##### 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 = "gamma.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 = "gamma.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
# "gamma.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 #####
# Suppose that the survey house that published
# these data withheld details about the survey
# design and instead published replication weights
# For the purpose of illustration, create a set of
# jk1 replicate weights
jk1reps <- jk1weights(psu=apistrat$dnum)
# Estimate the model regressing api00 on the "meals"
# "yr.rnd" variables.
z.out3 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey",
data = apistrat, repweights=jk1reps$weights,
type="JK1")
summary(z.out3)
# Set the explanatory variable "meals" at high and low values
x.low <- setx(z.out3, meals= quantile(apistrat$meals, 0.2))
x.high <- setx(z.out3, meals= quantile(apistrat$meals, 0.8))
# Generate first differences for the effect of the high
# versus low concentrations of poverty on school performance
s.out3 <- sim(z.out3, x=x.high, x1=x.low)
summary(s.out3)
# Generate a second set of fitted values and a plot:
plot(s.out3)
#### The user should also refer to the gamma model demo, since ####
#### gamma.survey models can take many of the same options as ####
#### gamma models. ####
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