Package 'catregs'

Compares two marginal effects (MEMs or AMEs). Estimate of uncertainty is from a simulated draw from a normal distribution.

Description

Given two marginal effects (AMEs or MEMs), as estimated via the margins package or via first.diff.fitted, this function simulates draws from the distribution of MEs defined by the estimates and their standard error, and computes the overlap in the two distributions. The p-value refers to proportion of times the two draws overlapped.

Usage

compare.margins(margins,margins.ses,seed=1234,rounded=3,nsim=10000)

Arguments

margins	The two marginal effects that you want to compare.
margins.ses	The standard errors for the marginal effects you want to compare.
seed	Random number seed so that results are reproducible.
rounded	The number of decimal places to round the output. The default is 3.
nsim	The number of simulated AMEs to draw from each distribution. The default is 10,000.

Value

differnce	The observed difference in the two AMEs.
p.value	The p-value associated with the difference. This is the proportion of the simulated sample when the MEs overlapped.

Author(s)

David Melamed

Examples

data("essUK")
m1 <- glm(safe ~ religious + minority*female + age,data=essUK,family="binomial")
des<-margins.des(m1,expand.grid(minority=c(0,1),female=c(0,1)))
des
ma1<-suppressWarnings(as.data.frame(marginaleffects::avg_slopes(m1,
variables="female",newdata=data.frame(minority=0,religious=3.6024,age=53.146))))
ma2<-as.data.frame(marginaleffects::avg_slopes(m1,variables="female",
newdata=data.frame(minority=1,religious=3.6024,age=53.146)))
cames <- rbind(ma2,ma1)
compare.margins(margins=cames$estimate,margins.ses=cames$std.error)

---

Fits four different count models and compares them.

Description

Given a Poisson model object, count.fit fits Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models to the data. It reports results of Vuong tests between the zero-inflated and non-zer-inflated models, summarizes the information criteria of the four models, summarizes the model output of the four models, creates a ggplot object of coefficient plots for each model, and creates a ggplot object of model residuals.

Usage

count.fit(m1,y.range,rounded=3,use.color="yes")

Arguments

m1	A Poisson regression model, as estimated via the glm function.
y.range	The observed response range for the count outcome. For example, if the observed range is 0 to 18, this would be 0:18
rounded	The number of decimal places to round the output. The default is 3.
use.color	Whether to use color in the ggplot objects. Default is "yes"

Value

ic	A data.frame summarizing the information criteria for the four models. Bayesian and Akaike's informaiton criteria are included.
models	A summary of the model estimates, including coefficients and standard errors.
pic	A ggplot object illustrating model residuals for each type of model.
models.pic	A ggplot object of coefficient plots from each type of model.

Author(s)

David Melamed

Examples

data("LF06art")
p1 <- glm(art ~ fem + mar + kid5 + phd + ment , family = "poisson", data = LF06art)
table(LF06art$art)
fit<-count.fit(p1,0:19)
names(fit)

---

Computes diagnostics for generalized linear models.

Description

Given a glm object, diagn returns case-level diagnostics. For logistic, probit, Poisson, and negative binomial models, it returns Pearson residuals, standardized Pearson residuals, the diagonal of the hat matrix, delta-beta (Cook's D), and deviance residuals. For zero-inflated and hurdle models, it returns the Pearson residual and the observation number.

Usage

diagn(model)

Arguments

model

A model object. The model should be regression model for limited dependent variables, such as a logistic regression.

Value

out

The output is a dataframe of diagnostic statistics. For logit, probit, Poisson, and negative binomial models, the output includes the Pearson residual (pearsonres), the diagonal of the Hat matrix (h), the standardized Pearson residual (stdpres), the delta-beta statistic (deltabeta), the observation number (obs), and the deviance residual (devres). For zero-inflated and hurdle models, the output includes the Pearson residual (pearsonres), and the observation number (obs).

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age + race2 + race3 +
race4 + income + ed1 + ed2 + ed3 + ed4,
family="binomial",data=Mize19AH)
head(diagn(m1))

---

A subset of data from the European Social Survey

Description

These are data from the European Social Survey used to illustrate mixed effects, multilevel, or hierarchical regression models.

