Association Function

Description

Calculates the R-squared from a LOESS regression of y on x. Can be used with outer to produce the a non-parametric correlation matrix.

Usage

assocfun(xind, yind, data)

Arguments

Value

a squared correlation.

Bootstrap Importance Function

Description

Function to calculate bootstrap measures of importance. This function must be passed to the boot function.

Usage

boot_imp(data, inds, obj)

Arguments

Value

A vector of standard deviation of predictions for each term in the model.

Caption Grob

Description

Create a caption grob

Usage

caption(lab, x = 0.5, y = 1, hj = 0.5, vj = 1, cx = 1, fs = 12, ft = "Arial")

Arguments

Value

A text grob.

Canadian Election Study

Description

These data are a subset of the Canadian Election Study telephone sample (Stephenson et. al. 2020).

Format

A data frame with 2799 rows and 29 variables

vote

Vote for Parliament - This variable is used to make all of the “Vote for …” variables. These are actual self-reported votes from the post-election study, not campaign-period vote intention. We coded those who indicated did not vote, none, don't know and refused as missing.

gender

Binary variable indicating respondent sex.

agegrp

Age Group - Age is calculated by subtracting the year of birth from the survey year. Then observations are put into age-groups (18-34, 35-54, 55+)

relig

Religious Affiliation - Respondents are coded into four groups - no religious affiliation/Agnostic, Catholic, Non-Catholic Christians ( incl. Anglican, Baptist, Eastern Orthodox, Johova's Witness, Lutheran, Pentecostal, Presbyterian, Protestant, United Church of Canada, Christian, Salvatian Army, Mennonite) and Other (incl. Buddhist, Hindu, Hewish, Muslim, Sikh). We also include an indicator variable for Catholic vs non-Catholic.

educ

Educational Attainment coded into three categories HS or Less (incl. No schooling, some elementary, completed elementary, some secondary, completed secondary), Some Post-secondary (incl. some echnical/community college, completed technical/community college, some university) and Univ Grad (incl. bachelor’s degree, master’s degree, professional degree)

region

Provinces are coded into four regions: Atlantic (Newfoundland and Labrador, PEI, Nova Scotia, New Brunswick), Quebec, Ontario and the West (Manitoba, Saskatchewan, Alberta and British Columbia)

province

Province of respondent

pid

Party with which respondent identifies. These are coded into Liberal, Conservative, NDP, Green, Bloc Quebecois and Other.

retroper

Retrospective Personal Economic Perceptions - Whether respondent thinks his or her personal economic situation has gotten better, stayed the same or gotten worse in the past year.

retrocan

Retrospective National Economic Perceptions - Whether respondent thinks Canada's economic situation has gotten better, stayed the same or gotten worse in the past year.

sp_defence

Respondent's opinion of how much defence spending should change in three categories - Less (much less, less), Stay the same, More (more or much more).

sp_envir

Respondent's opinion of how much spending on the environment should change in three categories - Less (much less, less), Stay the same, More (more or much more).

immig

Respondent's opinion about how immigration levels should change - Increase, Stay the same/Don't Know, Decrease

usties

Respondent's opinion about how ties between Canada and the US should change - Much more distant, Somewhat more distant, Stay the Same/Don't Know, Somewhat closer, Much closer.

jobspriv

Level of agreement with the following statement - The government should leave it ENTIRELY to the private sector to create jobs: Strongly disagree, Disagree, Don't know, Agree, Strongly agree.

blame

Level of agreement with the following statement - People who don't get ahead should blame themselves, not the system: Strongly disagree, Disagree, Don't know, Agree, Strongly agree.

poorgap

How much should be done to reduce the gap between rich and poor in Canada - Much less, Somewhat less, About the same/Don't know, Somewhat more, Much more.

stayhome

Level of agreement with the following statement - Society would be better off if fewer women worked outside the home: Strongly disagree, Disagree, Don't know, Agree, Strongly agree.

feelgays

Feeling thermometer for homosexuals.

dowomen

How much do you think should be done for women: Much less, Somewhat less, About the same/Don't know, Somewhat more, Much more.

leader_lib

Feeling thermometer for Justin Trudeau, leader of the Liberal Party.

leader_con

Feeling thermometer for Andrew Scheer, leader of the Conservative Party.

leader_ndp

Feeling thermometer for Jagmeet Singh, leader of the NDP.

leader_bloc

Feeling thermometer for Yves-Francois Blanchet, the leader of the Bloc Quebecois.

market

Market liberalism – additive scale of jobspriv, poorgap and blame variables.

moral

Moral traditionalism – additive scale of dowomen, stayhome and feelgays.

union

Whether respondent is a union member - yes or no.

weight_CES

Weighting variable for the CES.

