Nothing
## ----message=FALSE, results = 'hide'------------------------------------------
rm(list = ls())
library(tidyverse)
library(rstatix)
library(parameters)
library(performance)
library(jtools)
library(pubh)
library(sjlabelled)
library(sjPlot)
theme_set(sjPlot::theme_sjplot2(base_size = 10))
theme_update(legend.position = "top")
options('huxtable.knit_print_df' = FALSE)
options('huxtable.autoformat_number_format' = list(numeric = "%5.2f"))
knitr::opts_chunk$set(comment = NA)
## -----------------------------------------------------------------------------
data(birthwt, package = "MASS")
birthwt <- birthwt %>%
mutate(
smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
race = factor(race, labels = c("White", "African American", "Other"))
) %>%
var_labels(
bwt = 'Birth weight (g)',
smoke = 'Smoking status',
race = 'Race'
)
## -----------------------------------------------------------------------------
birthwt %>%
box_plot(bwt ~ smoke, fill = ~ race)
## -----------------------------------------------------------------------------
birthwt %>%
gen_bst_df(bwt ~ race|smoke) %>%
as_hux() %>% theme_pubh()
## -----------------------------------------------------------------------------
model_norm <- lm(bwt ~ smoke + race, data = birthwt)
## -----------------------------------------------------------------------------
model_norm %>% Anova()
## -----------------------------------------------------------------------------
model_norm %>% parameters()
## -----------------------------------------------------------------------------
model_norm %>% performance()
## -----------------------------------------------------------------------------
model_norm %>%
tbl_regression() %>%
cosm_reg() %>% theme_pubh() %>%
add_footnote(get_r2(model_norm), font_size = 9)
## -----------------------------------------------------------------------------
model_norm %>%
glm_coef(labels = model_labels(model_norm)) %>%
as_hux() %>% set_align(everywhere, 2:3, "right") %>%
theme_pubh() %>%
add_footnote(get_r2(model_norm), font_size = 9)
## -----------------------------------------------------------------------------
model_norm %>%
glm_coef(se_rob = TRUE, labels = model_labels(model_norm)) %>%
as_hux() %>% set_align(everywhere, 2:3, "right") %>%
theme_pubh() %>%
add_footnote(paste(
get_r2(model_norm), "\n",
"CIs and p-values estimated with robust standard errors."),
font_size = 9)
## -----------------------------------------------------------------------------
model_norm %>%
plot_model("pred", terms = ~race|smoke, dot.size = 1.5, title = "")
## -----------------------------------------------------------------------------
emmip(model_norm, smoke ~ race) %>%
gf_labs(y = get_label(birthwt$bwt), x = "", col = "Smoking status")
## -----------------------------------------------------------------------------
data(diet, package = "Epi")
diet <- diet %>%
mutate(
chd = factor(chd, labels = c("No CHD", "CHD"))
) %>%
var_labels(
chd = "Coronary Heart Disease",
fibre = "Fibre intake (10 g/day)"
)
## -----------------------------------------------------------------------------
diet %>% estat(~ fibre|chd) %>%
as_hux() %>% theme_pubh()
## -----------------------------------------------------------------------------
diet %>% na.omit() %>%
copy_labels(diet) %>%
box_plot(fibre ~ chd)
## -----------------------------------------------------------------------------
model_binom <- glm(chd ~ fibre, data = diet, family = binomial)
## -----------------------------------------------------------------------------
model_binom %>% parameters(exponentiate = TRUE)
## -----------------------------------------------------------------------------
model_binom %>% performance()
## -----------------------------------------------------------------------------
model_binom %>%
tbl_regression(exponentiate = TRUE) %>%
cosm_reg() %>% theme_pubh() %>%
add_footnote(get_r2(model_binom), font_size = 9)
## -----------------------------------------------------------------------------
model_binom %>%
plot_model("pred", terms = "fibre [all]", title = "")
## -----------------------------------------------------------------------------
data(bdendo, package = "Epi")
bdendo <- bdendo %>%
mutate(
cancer = factor(d, labels = c('Control', 'Case')),
gall = factor(gall, labels = c("No GBD", "GBD")),
est = factor(est, labels = c("No oestrogen", "Oestrogen"))
) %>%
var_labels(
cancer = 'Endometrial cancer',
gall = 'Gall bladder disease',
est = 'Oestrogen'
)
## -----------------------------------------------------------------------------
bdendo %>%
mutate(
cancer = relevel(cancer, ref = "Case"),
gall = relevel(gall, ref = "GBD"),
est = relevel(est, ref = "Oestrogen")
) %>%
copy_labels(bdendo) %>%
select(cancer, gall, est) %>%
tbl_strata(
strata = est,
.tbl_fun = ~ .x %>%
tbl_summary(by = gall)
) %>%
cosm_sum(bold = TRUE, head_label = " ") %>%
theme_pubh(2) %>%
set_align(1, everywhere, "center")
## -----------------------------------------------------------------------------
bdendo %>%
gf_percents(~ cancer|gall, fill = ~ est, position = "dodge", alpha = 0.6) %>%
gf_labs(
y = "Percent",
x = "",
fill = ""
)
## -----------------------------------------------------------------------------
require(survival, quietly = TRUE)
model_clogit <- clogit(cancer == 'Case' ~ est * gall + strata(set), data = bdendo)
## -----------------------------------------------------------------------------
model_clogit %>%
glm_coef(labels = c("Oestrogen/No oestrogen", "GBD/No GBD",
"Oestrogen:GBD Interaction")) %>%
as_hux() %>% set_align(everywhere, 2:3, "right") %>%
theme_pubh() %>%
add_footnote(get_r2(model_clogit), font_size = 9)
## ----message=FALSE------------------------------------------------------------
#require(MASS, quietly = TRUE)