Usage

data("ess")

Format

A data frame with 49519 observations on the following 37 variables.

country

a character vector

can.trust.people

a numeric vector

people.try.fair

a numeric vector

say.in.govt

a factor with levels Not at all Very little Some A lot A great deal

trust.legal.sys

a numeric vector

trust.police

a numeric vector

vote

a factor with levels Yes No Not eligible to vote

conservative

a numeric vector

life.satisfaction

a numeric vector

immigration.good.economy

a numeric vector

happy

a numeric vector

important.matters.people

a character vector

walk.alone.dark

a factor with levels Very unsafe Unsafe Safe Very safe

religious

a numeric vector

ethnic.minority

a character vector

num.children

a numeric vector

ideal.age.parent

a numeric vector

household.size

a numeric vector

gender

a character vector

age

a numeric vector

marital

a character vector

education

a numeric vector

employment

a character vector

income.decile

a numeric vector

father.education

a character vector

father.education.num

a numeric vector

everyone.job.wanted

a numeric vector

income.fairness

a factor with levels Low, extremely unfair Low, very unfair Low, somewhat unfair Low, slightly unfair Fair High, slightly unfair High, somewhat unfair High, very unfair High, extremely unfair

under.over.paid

a factor with levels Underpaid Right amount Overpaid

income.fairness.num

a numeric vector

wealth.diff.fair

a factor with levels Small, extremely unfair Small, very unfair Small, somewhat unfair Small, slightly unfair Fair Large, slightly unfair Large, somewhat unfair Large, very unfair Large, extremely unfair

wealth.differences

a factor with levels Too little Just right Too much

gdp

a numeric vector

urban.population

a numeric vector

unemployment

a numeric vector

alcolhol.consumption

a numeric vector

suicide.rate

a numeric vector

Source

European Social Survey.

Examples

data(ess)
head(ess)

---

A subset of data from the European Social Survey

Description

These are data from respondents in the United Kingdom from the European Social Survey. They are used to illustrate regression models for limited dependent variables.

Usage

data("essUK")

Format

A data frame with 2204 observations on the following 45 variables.

country

a character vector

can.trust.people

a numeric vector

people.try.fair

a numeric vector

say.in.govt

a factor with levels Not at all Very little Some A lot A great deal

trust.legal.sys

a numeric vector

trust.police

a numeric vector

vote

a factor with levels Yes No Not eligible to vote

conservative

a numeric vector

life.satisfaction

a numeric vector

immigration.good.economy

a numeric vector

happy

a numeric vector

important.matters.people

a character vector

walk.alone.dark

a factor with levels Very unsafe Unsafe Safe Very safe

religious

a numeric vector

ethnic.minority

a character vector

num.children

a numeric vector

ideal.age.parent

a numeric vector

household.size

a numeric vector

gender

a character vector

age

a numeric vector

marital

a character vector

education

a numeric vector

employment

a character vector

income.decile

a numeric vector

father.education

a character vector

father.education.num

a numeric vector

everyone.job.wanted

a numeric vector

income.fairness

a factor with levels Low, extremely unfair Low, very unfair Low, somewhat unfair Low, slightly unfair Fair High, slightly unfair High, somewhat unfair High, very unfair High, extremely unfair

under.over.paid

a factor with levels Underpaid Right amount Overpaid

income.fairness.num

a numeric vector

wealth.diff.fair

a factor with levels Small, extremely unfair Small, very unfair Small, somewhat unfair Small, slightly unfair Fair Large, slightly unfair Large, somewhat unfair Large, very unfair Large, extremely unfair

wealth.differences

a factor with levels Too little Just right Too much

gdp

a numeric vector

urban.population

a numeric vector

unemployment

a numeric vector

alcolhol.consumption

a numeric vector

suicide.rate

a numeric vector

safe

a numeric vector

minority

a numeric vector

female

a numeric vector

divorced

a numeric vector

married

a numeric vector

widow

a numeric vector

highinc

a numeric vector

age2

a numeric vector

Source

European Social Survey.

Examples

data(essUK)
head(essUK)

---

Computes the first difference in fitted values, or a series of first differences. Inference in supported via the delta method or bootstrapping.

Description

first.diff.fitted computes first differences between fitted values from a regression model.

Supported models include OLS regression via lm, logistic regression via glm, Poisson regression via glm, negative binomial regression via MASS:glm.nb, ordinal logistic regression via MASS::polr, partial proportional odds models via vgam::vglm, multinomial logistic regression via nnet::multinom, zero-inflated Poisson or negative binomial regression via pscl::zeroinfl, hurdle Poisson or negative binomial regression via pscl::hurdle, mixed effects logistic regression via lme4/lmerTest::glmer, mixed effects Poisson regression via lme4/lmerTest::glmer, mixed effects negative binomial regression via lme4/lmerTest::glmer.nb, and mixed effects ordinal logistic regression via ordinal::clmm.

