##### Example 1: User has Existing Sample Weights #####
# Attach sample data and variable names:
data(api)
# In this example, we will estimate a model using
# the percentages of students who receive subsidized
# lunch and an indicator for whether schooling is
# year-round to predict California public schools'
# academic performance index scores:
z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey",
weights=~pw, data = apistrat)
summary(z.out1)
# Set explanatory variables to their default (mean/mode) values, and set
# a high (80th percentile) and low (20th percentile) value for "meals,"
# the percentage of students who receive subsidized meals:
x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2))
x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8))
# Generate first differences for the effect of high versus low "meals"
# on academic performance:
s.out1 <- sim(z.out1, x=x.high, x1=x.low)
summary(s.out1)
# Generate a second set of fitted values and a plot:
plot(s.out1)
#### Example 2: User has Details about Complex Survey Design ####
#### (but not sample weights) ####
# Suppose that the survey house that provided
# the dataset excluded probability weights
# but made other details about the survey
# design available. We can still estimate
# a model without probability weights that takes
# instead variables that identify each the stratum
# and/or cluster from which each observation was
# selected and the size of the finite sample from
# which each observation was selected.
z.out2 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey",
strata=~stype, fpc=~fpc, data = apistrat)
summary(z.out2)
# Note that these results are identical to the results obtained
# when pre-existing sampling weights were used. When sampling
# weights are omitted, Zelig estimates them automatically for
# "normal.survey" models based on the user-defined description
# of sampling designs. If no description is present, the default
# assumption is equal probability sampling.
#
# setx() and sim() can then be run on z.out2 in the same fashion
# described in Example 1.
##### Example 3: User has Replicate Weights #####
# Load data for a model using the number of out-of-hospital
# cardiac arrests to predict the number of patients who arrive
# alive in hospitals:
data(scd)
# Create four Balanced Repeated Replicate (BRR) weights:
BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
# Estimate the model using Zelig:
z.out3 <- zelig(formula=alive ~ arrests , model = "normal.survey",
repweights=BRRrep, type="BRR", data=scd, na.action=NULL)
summary(z.out3)
# Set the explanatory variable at its minimum and maximum
x.min <- setx(z.out3, arrests = min(scd$alive))
x.max <- setx(z.out3, arrests = max(scd$alive))
# Generate first differences for the effect of the minimum
# versus the maximum number of cardiac arrests on the number
# of people who arrive alive:
s.out3 <- sim(z.out3, x=x.max, x1=x.min)
summary(s.out3)
# Generate a second set of fitted values and a plot:
plot(s.out3)
#### The user should also refer to the normal model demo, since ####
#### normal.survey models can take many of the same options as ####
#### normal models. ####
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