References

Stephenson, Laura B, Allison Harell, Daniel Rubenson, Peter John Loewen. (2020). "2019 Canadian Election Study - Phone Survey", doi:10.7910/DVN/8RHLG1, Harvard Dataverse, V1.

Compact Letter Display for Simple Slopes

Description

Calculates a letter matrix for a simple-slopes output.

Usage

## S3 method for class 'ss'
cld(object, ..., level = 0.05)

Arguments

Value

A compact letter matrix

Hybrid Plot for DFBETAS

Description

Plots a hybrid histogram, dot plot for DFBETAS. A histogram is plotted for the observations below cutval. Observations above cutval are plotted and labelled with individual points.

Usage

dfbhist(
  data,
  varname,
  label,
  cutval = 0.25,
  binwidth = 0.025,
  xlab = "DFBETAS",
  ylab = "Frequency",
  xrange = NULL,
  yrange = NULL,
  nudge_x = NULL,
  nudge_y = NULL
)

Arguments

Value

A ggplot.

Examples

data(wvs)
wvs <- na.omit(wvs[,c("country", "secpay", "gini_disp", "democrat")])
lmod <- lm(secpay ~ gini_disp + democrat, data=wvs)
dba <- dfbetas(lmod)
dbd <- wvs
dbd$dfb_ginil <- dba[,2]^2
dbd$dfb_democl <- dba[,3]^2
dfbhist(dbd, "dfb_ginil", "country")

Heatmap Fit Plot using GGplot

Description

Makes a Heatmap Fit plot (Esary and Pierce, 2012) using GGPlot rather than lattice that the heatmapFit package uses.

Usage

gg_hmf(
  observed,
  prob,
  method = c("loess", "gam"),
  span = NULL,
  nbin = 20,
  R = 1000,
  verbose = TRUE,
  progress = TRUE,
  ...
)

Arguments

Value

Two ggplots - the main heatmap Fit plot and a histogram that can be included as a marginal density.

Examples

data(india)
india$bjp <- ifelse(india$in_prty == 2, 1, 0)
mod1 <- glm(bjp ~  educyrs + anti_immigration, 
            data=india, family=binomial)

gh1 <- gg_hmf(model.response(model.frame(mod1)), 
              fitted(mod1), 
              method="loess")

Importace Measure for Generalized Linear Models

Description

Calculates importance along the lines of Greenwell et al (2018) using partial dependence plots.

Usage

glmImp(obj, varname, data, level = 0.95, ci_method = c("perc", "norm"), ...)

Arguments

Value

A data frame of importance measures with optimal bootstrapped confidence intervals.

References

Greenwell, Brandon M., Bradley C. Boehmke and Andrew J. McCarthy. (2018). “A Simple and Effective Model-Based Variable Importance Measure.” arXiv1805.04755 [stat.ML]

Examples

 
data(gss)
mod <- glm(childs ~ sei10 + sex + educ + age, 
            data=gss, family=poisson)
g_imp1 <- glmImp(mod, "age", gss)

General Social Survey

Description

This is a subset of the 2016 US General Social Survey (Smith et. al. 2016).

Format

A data frame with 2867 rows and 14 variables

aidhouse

On the whole, do you think it should or should not be the government's responsibility to provide decent housing for those who can't afford it? (Definitely Should Be, Probably Should Be, Probably Should Not Be, Definitely Should Not Be)

partyid

Combination of questions regarding partisan affiliation and strength of affiliation. Results in 7-point scale from Strong Democrat to Strong Republican along with Other Party affiliation coded separately as 8.

realinc

Total family income in constant US dollars.

aid_scale

Additive scale of items with same general form as aidhouse, but including items about: decent standard of living for the old, industry with the help it needs to grow, decent standard of living for the unemployed, give financial help to university students and low-income families. Items were standardized and reversed so higher vales indicated greater generosity.