Usage

first.diff.fitted(mod,design.matrix,compare,alpha=.05,rounded=3,
bootstrap="no",num.sample=1000,prop.sample=.9,data,seed=1234,cum.probs="no")

Arguments

mod	A model object. The model should be regression model for limited dependent variables, such as a logistic regression.
design.matrix	Design matrix of values for the independent variables in the regression model.
compare	Pairs of rows in the design matrix to use for computing the fitted values. The first difference between the fitted values is then computed. For example, compare=c(4,2) means to compute the difference in the fitted values between predictions for row 4 of the design matrix and row 2 of the design matrix. If more than two rows are provided, the function uses them two at a time and computes multiple first differences.
alpha	The alpha value for confidence intervals. Default is .05.
rounded	The number of decimal places to round the output. The default is 3.
bootstrap	By default, inference is based on the Delta Method, as implemented in the marginaleffects package. Alternatively, inference can be based upon a bootstrapped sampling distirbution. To do so, change this to "yes." Note that bootstrapping is only supported for one first difference at a time.
num.sample	num.sample is the number samples drawn to compute the sampling distibution when using bootstrapping. Default is 1,000
prop.sample	prop.sample is the proportion of the original sample (with replacement) to include in the sampling distibution samples when using bootstrapping. Default is .9
data	For nonparametric inference, provide the data used in the original model statement.
seed	For models using bootstrapped inference. The seed ensures reproducible results across runs. Default is 1234, but may be changed.
cum.probs	For ordinal logistic regression models, including mixed effects models, do you want the first differences to be based on probabilities of the response categories or cumulative probabilities of the response categories. The default is cum.probs=="no" corresponding to non-cumulative probabilities. Change cum.probs to "yes" for cumulative probabilities.

Value

out

If using parametric inference (delta method): output is a dataframe including the first fitted value ("fitted1"), the second fitted value ("fitted2"), the difference in fitted values ("first.diff"), the standard error ("std.error"), the lower limit ("ll"), and upper limit ("ul") of the confidence interval. Of course, ll and ul are based on the alpha level. If using nonparametric inference (bootstrapping): output is a list of objects. obs.diff is the observed difference in the response or fitted values. boot.dist is the sorted bootstrapped distribution of differences in the samples. mean.boot.dist is the average of the differences in the responses or fitted values. sd.boot.dist is the standard deviation of the sampling distribution. ci.95 is the Lower and Upper limits of the confidence interval; despite it's name, the confidence interval is based upon the alpha level. model.class is just the class of the model that was used to generate the fitted values.

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age + race2 +
race3 + race4 + income + ed1 + ed2 + ed3 +
ed4,family="binomial",data=Mize19AH)
des2<-margins.des(m1,expand.grid(woman=c(0,1),parrole=c(0,1)))
des2
first.diff.fitted(m1,des2,compare=c(4,2))
# Pr(Drink | Mothers) - Pr(Drink | Childless Women)

first.diff.fitted(m1,des2,compare=c(3,1))
# Pr(Drink | Fathers) - Pr(Drink | Childless Men)

---

Data from the 2016 General Social Survey.

Description

Limited date from the 2016 General Social Survey on respondent and paternal class and occupational classifications.

Usage

data("gss2016")

Format

A data frame with 12498 observations on the following 13 variables.

pclass

a factor with levels Unskilled Manual Skilled Manual Self-Employed Non-Manual/Service Professional, Lower Professional, Higher

sclass

a factor with levels Unskilled Manual Skilled Manual Self-Employed Non-Manual/Service Professional, Lower Professional, Higher

educ

a numeric vector

race

a character vector

id

a numeric vector

occ2

a character vector

occ

a numeric vector

unskmanual

a numeric vector

skmanual

a numeric vector

selfemp

a numeric vector

service

a numeric vector

proflow

a numeric vector

profhigh

a numeric vector

Source

The General Social Survey.

Examples

data(gss2016)
head(gss2016)

---

Data to replicate Long and Freese's (2006) count models (pp354-414)

Description

For replication purposes between Stata and R. Long and Freese (2006) analyze these data to illustrate regression models for count dependent variables.

