update_BLP_data | R Documentation |
Updates the set of linear, exogenous, random coefficient, share or mean utility variable in the data object.
update_BLP_data(data_update, blp_data)
data_update |
data.frame with variables to update (must contain the market_identifier and product_identifier variables as in |
blp_data |
data object created by the function |
Returns an object of class blp_data
.
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) new_data <- data.frame(price = seq(1,10,length.out=500), x1 = seq(2,10,length.out=500), cdid = sort(rep(1:25,20)), prod_id = rep(1:20,25) ) blp_data_example_updated <-update_BLP_data(blp_data = blp_data, data_update = new_data)
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