Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
dpi = 300
)
## -----------------------------------------------------------------------------
data(mtcars) #load the data
## -----------------------------------------------------------------------------
m_cyl <- lm(mpg ~ wt * cyl, data = mtcars)
summary(m_cyl)
## ----out.width="80%"----------------------------------------------------------
library(interplot)
interplot(m = m_cyl, var1 = "cyl", var2 = "wt")
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "wt", var2 = "cyl")
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "wt", var2 = "cyl", ci = .9, point = T)
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "cyl", var2 = "wt", point = T) +
# changing the angle of x labels for a clearer vision
theme(axis.text.x = element_text(angle=90))
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "cyl", var2 = "wt") +
# Add labels for X and Y axes
xlab("Automobile Weight (thousands lbs)") +
ylab("Estimated Coefficient for\nNumber of Cylinders") +
# Change the background
theme_bw() +
# Add the title
ggtitle("Estimated Coefficient of Engine Cylinders \non Mileage by Automobile Weight") +
theme(plot.title = element_text(face="bold")) +
# Add a horizontal line at y = 0
geom_hline(yintercept = 0, linetype = "dashed")
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "wt", var2 = "cyl", ercolor = "blue", esize = 1.5) +
geom_point(size = 2, color = "red")
## ----out.width="80%"----------------------------------------------------------
m_wt <- lm(mpg ~ wt + I(wt^2), data = mtcars)
interplot(m = m_wt, var1 = "wt", var2 = "wt")
## ----out.width="80%"----------------------------------------------------------
mtcars$gear <- factor(mtcars$gear)
m_gear <- lm(mpg ~ gear * wt, data = mtcars)
interplot(m = m_gear, var1 = "wt", var2 = "gear")
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_gear, var1 = "wt", var2 = "gear", facet_labs = c("4-speed", "5-speed"))
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "cyl", var2 = "wt", hist = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed")
## ----out.width="80%"----------------------------------------------------------
interplot(m = m_cyl, var1 = "cyl", var2 = "wt", hist = TRUE) +
aes(color = "pink") + theme(legend.position="none") + # geom_line(color = "pink") +
geom_hline(yintercept = 0, linetype = "dashed")
## ----out.width="80%"----------------------------------------------------------
set.seed(313)
interplot(m = m_cyl, var1 = "cyl", var2 = "wt", stats_cp = "ci")
interplot(m = m_cyl, var1 = "cyl", var2 = "wt", stats_cp = "ks")
## ----out.width="80%"----------------------------------------------------------
set.seed(313)
interplot(m = m_cyl, var1 = "cyl", var2 = "wt", stats_cp = "ci", txt_caption = "\n Source: Motor Trend Car Road Tests 1973.")
## ----out.width="80%"----------------------------------------------------------
stdCI_plot <- interplot(m = m_wt, var1 = "wt", var2 = "wt", adjCI = FALSE) +
ggtitle("Marginal Effects with Standard CIs")
adjCI_plot <- interplot(m = m_wt, var1 = "wt", var2 = "wt", adjCI = TRUE) +
ggtitle("Marginal Effects with Adjusted CIs")
library(gridExtra)
grid.arrange(stdCI_plot, adjCI_plot, ncol = 2)
## ----out.width="80%"----------------------------------------------------------
pew1.w <- read.csv("pew1_w.csv")
m <- glm(formula=meritocracy~ginicnty+income_i+ginicnty:income_i+income_cnty+black_cnty+
perc_bush04+pop_cnty+educ_i+age_i+gender_i+unemp_i+union_i+partyid_i+
ideo_i+attend_i+survid2006+survid2007+survid2009,
data=pew1.w,family=binomial(link="logit"))
plot_avg <- interplot(m, var1 = "ginicnty",var2 = "income_i", predPro = FALSE) +
ggtitle("Average Conditional Effects")
plot_3val <- interplot(m, var1 = "ginicnty",var2 = "income_i", predPro = TRUE, var2_vals = c(min(pew1.w$income_i), max(pew1.w$income_i))) +
ggtitle("Conditional Predicted Probabilities for \nCitizens with Low and High Incomes") +
scale_colour_discrete(guide = guide_legend(title = "Income"), labels = c("Low", "High")) +
scale_fill_discrete(guide = guide_legend(title = "Income"), labels = c("Low", "High")) +
theme(legend.position = c(0, .8), legend.justification = c(0, .5))
grid.arrange(plot_avg, plot_3val, ncol = 2)
## ----out.width="80%"----------------------------------------------------------
# Create a fake dataset of conditional effects
fake <- rnorm(100, 0, 1)
coef1 <- fake * sample(.5:2.5, 100, replace = T)
lb <- coef1 - .5
ub <- coef1 + .5
df_fake <- data.frame(cbind(fake, coef1, lb, ub))
# Use interplot directly with the dataset
interplot(df_fake)
## ----out.width="80%"----------------------------------------------------------
var2_fake <- fake
# Set `hist` to TRUE is required to superimpose a histogram.
interplot(df_fake, hist = TRUE, var2_dt = var2_fake)
## The ribbon and histogram do not fit. This is just an illustration
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