Usage

data("LF06art")

Format

A data frame with 915 observations on the following 6 variables.

art

count response

fem

dummy for sex

mar

dummy for married

kid5

number of children under five

phd

a numeric vector

ment

a numeric vector

Source

Long, Scott J. and Jeremy Freese. 2006. "Regression Models for Categorical Dependent Variables Using Stata." Austin, TX: Stata Press

Examples

data(LF06art)
head(LF06art)

---

Travel time example data for alternative-specific outcomes.

Description

Example data, also used in Long and Freese (2006), to illustrate conditional or fixed effects logistic regression. Also refered to as alternative-specific outcomes.

Usage

data("LF06travel")

Format

A data frame with 456 observations on the following 13 variables.

id

a numeric vector denoting nested units (individuals) or strata

mode

a numeric vector denoting mode of transit

train

a dummy variable for selecting the train

bus

a dummy variable for selecting the bus

car

a dummy variable for selecting a car

time

a numeric vector denoting transit time

invc

a numeric vector denoting invertng cost

choice

a numeric vector denoting the choice of travel, i.e. the dependent variable

ttme

a numeric vector

invt

a numeric vector

gc

a numeric vector

hinc

a numeric vector

psize

a numeric vector

Source

Long, Scott J. and Jeremy Freese. 2006. "Regression Models for Categorical Dependent Variables Using Stata." Austin, TX: Stata Press

Examples

data(LF06travel)
head(LF06travel)

---

Transform glm and mixed model objects into model summaries that include coefficients, standard errors, exponentiated coefficients, confidence intervals and percent change.

Description

Given a glm model object or a mixed model model object, the function computes and returns: coefficients, standard errors, z-scores, confidence intervals, p-values, exponentiated coefficients, confidence intervals for exponentiated coefficients, and percent change.

Supported models include logistic regression via the glm function, ordinal regression via mass::polr, multinomial regression via nnet:multinom, Poisson regression via the glm function, negative binomial regression via mass::glm.nb, mixed effects models for continuous outcomes with serial correlation via nlme::lme, mixed effects logistic and poisson regression via lme4::glmer, mixed effects negative binomial regression via lme4::glmer.nb, and mixed effects ordinal regression via ordinal::clmm.

Usage

list.coef(model,rounded=3,alpha=.05)

Arguments

model	A model object. The model should be regression model for limited dependent variables, such as a logistic regression, or a mixed model from nlme or lme4/lmerTest.
rounded	The number of decimal places to round the output. The default is 3.
alpha	The alpha value for confidence intervals. Default is .05.

Value

b	The estimated model coefficients from the model object.
S.E.	The estimated model standard errors from the model object.
Z	The z-statistic corresponding to the coefficient.
LL CI	Given the coefficient, standard error and alpha value (default=.05), the lower limit of the confidence interval around the coefficient is reported.
UL CI	Given the coefficient, standard error and alpha value (default=.05), the upper limit of the confidence interval around the coefficient is reported.
p-val	The p-value associated with the z-statistic.
exp(b)	The exponentiated model coefficients. That is, odds ratios in the case of a logistic regression, or incidence rate ratios in the case of a count model.
LL CI for exp(b)	Given the exponentiated coefficient, standard error and alpha value (default=.05), the lower limit of the confidence interval around the exponentiated coefficient is reported.
UL CI for exp(b)	Given the exponentiated coefficient, standard error and alpha value (default=.05), the upper limit of the confidence interval around the exponentiated coefficient is reported.
Percent	The coefficients in terms of percent change. That is, 100*(exp(coef(model))-1)

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age + race2 +
race3 + race4 + income + ed1 + ed2 + ed3 +
ed4,family="binomial",data=Mize19AH)
list.coef(m1,rounded=4)
list.coef(m1,rounded=4,alpha=.01)

---

Replication data for Logan's (1983) application of conditional logistic regression to mobility processes.

Description

Replication data for Logan's (1983) application of conditional logistic regression to mobility processes.

Usage

data("logan")

Format

A data frame with 4190 observations on the following 11 variables.

occupation

respondent occupation with levels farm operatives craftsmen sales professional

focc

paternal occupation, i.e., father's occupation with levels farm operatives craftsmen sales professional

education

a numeric vector

race

a factor with levels non-black black

id

a numeric vector

tocc

a factor with levels farm operatives craftsmen sales professional

case

a numeric vector

craftsmen

a numeric vector

farm

a numeric vector

operatives

a numeric vector

professional

a numeric vector

References

Logan, John A. 1983. “A Multivariate Model for Mobility Tables.” American Journal of Sociology 89(2):324–349.

