Nothing
## ----message=FALSE------------------------------------------------------------
library(datasets)
data(mtcars)
mtcars$am <- factor(mtcars$am, labels = c("Automatic", "Manual"))
fit <- lm(mpg ~ cyl + disp + hp + am, data = mtcars)
library(Greg)
printCrudeAndAdjustedModel(fit)
## -----------------------------------------------------------------------------
printCrudeAndAdjustedModel(fit,
digits = 1,
add_references = TRUE,
rowname.fn = function(n){
if (n == "disp")
return("Displacement (cu.in.)")
if (n == "hp")
return("Gross horsepower")
if (n == "cyl")
return("No. cylinders")
if (n == "am")
return("Transmission")
return(n)
})
## ----message=FALSE------------------------------------------------------------
library(Hmisc)
label(mtcars$disp) <- "Displacement (cu.in)"
label(mtcars$cyl) <- "No. cylinders"
label(mtcars$hp) <- "Gross horsepower"
label(mtcars$am) <- "Transmission"
printCrudeAndAdjustedModel(fit,
digits = 1,
add_references = TRUE)
## ----styling_with_addHtmlTableStyle-------------------------------------------
library(htmlTable)
printCrudeAndAdjustedModel(fit,
digits = 1,
add_references = TRUE) |>
# We can also style the output as shown here
addHtmlTableStyle(css.rgroup = "")
## ----theming------------------------------------------------------------------
setHtmlTableTheme(css.rgroup = "")
## -----------------------------------------------------------------------------
fit_mpg <- lm(mpg ~ cyl + disp + hp + am, data = mtcars)
fit_weight <- lm(wt ~ cyl + disp + hp + am, data = mtcars)
p_mpg <- printCrudeAndAdjustedModel(fit_mpg, digits = 1, add_references = TRUE)
p_weight <- printCrudeAndAdjustedModel(fit_weight, digits = 1, add_references = TRUE)
rbind("Miles per gallon" = p_mpg,
"Weight (1000 lbs)" = p_weight)
cbind("Miles per gallon" = p_mpg,
"Weight (1000 lbs)" = p_weight)
## -----------------------------------------------------------------------------
p_mpg[,1:2]
p_mpg[1:2,]
## -----------------------------------------------------------------------------
library("survival")
set.seed(10)
n <- 500
ds <- data.frame(
ftime = rexp(n),
fstatus = sample(0:1, size = n, replace = TRUE),
y = rnorm(n = n),
x1 = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
x2 = rnorm(n, mean = 3, 2),
x3 = rnorm(n, mean = 3, 2),
x4 = factor(sample(letters[1:3], size = n, replace = TRUE)),
stringsAsFactors = FALSE)
library(survival)
library(splines)
fit <- coxph(Surv(ds$ftime, ds$fstatus == 1) ~ x1 + ns(x2, 4) + x3 + strata(x4), data = ds)
printCrudeAndAdjustedModel(fit, add_references = TRUE)
## -----------------------------------------------------------------------------
# Note that the crude is with the strata
a <- getCrudeAndAdjustedModelData(fit)
a["x3", "Crude"] == exp(coef(coxph(Surv(ds$ftime, ds$fstatus == 1) ~ x3 + strata(x4), data = ds)))
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