mrp_model: Multilevel Regression with Poststratification

In alexdulin/MCmrp: Morning Consult's tools for Multilevel Regression and Poststratification

Description

Implements a multilevel regression model with postratification to create estimates of public opinion at differing strata using census and survey data.

Usage

mrp_model(formula, data, mrp.data, method = c("glmer", "bglmer"),
  family = binomial(link = "logit"), ...)

Arguments

formula	A formula specifying the regression model. The formula must be in a valid format for glmer.
data	A data frame containing the survey data. All variables in formula must be columns in the dataset.
mrp.data	A data frame created by mrp_table of class mrp.table with marginal distributions of individual level characteristics. Any variables (column names) in mrp.data that are also in formula (either as random or fixed effects) will be used in the poststratification stage to create weighted cell predictions.
method	character: The regression function to use. Options are 'glmer' for the glmer() function or 'bglmer' for the bglmer function from the blme package.
family	a GLM family, see glm and family.
...	Other options passed to glmer or bglmer.

Details

Model Specifications:

• Dependent variable: The dependent variable needs to be a binary choice that comes from the survey data. The ideal variable would be one that is known to have high variance from state to state (or whatever geographic sub-level is desired), such as gay marriage approval.

• Fixed effects (regional predictors): These are the variables that occur first in the model, and comprise known values of external state-level data. The variables included as the fixed effects need to have sufficient variance across states, need to be influential (either statistically or theoretically) to the dependent variable, and need to be available for all states and DC.

  (Note: these factors are what allow MRP to create estimates for sub-geographic regions. To create cell predictions for congressional districts, for example, one would use district-level data rather than state-level. Because these factors are the principal contributors to the sub-region estimates, choosing the correct regional indicators is extremely important to forming a valid MRP model.)

• Random effects (individual-level variables): The random effect variables comprise individual level demographic information about respondents in the survey. These are characteristics such as age, race, gender, etc. Any variables that are to be used in poststratification need to be included in the model as random effects.

  Additional random effects can be included that are not used in poststratification. These can be factors such as interactions between independent individual level variables, state-level variables, etc.

• Family: Generally, a binomial-logit model will suffice, but in applications where various marginal distributions in the data have few observations (for example, black men aged 18-25 with a Post graduate degree and income less that 40k from the southwest) a quasi-Poisson distribution may be appropriate (see Gelman and Ghitza 2013, footnote 3).

Value

A list object of class mrp.model containing the fitted model from glmer; a three-column data frame with the grouping variable index and the weighted and unweighted cell predictions; and the function call.

See Also

mrp_table, mrp_merge, glmer

Examples

mrp_load_libs()

### Needed to get 2012 vote share
library(RCurl)
library(XML)
library(stringr)

### Define model for poststratification
model <- list(~state, ~age4, ~sex, ~race4, ~educ3)
### Load in merged trend dataset, recode to match CPS variables
data <- tsdat[tsdat$wts > 0 & !is.na(tsdat[, 'nr1']), ]
## Take a random sample of the data
data <- sample_frac(data, size = .2)
data <- copy_lev(data, tsdat)
data.format()

## Recodes
data$state <- recode(data$demState, paste0(paste(1:51, 1:51, sep = "=", collapse = ";"), ";else=NA"))
levels(data$state) <- c(state.abb[1:8], "DC", state.abb[9:50])
data$sex <- data$demGender
data$age4 <- data$age
data$race4 <- data$demRace4
data$educ3 <- data$xeduc3
data$grp <- mrp_grp(data, model) # Make variable for later merging

## Make mariginal distribution table using 2012 CPS data
cps.tbl <- mrp_table(model = model)

## 2012 vote share by state, will be used as fixed-effect
presdat <- readHTMLTable(getURL("http://www.archives.gov/federal-register/electoral-college/2012/popular-vote.html"),
                         stringsAsFactors = FALSE)[[2]][1:51, 1:3]
names(presdat) <- c('state', 'democrat', 'republican')
for (i in 2:3)
  presdat[, i] <- as.numeric(presdat[, i])
presdat[, 2:3] <- presdat[, 2:3] / rowSums(presdat[, 2:3])
presdat$state <- gsub("NY**", "NY", presdat$state, fixed = TRUE)

## Add vote share to main data and population table
data$st.repvote <- presdat$republican[data$state]
data$z.st.repvote <- rescale(data$st.repvote)  # needs to be rescaled to play nice with glmer

presdat$stname <- presdat$state
presdat <- presdat[, c('stname', 'republican')]
colnames(presdat)[2] <- 'st.repvote'
presdat[, 'z.st.repvote'] <- rescale(presdat[, 2])

cps.tbl <- merge(cps.tbl, presdat, by = 'stname', all.x = TRUE, sort = FALSE) # see merge.mrp.table

## Run the MRP regression
mrp.fit1 <- mrp_model(y ~ z.st.repvote + (1 | age4) + (1 | sex) + (1 | race4) + (1 | educ3),
                      data = data, mrp.data = cps.tbl)

### Extract cell predictions and merge with data
res <- mrp_merge(data, mrp.fit1)

alexdulin/MCmrp documentation built on May 11, 2019, 11:29 p.m.