age

Respondent age.

sei10

Socio-economic Status indicator - theoretically ranges from 0 to 1.

sex

Binary indicator of respondent sex.

tax

Are your federal income taxes too high, about right or too low?

newsfrom

Where do you get most of your information about current news events? (newspapers, magazines, the Internet, books or other printed materials, TV, radio, government agencies, family, friends, colleagues, some other source)

educ

Total number of years of formal education completed.

sparts

Please tell me whether you would like to see more or less government spending on culture and the arts. Remember, that if you say "much more" it might require a tax increase to pay for it. Five-point Scale from Spend Much More to Spend Much Less.

wtssnr

Survey Weighting variable

party3

Party ID variable that puts leaners, independents and others together in Other; Strong and moderate Democrats are coded as Democrat while strong and moderate Republicans are coded Republican.

childs

Number of children in respondent's household.

References

Smith, Tom W, Peter Marsden, Michael Hout, and Jibum Kim. (2016). General Social Surveys, 1972-2016 [machine-readable data file] Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science Foundation. -NORC ed.- Chicago: NORC at the University of Chicago [producer and distributor]. Data accessed from the GSS Data Explorer website at https://gss.norc.org/get-the-data.html.

Income and Inequality Data

Description

This merges the Gini coefficient measured in disposable income from the Standardized World Income Inequality Data (Solt, 2020) with GDP and population data from the Penn World Tables version 10 (Feenstra et. al., 2015).

Format

A data frame with 12810 rows and 6 variables

country

Country Name

year

Year

rgdpe

Expenditure-side real GDP at chained PPPs (in mil. 2017 US Dollars). Useful for making cross-country/cross-time comparisons of relative living standards. [PWT]

pop

Population in millions. [PWT]

gdp_cap

GDP divided by population from PWT.

gini

Gini Coefficient (Disposable Income) [SWIID]

References

Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at https://www.rug.nl/ggdc/productivity/pwt/.

Solt, Frederick. 2020. "Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database." Social Science Quarterly 101(3):1183-1199. SWIID Version 9.0, October 2020.

Indian Election Data

Description

These data are from the International Social Survey Programme: National Identity III survey (ISSP Research Group 2015). This subset contains only the data from India.

Format

A data frame with 1530 rows and 22 variables

patriotism

Additive scale of level of agreement regarding statements about patriotism (5-point scale Agree Strongly to Disagree Strongly): Strengthens India's place in the world (-), leads to intolerance in India (+), is needed for India to remain united (-), leads to negative attitudes toward immigrants in India (+). All items are standardized before being summed.

imp_roots

Additive scale of level of agreement regarding statements about importance of the following things for being truly Indian (5-point scale Agree Strongly to Disagree Strongly, all indicators positively associated with the scale): being born in India, having Indian citizenship, having lived in India most of your life, ability to speak Hindi, to be Hindu, to respect India's political institutions and laws, to feel Indian and to have Indian ancestry. All items are standardized and reversed before being summed.

pride_country

Additive scale of level of agreement regarding statements about pride in the following things about India (5-point scale Agree Strongly to Disagree Strongly, all indicators positively associated with the scale): the way democracy works, India's political influence in the world, India's economic achievements, its social security system, its scientific and technological achievements, its achievements in sports, its achievements in the arts and literature, India's armed forces, its history, its fair and equal treatment of all groups in society. All items are standardized and reversed before being summed.

country_first

Additive scale of level of agreement regarding statements about the following things regarding India relationships with other countries (5-point scale Agree Strongly to Disagree Strongly, all indicators positively associated with the scale): India should limit the import of foreign products to protect national economy, India should follow its own interests even if that leads to conflict, foreigners should not be allowed to buy land in India, India's television should give preference to Indian films and programs. All items are standardized and reversed before being summed.

anti_immigration

Additive scale of level of agreement regarding statements about pride in the following things about immigrants (5-point scale Agree Strongly to Disagree Strongly): immigrants increase crime dates (+), immigrants are generally good for India's economy (-), immigrants take jobs away from people born in India (+), immigrations improve Ind'a s society by bringing new ideas and cultures (-), India's culture is generally undermined by immigrants (-), legal immigrants to India who are not citizens should not have the same rights as Indian citizens (+), India should take stronger measures to exclude illegal immigrants (+), legal immigrants should have equal access to public education as Indian citizens (-). All items standardized before being summed.