data(housing, package = "MASS")
housing <- housing %>%
var_labels(
Sat = "Satisfaction",
Infl = "Perceived influence",
Type = "Type of rental",
Cont = "Contact"
)
## -----------------------------------------------------------------------------
model_ord <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing, Hess = TRUE)
## -----------------------------------------------------------------------------
model_ord %>%
tbl_regression(exponentiate = TRUE) %>%
cosm_reg() %>% theme_pubh() %>%
add_footnote(get_r2(model_ord), font_size = 9)
## -----------------------------------------------------------------------------
model_ord %>%
plot_model(type = "pred", terms = c("Infl", "Cont"),
dot.size = 1, title = "") %>%
gf_theme(axis.text.x = element_text(angle = 45, hjust = 1))
## -----------------------------------------------------------------------------
model_ord %>%
plot_model(type = "pred", terms = c("Infl", "Type"),
dot.size = 1, title = "") %>%
gf_theme(axis.text.x = element_text(angle = 45, hjust = 1))
## -----------------------------------------------------------------------------
emmip(model_ord, Infl ~ Type |Cont) %>%
gf_labs(x = "Type of rental", col = "Perceived influence")
## -----------------------------------------------------------------------------
data(quine, package = "MASS")
levels(quine$Eth) <- c("Aboriginal", "White")
levels(quine$Sex) <- c("Female", "Male")
## -----------------------------------------------------------------------------
quine <- quine %>%
var_labels(
Days = "Number of absent days",
Eth = "Ethnicity",
Age = "Age group"
)
## -----------------------------------------------------------------------------
quine %>%
cross_tbl(by = "Eth") %>%
theme_pubh(2) %>%
add_footnote("n (%); Median (IQR)", font_size = 9)
## -----------------------------------------------------------------------------
quine %>%
box_plot(Days ~ Age|Sex, fill = ~ Eth)
## -----------------------------------------------------------------------------
model_pois <- glm(Days ~ Eth + Sex + Age, family = poisson, data = quine)
## -----------------------------------------------------------------------------
model_pois %>%
tbl_regression(exponentiate = TRUE) %>%
cosm_reg() %>% theme_pubh() %>%
add_footnote(get_r2(model_pois), font_size = 9)
## -----------------------------------------------------------------------------
model_pois %>% performance()
## -----------------------------------------------------------------------------
model_pois %>% check_overdispersion()
## -----------------------------------------------------------------------------
model_negbin <- MASS::glm.nb(Days ~ Eth + Sex + Age, data = quine)
## -----------------------------------------------------------------------------
model_negbin %>%
tbl_regression(exponentiate = TRUE) %>%
cosm_reg() %>% theme_pubh() %>%
add_footnote(get_r2(model_negbin), font_size = 9)
## -----------------------------------------------------------------------------
model_negbin %>% performance()
## -----------------------------------------------------------------------------
model_negbin %>%
plot_model(type = "pred", terms = c("Age", "Eth"),
dot.size = 1.5, title = "")
## -----------------------------------------------------------------------------
emmip(model_negbin, Eth ~ Age|Sex) %>%
gf_labs(y = "Number of absent days", x = "Age group", col = "Ethnicity")
## -----------------------------------------------------------------------------
multiple(model_negbin, ~ Age|Eth)$df
## -----------------------------------------------------------------------------
multiple(model_negbin, ~ Age|Eth)$fig_ci %>%
gf_labs(x = "IRR")
## -----------------------------------------------------------------------------
data(bladder)
bladder <- bladder %>%
mutate(times = stop,
rx = factor(rx, labels=c("Placebo", "Thiotepa"))
) %>%
var_labels(times = "Survival time",
rx = "Treatment")
## -----------------------------------------------------------------------------
model_surv <- survreg(Surv(times, event) ~ rx, data = bladder)
## -----------------------------------------------------------------------------
model_surv %>%
glm_coef(labels = c("Treatment: Thiotepa/Placebo", "Scale"), se_rob = TRUE) %>%
as_hux() %>% set_align(everywhere, 2:3, "right") %>%
theme_pubh(1) %>%
add_footnote(get_r2(model_surv), font_size = 9)
## -----------------------------------------------------------------------------
model_exp <- survreg(Surv(times, event) ~ rx, data = bladder, dist = "exponential")
## -----------------------------------------------------------------------------
model_exp %>%
glm_coef(labels = c("Treatment: Thiotepa/Placebo"), se_rob = TRUE) %>%
as_hux() %>% set_align(everywhere, 2:3, "right") %>%
theme_pubh() %>%
add_footnote(get_r2(model_exp), font_size = 9)
## -----------------------------------------------------------------------------
model_exp %>%
glm_coef(labels = c("Treatment: Thiotepa/Placebo")) %>%
as_hux() %>% set_align(everywhere, 2:3, "right") %>%
theme_pubh() %>%
add_footnote(get_r2(model_exp), font_size = 9)
## -----------------------------------------------------------------------------
model_exp %>%
plot_model(type = "pred", terms = ~ rx, dot.size = 1.5, title = "") %>%
gf_labs(y = "Survival time", x = "Treatment", title = "")
## -----------------------------------------------------------------------------
model_cox <- coxph(Surv(times, event) ~ rx, data = bladder)
## -----------------------------------------------------------------------------
model_cox %>%
tbl_regression(exponentiate = TRUE) %>%
cosm_reg() %>% theme_pubh() %>%
add_footnote(get_r2(model_cox), font_size = 9)
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