Examples

data(logan)
head(logan)

---

Add model predictions, standard errors and confidence intervals to a design matrix for a model object.

Description

Given a model object and a design matrix, this creates a data.frame of the design matrix, with model predictions, standard errors and lower/upper limits of confidence intervals around the predictions. This is a wrap around function for calls to emmeans; it adjusts the emmeans equation to return fitted values on the response scale.

Supported models include OLS regression via lm, logistic regression via glm, Poisson regression via glm, negative binomial regression via MASS:glm.nb, ordinal logistic regression via MASS::polr, multinomial logistic regression via nnet::multinom, zero-inflated Poisson or negative binomial regression via pscl::zeroinfl, hurdle Poisson or negative binomial regression via pscl::hurdle, linear mixed effects models with or without serial correlation via nlme::lme, linear mixed effects models via lme4/lmerTest::lmer, mixed effects logistic regression via lme4/lmerTest::glmer, mixed effects Poisson regression via lme4/lmerTest::glmer, mixed effects negative binomial regression via lme4/lmerTest::glmer.nb, and mixed effects ordinal logistic regression via ordinal::clmm.

For mixed effects ordinal logistic regression models, as estimated via the ordinal package, the outcome variable in the regression model (i.e., the clmm function) needs to be named "dv."

Given one of these model objects and an appropiate design matrix, the function detects the model response type and generates fitted values on the response scale. For example, a logistic regression model returns predicted probabilities, and a Poisson model returns the fitted counts. In addition to the fitted values, the function returns the delta method standard error for the fitted value and a confidence interval. The confidence interval is 95 percent by default, but that may be changed by the user.

Usage

margins.dat(mod,des,alpha=.05,rounded=3,cumulate="no",
pscl.data=data,num.sample=1000,prop.sample=.9,seed=1234)

Arguments

mod	A regression model object.
des	Design matrix of values for the independent variables in the regression model.
alpha	The alpha value for confidence intervals. Default is .05.
rounded	The number of decimal places to round the output. The default is 3.
cumulate	Whether the fitted values should reflect cumulative probabilities. Default is "no." Intended for predicted probabilities drawn from ordinal logistic regression models (polr) or mixed effects ordinal logistic regression (clmm).
pscl.data	If generating predicted counts from Zero-Inflated models (either Poisson or negative binomial), you need to include the data that was specified in the model statement, i.e., the data in the "mod" object.
num.sample	num.sample is the number samples drawn to compute the sampling distibution.
prop.sample	prop.sample is the proportion of the original sample to include in the sampling distibution samples. Default is .9
seed	For models using bootstrapped inference. The seed ensures reproducible results across runs. Default is 1234, but may be changed.

Value

marginsdat

Returns a data.frame containing the design matrix and additional columns for the fitted value on the response scale, the delta method standard error (except partial proportional odds models, which are bootstrapped), and the lower/upper limits on confidence intervals around the fitted value.

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age +
race2 + race3 + race4 + income + ed1 +
ed2 + ed3 + ed4,family="binomial",data=Mize19AH)
des2<-margins.des(m1,expand.grid(woman=c(0,1),parrole=c(0,1)))
margins.dat(m1,des2,rounded=5)
des1 <- margins.des(m1,expand.grid(parrole=1,woman=1))
margins.dat(m1,des1,rounded=5)
des3 <- margins.des(m1,expand.grid(age=seq(20,75,5),parrole=c(0,1)))
a<- margins.dat(m1,des3,rounded=5)
a # Then plot a using ggplot

---

Computes predicted probabilities for conditional and rank-order/exploded logistic regression models. Inference is based upon simulation techniques (requires the MASS package). Alternatively, bootstrapping is an option for conditional logistic regression models.

Description

Given a model object and a design matrix, this creates a data.frame of the design matrix, with predicted probabilities for each response category. Inferential information about the predict probabilities is supported with simulation. Bootstrapping may be added as an option for conditional logistic regression models.