educyrs

Years of formal education, capped at 20.

age

Respondent age.

sbc

Dummy indicator for Scheduled or Backaward Caste.

sex

Binary indicator of respondent gender.

partliv

Living in a steady relationship with a partner.

religgrp

Religious group to which respondent belongs.

attend

Frequency of attendance at religious services.

topbot

Self-placement in socio-economic status decile.

in_ethn1

Respondent ethnicity.

hhchildr

Number of children under 18 in the household.

in_inc

Income group in local currency.

urbrural

Urban-rural category of residence.

work

Ever had paying work (currently, previously, never).

mainstat

Main current employment status.

union

Union membership (current, previous, never).

vote_bjp

Vote for the BJP in most recent election.

vote_le

Vote turnout in last election.

in_prty

Party voted for in most recent parliamentary election.

party_lr

Party voted for in most recent parliamentary election in terms of ideological position.

References

ISSP Research Group (2015): International Social Survey Programme: National Identity III - ISSP 2013. GESIS Data Archive, Cologne. ZA5950 Data file Version 2.0.0, doi:10.4232/1.12312

Dot Plot with Letter Display

Description

Produces an dot plot with error bars along with a compact letter display

Usage

letter_plot(fits, letters, xlim = NULL)

Arguments

Value

A ggplot.

Examples

library(psre)
library(ggeffects)
library(multcomp)
library(dplyr)
library(ggplot2)
data(wvs)
wvs$civ <- with(wvs, case_when(
    civ == 4 ~ "Islamic", 
    civ == 6 ~ "Latin American", 
    civ == 7 ~ "Orthodox", 
    civ == 8 ~ "Sinic", 
    civ == 9 ~ "Western", 
    TRUE ~ "Other"))
wvs$civ = factor(wvs$civ, levels=c("Western", 
                                   "Sinic", 
                                   "Islamic", 
                                   "Latin American", 
                                   "Orthodox", 
                                   "Other"))

mod <- lm(resemaval ~ civ + gdp_cap + 
            pct_secondary + pct_univ_degree + 
            pct_high_rel_imp, data=wvs)

eff <- ggpredict(mod, 
                            "civ", 
                            ci.lvl = .95)

pwc <- summary(glht(mod, linfct=mcp(civ = "Tukey")), 
               test=adjusted(type="none"))
cld1 <- cld(pwc)
lmat <- cld1$mcletters$LetterMatrix
eff$x <- reorder(eff$x, eff$predicted, mean)
letter_plot(eff, lmat) + 
  labs(x="Predicted Emancipative Values\n(95% Confidence Interval)")

Make Arguments for Linear Smooth

Description

Makes arguments that serve as input to 'ggplot2::geom_smooth()'.

Usage

linear_args(
  method = "lm",
  formula = NULL,
  se = FALSE,
  na.rm = TRUE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  color = "black",
  linetype = 1,
  ...
)

Arguments

Value

A list with arguments that can be used as input to 'ggplot2::geom_smooth()'.

Make Arguments for LOESS Smooth

Description

Makes arguments that serve as input to 'ggplot2::geom_smooth()'.

Usage

loess_args(
  method = "loess",
  formula = NULL,
  se = FALSE,
  na.rm = TRUE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  span = 0.75,
  color = "black",
  linetype = 2,
  ...
)

Arguments

Value

A list with arguments that can be used as input to 'ggplot2::geom_smooth()'.

Linear Scatterplot Array

Description

Produces a linear scatterplot array with marginal histograms

Usage

lsa(
  formula,
  xlabels = NULL,
  ylab = NULL,
  data,
  ptsize = 1,
  ptshape = 1,
  ptcol = "gray65",
  linear = TRUE,
  loess = TRUE,
  lm_args = linear_args(),
  lo_args = loess_args(),
  ptalpha = 1,
  ...
)

Arguments

Value

A cowplot object.

