 1) theta_guesses <- matrix(c(0.5,2), nrow=2) rownames(theta_guesses) <- c("x1","x2") colnames(theta_guesses) <- "unobs_sd" jacobian <- getJacobian_wrap(blp_data=blp_data, par_theta2 = theta_guesses, printLevel = 2) head(jacobian)
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