Usage

margins.dat.clogit(mod,design.matrix,run.boot="no",num.sample=1000,
prop.sample=.9,alpha=.05,seed=1234,rounded=3)

Arguments

mod	A conditional logistic regression model as estimated in the Epi package or an Exploded logistic regression model as estimated in the mlogit package.
design.matrix	Design matrix of values for the independent variables in the regression model. Unlike the design matrices in the margins.des function, the design matrix for a conditional logistic regression entails multiple rows, corresponding to the number of response options.
run.boot	Whether to compute confidence intervals around the predicted probabilities using bootstrapping. Defaul is "no."
num.sample	num.sample is the number samples drawn to compute the sampling distibution.
prop.sample	prop.sample is the proportion of the original sample to include in the sampling distibution samples. Default is .9
alpha	The alpha value for confidence intervals. Default is .05.
seed	Sets a seed so that random results are reprodicible.
rounded	How many decimal places to show in the output.

Value

des	Returns a data.frame containing the design matrix and additional columns for the predicted probabilities.
boot.dist	The full bootstrapped distribution for the probabilities.

Author(s)

David Melamed

Examples

data("LF06travel")
m1 <- Epi::clogistic(choice ~ train + bus +
time  + invc, strata=id, data=LF06travel)
design <- data.frame(train=c(0,0,1),bus=c(0,1,0),time=200,invc=20)
design
margins.dat.clogit(m1,design)

---

Creates a design matrix of idealized data for illustrating model predictions.

Description

Create a data frame of idealized data for making model predictions/predicted margins that will be used with margins.dat for generating/plotting model predictions. Given a model object (generalized linear model or generalized linear mixed model), a grid of independent variable values, and a list of any variables (factor variables in particular) to exclude from the design matrix, the function returns the design matrix as a data.frame object. All covariates are set to their means in the data used to estimate the model object. If there are factors in the model, they need to be excluded using the "excl" option.

Supported models include OLS via the lm function, logistic and Poisson regression via the glm function, negative binomial regression via MASS::glm.nb, ordinal logistic regression via MASS::polr, multinomial logistic regression via nnet::multinom, partial proportional odds models via vgam::vglm, linear mixed effects models with or without serial correlation specified via nlme::lme, mixed effects logistic regression via lme4::glmer, mixed effects Poisson regression via lme4::glmer, mixed effects negative binomial regression via lme4::glmer.nb, and mixed effects ordinal logistic regression via ordinal::clmm. Zero-inflated and hurdle models are also supported via pscl::zeroinfl and pscl::hurdle, respectively.

For multinomial regression model, as estimated via the nnet package, you need to provide the data used in the nnet function that defined the model. For partial proportional odds models, as estimated via the vgam package, you need to specify an ordinal model via MASS::polr and provide that model to the margins.des function (the data for the model are not part of a vgam object.) For mixed effects regression models, as estimated by lme4/lmerTest, you need to provide the data used in the glmer or lmer function that defined the model. For mixed effects ordinal logistic regression models, as estimated via the ordinal package, the outcome variable in the regression model (i.e., the clmm function) needs to be named "dv."

Usage

margins.des(mod,ivs,excl="nonE",data)

Arguments

mod	A model object. The model should be regression model for limited dependent variables, such as a logistic regression. Specifically, supported models include lm, glm, polr, multinom, vgam, zeroinf (pscl), amd hurdle (pscl). vgam is supported for partial proportional odds models, not models for count outcomes. zerotrunc is only supported with bootstrapped inference, and may take a while.
ivs	This should be an 'expand.grid' statement including all desired variables and their corresponding levels in the design matrix.
excl	If you want to exclude covariates from the design matrix, you can list them here. This is designed to exclude factor variables from the design matrix, as they do not have means, but may be useful in other specialized cases. Default is "nonE," corresponding to excluding none of the variables.
data	If the model is a multinomial model, you also need to provide the data. This is because nnet objects do not include the relevant information for computing the means of covariates.

Value

design

Returns a data.frame containing the design matrix for model predictions.

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age + race2 +
race3 + race4 + income + ed1 + ed2 + ed3 +
ed4,family="binomial",data=Mize19AH)
des1 <- margins.des(m1,expand.grid(parrole=1,woman=1))
des1
des2<-margins.des(m1,expand.grid(woman=c(0,1),parrole=c(0,1)))
des2
des3 <- margins.des(m1,expand.grid(age=seq(20,75,5),parrole=c(0,1)))
des3

---

Add-Health Data analzed in Mize (2019)

Description

Mize (2019) illustrates how to establish moderation in the context of regression models for limited dependent variables. He illustrates using AddHealth data and provides Stata code to replicate the results. Catregs functions can replicate these results in R.

Usage

data("Mize19AH")

Format

A data frame with 4307 observations on the following 29 variables.