Examples

data(wvs)
lsa(formula = as.formula(sacsecval ~ resemaval + moral + 
                           pct_univ_degree + pct_female + 
                           pct_low_income), 
  xlabels = c("Emancipative Vals", "Moral Perm", 
              "% Univ Degree", "% Female", "% Low Income"), 
  ylab = "Secular Values", 
  data=wvs)

Kernel Density with Normal Density Overlay

Description

Calculates a kernel density estimate of the data along with confidence bounds. It also computes a normal density and confidence bounds for the normal density with the same mean and variance as the observed data.

Usage

normBand(x, ...)

Arguments

Details

The function is largely cribbed from the sm package by Bowman and Azzalini

Value

A named vector of scalar measures of fit

Author(s)

Dave Armstrong, A.W. Bowman and A. Azzalini

References

A.W> Bowman and A. Azzalini, R package sm: nonparametric smoothing methods (verstion 5.6).

Calculate the Optimal Visual Testing Confidence Level

Description

Calculates the Optimal Visual Testing (OVT) confidence level. The OVT level is a level you can use to make confidence intervals such that the overlapping (or non-overlapping) of confidence intervals preserves the pairwise testing results. That is, statistically different estimates have confidence intervals that do not overlap and statistically indistinguishable intervals have confidence intervals that do overlap. It does not always work perfectly, but it generally results in fewer inferential errors than the nominal level.

Usage

optCL(
  obj = NULL,
  b = NULL,
  v = NULL,
  level = 0.95,
  grid_range = c(0.75, 0.99),
  grid_length = 100,
  adjust = p.adjust.methods[c(8, 1:7)],
  print_message = TRUE,
  ...
)

Arguments

Value

A list (of class "viztest") with the following elements: 1. tab: a data frame with results from the grid search. The data frame has four variables: 'level' - is the confidence level used in the grid search; 'psame' - the proportion of (non-)overlaps that match the normal theory tests; 'pdiff' - the proportion of pairwise tests that are statistically significant; 'easy' - the ease with which the comparisons are made. 2. pw_tests: A logical vector indicating which tests are significantly significant. 3. ci_tests: A logical vector indicating whether the confidence intervals are disjoint ('TRUE') or overlap ('FALSE'). 4. combs: The pairwise combinations of stimuli used in the test. Note, the stimuli are reordered from largest to smallest, so the numbers do not represent the position in the original ordering. 5. param_names: A vector of the names of the parameters reordered by size - largest to smallest. 6. L: The lower confidence bounds from the grid search. 7. U: The upper confidence bounds from the grid search. 8. est: A data frame with the variables 'vbl' - the parameter name; 'est' - the parameter estimate; 'se' - the parameter standard error. 9. call: model call

Examples

data(wvs)
wvs$civ2 <- "Other"
wvs$civ2 <- ifelse(wvs$civ == 9, 
                   "Western", 
                   wvs$civ2)
wvs$civ2 <- ifelse(wvs$civ == 6, 
                   "Latin American", 
                   wvs$civ2)
wvs$civ2 <- as.factor(wvs$civ2)

intmod <- lm(resemaval ~ civ2 * pct_secondary, 
             data=wvs)

ss2 <- simple_slopes(intmod, 
                     "pct_secondary", 
                     "civ2")
o2 <- optCL(b=ss2$est$slope, v=ss2$v)

Print Method for Silber, Rosenbaum and Ross Importance Measure

Description

Prints the results of the srr_imp function

Usage

## S3 method for class 'srr'
print(x, ...)

Arguments

Value

Printed output

Print Method for Simple Slopes

Description

Prints the results of the Simple Slopes function

Usage

## S3 method for class 'ss'
print(x, ...)

Arguments

Value

Printed output

Quantile Comparison Data

Description

Makes data that can be used in quantile comparison plots.

Usage

qqPoints(
  x,
  distribution = "norm",
  line = c("quartiles", "robust", "none"),
  conf = 0.95,
  ...
)

Arguments

Value

A data frame with variables x observed quantiles, theo the theoretical quantiles and lwr and upr the confidence bounds. The slope and intercept of the line running through the points are returned as a and b as an attribute of the data.a

Examples

x <- rchisq(100, 3)
qqdf <- qqPoints(x)
a <- attr(qqdf, "ab")[1]
b <- attr(qqdf, "ab")[2]
l <- min(qqdf$theo) * b + a
u <- max(qqdf$theo) * b + a
library(ggplot2)
ggplot(qqdf, aes(x=theo, y=x)) +
  geom_ribbon(aes(ymin=lwr, ymax=upr), alpha=.15) +
  geom_segment(aes(x=min(qqdf$theo), xend=max(qqdf$theo), y = l, yend=u)) +
  geom_point(shape=1) +
  theme_classic() +
  labs(x="Theoretical Quantiles",
       y="Observed Quantiles")

World Values Survey Religious Importance

Description

A subset of data from the second thorugh fifth waves of the World Values Survey measuring religious importance.

