Hi !

References in the first paragraph of Chapter 5 aren't displaying correctly.

Thanks!

The chapter link reference is not displaying.

I am using the ipfp package to generate a synthetic population for 100 zones, a total of 523,930 people. My sample size is 24,000. I have 4 constraints: age (5 levels), sex (2 levels), race (4 levels) and employment status (3 levels)。

I have made sure that the totals across different constraints are consistent. I basically use the same code that's provided by the code to do the microsimulation.

weights_ga <- array(NA, dim=c(nrow(ind_ga),nrow(cons_ga)))

ga_ind_agg <- matrix(colSums(ga_ind_cat), nrow(cons_ga), ncol(cons_ga), byrow = T)

# Iterative proportional fitting (IPF) stage
library(ipfp) # load the ipfp package -may need install.packages("ipfp")
cons_ga <- apply(cons_ga, 2, as.numeric) # convert the constraints to 'numeric'

ga_ind_catt <- t(ga_ind_cat) # transpose the dummy variables for ipfp

x0 <- rep(1, nrow(ind_ga)) # set the initial weights

weights_ga_f <- apply(cons_ga, 1, function(x) ipfp(x, ga_ind_catt, x0))

Created on 2018-08-16 by the reprex
package (v0.2.0).

``
And my weights looks like this

What could be possibly going wrong? I have several 0s in the constraints, but there are not many of them. I tried to replace them to 0.0001, but still doesn't work.

build.R:
install.packages("rgeos", "mipfp", "rgdal", "gridExtra")
uncomment lines 4128 to 4130 in book.Rmd (install GREGWT)
missing figure (row 863) figures/msim-schema.png"
missing file (row 4045) cache-smsim-in-R.RData"
(row 5133) error in "R/CakeMapInts.R" - data.frame different lenghts of columns(ints_df)

The GREGWT example on pg. 112 isn't running.
data_in <- prepareData(cbind(age, sex), ind, census_area_id = F, breaks = c(3))
Error in [.data.frame(Tx, names(Tx)[i:b]) : undefined columns selected

build.R currently requires GREGWT package but the link to download the source no longer exists: https://www.dropbox.com/s/l3n0gzl7s4a2zpy/GREGWT_1.4.tar.gz?dl=0 (see https://gitlab.com/emunozh/mikrosim)

I have been trying to implement the IPF algorithm from SMiR's Chapter 7 on a dataset I created using the American Community Survey (ACS). I have been using almost the similar data structure as the CakeMap example, but I get the following error message:

Error in ipfp(x, ind_catt, x0, maxit = 100) :
BLAS/LAPACK routine 'DGEMV DGER DSBMV DS' gave error code -6

I have attached the R code and necessary files here. I would be extremely grateful if someone could help me troubleshoot the issue.

`
ind <- read.csv("ind.csv")
cons <- read.csv("cons.csv")

ind <- ind %>%
select(eap_eligible, vehicle, home_ownership, hh_income_cat, race)
cons <- cons[, -1]

con1 <- cons[1:10] # load the tenure/race constraint
con2 <- cons[11:12] # load the car/no car constraint
con3 <- cons[13:28] # income class

cat_labs <- names(cons) # category names, from correct from cons.R

create new homeownership/race variable

HR <- paste0(ind$home_ownership, ind$race)
unique(HR)

matrix for constraint 1 - tenure/race

m1 <- model.matrix(~HR-1)
head(cons)
head(m1)
m1_names <- c("Asian_Owner", "Black_Owner", "Indigenous_Owner", "Multiracial_Owner", "White_Owner",
"Asian_Renter", "Black_Renter", "Indigenous_Renter", "Multiracial_Renter", "White_Renter")
colnames(m1) <- names(m1_names)
head(m1)
summary(rowSums(m1))

matrix for con2 (car ownership)

ind$vehicle <- as.character(ind$vehicle)
m2 <- model.matrix(~ind$vehicle-1)
head(m2)
summary(m2)

matrix for con3 (income category)

ind$hh_income_cat<- as.character(ind$hh_income_cat)
m3 <- model.matrix(~ind$hh_income_cat-1)
head(m3)
names(cons)

ind_cat <- data.frame(cbind(m1, m2, m3))
rm(m1, m2, m3)

names(ind_cat) <- cat_labs
names(ind_cat)[1:10] <- m1_names
head(ind_cat)

Check constraint totals - should be true

sum(ind_cat[,1:ncol(con1)]) == nrow(ind) # is the number in each category correct?
sum(ind_cat[,ncol(con1)+1:ncol(con2)]) == nrow(ind)

weights <- array(NA, dim=c(nrow(ind),nrow(cons)))

ind_agg <- matrix(colSums(ind_cat), nrow(cons), ncol(cons), byrow = T)

Iterative proportional fitting (IPF) stage

library(ipfp) # load the ipfp package -may need install.packages("ipfp")

cons <- apply(cons, 2, as.numeric) # convert the constraints to 'numeric'
ind_catt <- t(ind_cat) # transpose the dummy variables for ipfp
x0 <- rep(1, nrow(ind)) # set the initial weights

weights <- apply(cons, 1, function(x) ipfp(x, ind_catt, x0, maxit = 100))`

cons.csv
ind.csv

This code fails here:

for(area in seq(dim(age)[1])){
    gregwt = GREGWT(data_in, area_code = area)
    fw <- gregwt$final_weights
    fweights <- c(fweights, fw)
    ## Estimate income
    sum.income <- sum(fw * ind$income)
    cap.income <- sum(fw * ind$income / sum(fw))
    Result[area,] <- c(area, sum.income, cap.income)
}

## Error in Tx_complete[i, ] : subscript out of bounds

Just not running the code is not a good solution! f02fb39

Please take a look @emunozh

New version of GREGWT is looking good.

There is a problem with the int_pp function (PP integerisation) due to the limitation of summary() by maxsum argument, whose default is 100 for factors.
Try int_pp(x=rep(1.3333,100000)) to reproduce the error.

My patch is:

 # Aggregate the result into a weight vector
   xsumm <- summary(as.factor(xs), maxsum = length(levels(as.factor(xs)))+1)

Warning: "pandoc-citeproc: reference rjava not found"

When knitting 13-smsim-for-abm, presumably missing a label in the bib.

File still knits, but thought you should know.

On page 6:
ind_agg0 <- t(apply(cons, 1, function(x) x^0 * ind_agg))
Returns this error:
Error in x^0 : non-numeric argument to binary operator