AID

a numeric vector

race

a numeric vector

age

a numeric vector

educ

a numeric vector

degree

a numeric vector

college

a numeric vector

health

a numeric vector

role

a numeric vector

workrole

a numeric vector

parrole

a numeric vector

income

a numeric vector

wages

a numeric vector

logwages

a numeric vector

depB

a numeric vector

alcB

a numeric vector

woman

a numeric vector

edyrs

a numeric vector

whiteB

a numeric vector

X_est_prno

a numeric vector

X_est_prpar

a numeric vector

X_est_alcedmod

a numeric vector

X_est_alcmod

a numeric vector

race2

a numeric vector

race3

a numeric vector

race4

a numeric vector

ed1

a numeric vector

ed2

a numeric vector

ed3

a numeric vector

ed4

a numeric vector

Source

Mize, Trenton D. 2019. "Best Practices for Estimating, Interpreting, and Presenting Nonlinear Interaction Effects" Sociological Science 6: 81-117.

Examples

data(Mize19AH)
head(Mize19AH)

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General Social Survey Data analzed in Mize (2019)

Description

Mize (2019) illustrates how to establish nonlinear moderation in the context of regression models. He illustrates using General Social Survey (GSS) data and provides Stata code to replicate the results. Catregs functions can replicate these results in R.

Usage

data("Mize19GSS")

Format

A data frame with 19337 observations on the following 42 variables.

nosameB

a numeric vector

sameokB

a numeric vector

polviews

a character vector

age

a numeric vector

age10

a numeric vector

year

a numeric vector

id

a numeric vector

degree

a numeric vector

race

a numeric vector

partyid

a character vector

natspac

a character vector

natenvir

a character vector

natheal

a character vector

natcity

a character vector

natcrime

a character vector

natdrug

a character vector

nateduc

a character vector

natrace

a character vector

natarms

a character vector

nataid

a character vector

natfare

a character vector

health

a character vector

helpnotB

a character vector

conserv

a character vector

polviews3

a character vector

employed

a numeric vector

male

a numeric vector

woman

a numeric vector

white

a numeric vector

college

a numeric vector

married

a numeric vector

parent

a character vector

edyrs

a numeric vector

income

a numeric vector

hrswork

a character vector

parttime

a character vector

wages

a numeric vector

conviewSS

a numeric vector

year2

a numeric vector

yearcat

a numeric vector

year1976

a numeric vector

year1976.2

a numeric vector

Source

Mize, Trenton D. 2019. "Best Practices for Estimating, Interpreting, and Presenting Nonlinear Interaction Effects" Sociological Science 6: 81-117.

Examples

data(Mize19GSS)
head(Mize19GSS)

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Aggregate Standard Errors using Rubin's Rule.

Description

The function takes a vector of standard error estimates and it pools them using Rubin's rule.

Usage

rubins.rule(std.errors)

Arguments

std.errors

A vector of standard errors to be aggregated using Rubin's rule.

Value

r.r.std.error

The aggregated standard error.

Author(s)

David Melamed

References

Rubin, Donald B. 2004. Multiple Imputation for Nonresponse in Surveys. Vol. 81. John Wiley & Sons.

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Computes the second difference in fitted values. Inference in supported via the delta method or bootstrapping.

Description

second.diff.fitted computes the second differences between fitted values, that is, the difference between two first differences, from a regression model.

Supported models include OLS regression via lm, logistic regression via glm, Poisson regression via glm, negative binomial regression via MASS:glm.nb, ordinal logistic regression via MASS::polr, partial proportional odds models via vgam::vglm, multinomial logistic regression via nnet::multinom, zero-inflated Poisson or negative binomial regression via pscl::zeroinfl, hurdle Poisson or negative binomial regression via pscl::hurdle, mixed effects logistic regression via lme4/lmerTest::glmer, mixed effects Poisson regression via lme4/lmerTest::glmer, mixed effects negative binomial regression via lme4/lmerTest::glmer.nb, and mixed effects ordinal logistic regression via ordinal::clmm.