Format

A data frame with 224 rows and 4 variables

country

Country of respondent residence.

relig_imp

Response Category for the religious importance variable: Very Important, Rather Important, Not Very Important and Not At All Important.

n

Proportion of observation in each country-response category.

l

The average value of religious importance on the 1-4 scale.

Details

These data come from the same source as the wvs data. These are aggregated responses to the question about religious importance by country and religious importance response.

The dataset has 224 rows and 4 variables. The variables are as follows:

References

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014a. World Values Survey: Round Two - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014b. World Values Survey: Round Three - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014c. World Values Survey: Round Four - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014d. World Values Survey: Round Five - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

State Repression Dataset

Description

These data consider the democracy-repression nexus. While they are different data than used in previous studies, they are similar in spirit to the data used in Poe and Tate (1994) and in Davenport and Armstrong (20040).

Format

A data frame with 1530 rows and 22 variables

gwno

Gleditsch and Ward numeric country code

year

Year of observation

pts_s

Political Terror Scale coding of State Department country reports.

rgdpe

Penn World Tables measure of GDP in millions $USD.

pop

Population in millions from the Penn World Tables.

pr

Freedom House's Political Rights measure (0-40)

cwar

Civil War indicator from the UCDP Armed Conflict Database.

iwar

Interstate War indicator from the UCDP Armed Conflict Database.

References

Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer 2015. ‘The Next Generation of the Penn World Table’ American Economic Review, 105(10), 3150-3182, available for download at https://www.rug.nl/ggdc/productivity/pwt/.

Freedom House. (2020). Freedom in the World 2020. New York: Freedom House.

Gleditsch, Nils Petter; Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Havard Strand, 2002. ‘Armed Conflict 1946–2001: A New Dataset’, Journal of Peace Research 39(5): 615–637.

Gibney, Mark, Linda Cornett, Reed Wood, Peter Haschke, Daniel Arnon, Attilio Pisano, Gray Barrett, and Baekkwan Park. 2020. ‘The Political Terror Scale 1976-2019.’ Date Retrieved, from the Political Terror Scale website: https://www.politicalterrorscale.org/.

Residual-Residual Plot

Description

Produces a linear scatterplot array with marginal histograms. The plots have OLS regression lines and a 45-degree line.

Usage

rrPlot(
  formula,
  xlabels = NULL,
  ylab = NULL,
  data,
  return = c("grid", "grobs"),
  ptsize = 1,
  ptshape = 1,
  ptcol = "gray65"
)

Arguments

Value

A cowplot object.

Examples

data(wvs)
library(MASS)
lmod <- lm(secpay ~ gini_disp + democrat + log(pop), data=wvs)
e1_m <- rlm(secpay ~ gini_disp + democrat + log(pop), 
                  data=wvs, method="M")$residuals
e1_mm <- rlm(secpay ~ gini_disp + democrat + log(pop), 
                   data=wvs, method="MM")$residuals
e1dat <- data.frame(OLS = lmod$residuals, 
                    M = e1_m, 
                    MM = e1_mm)
rrPlot(OLS ~ M + MM, data=e1dat)

Shuffle coefficients and standard errors together

Description

Function shuffles together coefficients and standard errors with a significance flag.

Usage

shuffle(b, pv, se, alpha = 0.05, digits = 3, names = NULL)

Arguments

Value

A character vector of printed output

Examples

library(nnet)
data(repress)
mrm <- multinom(pts_s ~ pr + cwar + iwar +  log(rgdpe) + log(pop), data=repress)
b <- coef(mrm)
v <- vcov(mrm)
b <- c(t(b))
se <- sqrt(diag(v))
pv <- 2*pnorm(abs(b/se), lower.tail=FALSE)
tab11_7 <- matrix(shuffle(b, pv, se), ncol=4)
rownames(tab11_7) <- rep("", 12)
rownames(tab11_7)[seq(1, 12, by=2)] <- colnames(coef(mrm))
colnames(tab11_7) <- paste0("PTS = ", 2:5)
noquote(tab11_7)

Calculate Simple Slopes

Description

Calculates Simple Slopes from an interaction between a categorical and quantitative variable.

Usage

simple_slopes(mod, quant_var, cat_var, ...)

Arguments

Value

A data frame giving the conditional partial effect along with standard errors, t-statistics and p-values.

Absolute Importance Measure

Description

Calculates absolute importance along the lines consistent with relative importance as defined by Silber, Rosenbaum and Ross (1995)

Usage

srr_imp(
  obj,
  data,
  boot = TRUE,
  R = 250,
  level = 0.95,
  pct = FALSE,
  combine_terms = NULL,
  ...
)

Arguments

Value

A data frame of importance measures with optimal bootstrapped confidence intervals.

References

Silber, J. H., Rosenbaum, P. R. and Ross, R N (1995) Comparing the Contributions of Groups of Predictors: Which Outcomes Vary with Hospital Rather than Patient Characteristics? JASA 90, 7–18.

Examples

data(gss)
mod <- glm(childs ~ sei10 + sex + educ + age, 
            data=gss, family=poisson)
srr_imp(mod, data=gss)

Truncated Power Basis Functions

Description

Makes truncated power basis spline functions.

Usage

tpb(x, degree = 3, nknots = 3, knot_loc = NULL)

Arguments

Value

A n x degree+nknots matrix of basis function values.

Examples

library(psre)
data(wvs)
smod3 <- lm(secpay ~ tpb(gini_disp, degree=3, knot_loc=.35) + democrat, data=wvs)
summary(smod3)

Transform Variables to Normality

Description

Note, that we do note use the Doornik-Hansen test because the implementation in 'normwh.test' has been archived. We continue to use the other methods prescribed in Velez et al.

Usage

transNorm(
  x,
  start = 0.01,
  family = c("bc", "yj"),
  lams,
  combine.method = c("Stouffer", "Fisher", "Average"),
  ...
)

Arguments

Details

Uses the method proposed by Velez, Correa and Marmolejo-Ramos to normalize variables using Box-Cox or Yeo-Johnson transformations.

Value

A scalar giving the optimal transformation parameter.

References

Velez Jorge I., Correa Juan C., Marmolejo-Ramos Fernando. (2015) "A new approach to the Box-Cox Transformation" Frontiers in Applied Mathematics and Statistics.

Examples

data(wvs)
library(car)
lam <- transNorm(wvs$gdp_cap,
          family="yj",
          lams =c(-2,2))
wvs$trans_gdp <- yjPower(wvs$gdp_cap, 
             lambda=lam)

World Values Survey Aggregate Data

Description

A subset of data from the second thorugh fifth waves of the World Values Survey.

Format

A data frame with 162 rows and 26 variables

country

Country of respondent residence.

wave

Wave of the survey.

year

Year of the survey.

pct_high_rel_imp

Religious importance is coded as Very, Rather, Not very or Not at all important in the individual data. This variable is the proportion of respondents who indicated Very or Rather.

pct_female

Proportion of observations identifying as female.

mean_lr

Left-right self-placement is coded on a 1 (Left) to 10 (Right) scale in the individual data. The mean_lr variable is the country-wave average of left-right self-placement.

pct_less_secondary, pct_secondary, pct_some_univ, pct_univ_degree

In the individual data, education is coded as Less than secondary, Secondary complete, Some university and University degree or more. In the aggregate data, we calculate the proportion of observations in each category.

pct_low_income, pct_mid_income, pct_high_income

In the individual data, income is coded in decies (i.e., a 1-10 scale). In the aggregate data, we calculate the proportion of observations in categories 1-3 (Low), 4-7 (Middle) and 8-10 (High) categories.

moral

In the individual data, we created an additive scale of variables about how justifiable the following actions are: Illegally claiming government benefits, Avoiding a fare on public transport, Cheating on taxes, Accepting a bribe, Homosexuality, Divorce, Abortion, Prostitution, Euthanasia, Suicide on a scale from 1 (Never justifiable) to 10 (Always Justifiable). In the aggregate data, we calculate the country-wave average of this scale.