Usage

second.diff.fitted(mod,design.matrix,compare,alpha=.05,rounded=3,
bootstrap="no",num.sample=1000,prop.sample=.9,
data,seed=1234,cum.probs="no")

Arguments

mod	A model object. The model should be regression model for limited dependent variables, such as a logistic regression.
design.matrix	Design matrix of values for the independent variables in the regression model.
compare	A set of four rows in the design matrix to use for computing the fitted values that are used in the calculation of second differences. For example, compare(a,b,c,d) results in computing the fitted values for rows a, b, c, and d of the design matrix, respectively, and then computing the following second difference: (a - b) - (c - d). Only four rows may be compared at a time.
alpha	The alpha value for confidence intervals. Default is .05.
rounded	The number of decimal places to round the output. The default is 3.
bootstrap	By default, inference is based on the Delta Method, as implemented in the marginaleffects package. Alternatively, inference can be based upon a bootstrapped sampling distirbution. To do so, change this to "yes"
num.sample	num.sample is the number samples drawn to compute the sampling distibution when using bootstrapping. Default is 1,000
prop.sample	prop.sample is the proportion of the original sample to include in the sampling distibution samples when using bootstrapping. Default is .9
data	For nonparametric inference, provide the data used in the original model statement.
seed	For models using bootstrapped inference. The seed ensures reproducible results across runs. Default is 1234, but may be changed.
cum.probs	For ordinal logistic regression models, including mixed effects models, do you want the first differences to be based on probabilities of the response categories or cumulative probabilities of the response categories. The default is cum.probs=="no" corresponding to non-cumulative probabilities. Change cum.probs to "yes" for cumulative probabilities.

Value

out

If using parametric inference (delta method): output is a dataframe including the second difference in fitted values ("est"), the standard error ("std.error"), the lower limit ("ll"), and upper limit ("ul") of the confidence interval. Of course, ll and ul are based on the alpha level. If using nonparametric inference (bootstrapping): output is a list of objects. obs.diff is the observed second difference in the response or fitted values. boot.dist is the sorted bootstrapped distribution of second differences in the samples. mean.boot.dist is the average of the second differences in the responses or fitted values. sd.boot.dist is the standard deviation of the sampling distribution. ci.95 is the Lower and Upper limits of the confidence interval; despite it's name, the confidence interval is based upon the alpha level. model.class is just the class of the model that was used to generate the fitted values.

Author(s)

David Melamed

Examples

data("Mize19AH")
m1 <- glm(alcB ~woman*parrole + age + race2 + race3 +
race4 + income + ed1 + ed2 + ed3 +
ed4,family="binomial",data=Mize19AH)
des2<-margins.des(m1,expand.grid(woman=c(0,1),parrole=c(0,1)))
des2
second.diff.fitted(m1,des2,compare=c(4,2,3,1),rounded=5)
# [Pr(Drink | Mothers) - Pr(Drink | Childless Women)] -
# [Pr(Drink | Fathers) - Pr(Drink | Childless Men)]

# Note that this is reported as the "Second Difference" in
# Table 3 of Mize (2019: 104, "Best Practices for Estimating,
# Interpreting, and Presenting Nonlinear Interaction Effect.
# Sociological Science. 6(4): 81-117.")

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Data to illustrate mixed effects regression models with serial correlation.

Description

Replication data illustrating serial correlation specifications to adjust for correlated residuals.

Usage

data("wagepan")

Format

A data frame with 4360 observations on the following 51 variables.

nr

a numeric vector

year

a numeric vector

agric

a numeric vector

black

a numeric vector

bus

a numeric vector

construc

a numeric vector

ent

a numeric vector

exper

a numeric vector

fin

a numeric vector

hisp

a numeric vector

poorhlth

a numeric vector

hours

a numeric vector

manuf

a numeric vector

married

a numeric vector

min

a numeric vector

nrthcen

a numeric vector

nrtheast

a numeric vector

occ1

a numeric vector

occ2

a numeric vector

occ3

a numeric vector

occ4

a numeric vector

occ5

a numeric vector

occ6

a numeric vector

occ7

a numeric vector

occ8

a numeric vector

occ9

a numeric vector

per

a numeric vector

pro

a numeric vector

pub

a numeric vector

rur

a numeric vector

south

a numeric vector

educ

a numeric vector

tra

a numeric vector

trad

a numeric vector

union

a numeric vector

lwage

a numeric vector

d81

a numeric vector

d82

a numeric vector

d83

a numeric vector

d84

a numeric vector

d85

a numeric vector

d86

a numeric vector

d87

a numeric vector

expersq

a numeric vector

r

a numeric vector

num

a numeric vector

number

a numeric vector

mn_lwage

a numeric vector

yeart

a numeric vector

yeart2

a numeric vector

yeart3

a numeric vector

Examples

data(wagepan)
head(wagepan)

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