sacsecval

Secular Values - opposite of traditional values wherein religion, parent-child ties, deference to authority and traditional family values are paramount. In the aggregate data, we take the country-wave average of this scale.

secpay

Imagine two secretaries, of the same age, doing practically the same job. One finds out that the other earns considerably more than she does. The better paid secretary, however, is quicker, more efficient and more reliable at her job. In your opinion, is it fair or not fair that one secretary is paid more than the other? The secpay variable is the proportion of people in each country indicating that the pay discrepancy is unfair.

resemaval

Emancipative Values - preference for gender and racial equality, liberty and personal autonomy. In the aggregate data, we take the country-wave average of this scale.

rgdpe

Expenditure-side real GDP at chained PPPs (in mil. 2017US$). Useful for making cross-country/cross-time comparisons of relative living standards. Obtained from Penn World Tables.

rel_soc

Dummy variable indicating places where at least 75 respondents identified religion as being important.

pop

Population in Millions, obtained from Penn World Tables.

gdp_cap

GDP/capita: rgdpe/pop.

gini_disp

Gini coefficient in terms of disposable income from the SWIID.

gini_mkt

Gini coefficient in terms of market prices from the SWIID.

polrt

Measure of the violation of political rights from the Freedom in the World Project. Coded on a scale from 1 (fewest violations) to 7 (most violations).

civlib

Measure of the violation of civil liberties from the Freedom in the World Project. Coded on a scale from 1 (fewest violations) to 7 (most violations).

democrat

Using the freedom status variable, we code a country as a democracy if in the past 15 years it was always at least partly free and was free for at least 50 percent of the time. This follows the work of Weakliem et. al. (2005).

civ

Categories defining the civilization in which each country belongs. Other=0, African=1, Buddhist=2, Hindu=3, Islamic=4, Japanese=5, Latin American=6, Orthodox=7, Siinic=8, Western=9.

Details

We started with waves 2 (Inglehart et. al., 2014a), 3 (Inglehart et. al., 2014b), 4 (Inglehart et. al., 2014c) and 5 (Inglehart et. al., 2014d) of the World Values Survey (WVS). The WVS is a cross-national survey effort aimed at describing the character of value systems around the globe. From each survey, we capture country and survey year, several demographic variables (Religious Importance, fairness, left-right self-placement, education, income, sex and age) along with some values scales (emancipative values and secular values). We also capture several questions about the extent to which several controversial actions are morally justifiable. We add data from several other projects to these data. To measure inequality, we use the Standardized World Income Inequality Data (Solt, 2020). From this dataset, we capture the Gini Coefficient (both in disposable income and market income, though we tend to use the former in models). We obtain GDP and population data from the Penn World Tables version 10 (Feenstra et. al., 2015). We gather data on political rights, civil liberties and freedom status from the Freedom in the World Project (Freedom House, 2020). We use the civilizations codes from Henderson and Tucker (2001), which were used to test Huntington’s (1996) argument about the “clash of civilizations”. Finally, we get the human development index (HDI) from the United Nations Development Programme (2020). The combined dataset has 237,787 individual observations nested within 84 countries. Most countries appear in only one or two waves (65), but nine appear in three waves and 10 in four waves.

We aggregate the variables in the individual dataset by country-wave to produce a more manageable data set. The aggregate dataset has 162 rows and 38 variables. The variables are as follows:

References

Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at https://www.rug.nl/ggdc/productivity/pwt/.

Freedom House. (2020). Freedom in the World 2020. New York: Freedom House.

Henderson, Errol A. and Richard Tucker. 2001. "Clear and Present Strangers: The Clash of Civilizations and International Conflict." International Studies Quarterly, 45(2):317–338.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014a. World Values Survey: Round Two - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014b. World Values Survey: Round Three - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014c. World Values Survey: Round Four - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014d. World Values Survey: Round Five - Country-Pooled Datafile Version. Madrid: JD Systems Institute.

Solt, Frederick. 2020. "Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database." Social Science Quarterly 101(3):1183-1199. SWIID Version 9.0, October 2020.