sjPlot (version 2.0.0)

sjt.glm: Summary of generalized linear models as HTML table

Description

Summarizes (multiple) fitted generalized linear models (odds ratios, ci, p-values...) as HTML table, or saves them as file. The fitted models may have different predictors, e.g. when comparing different stepwise fitted models.

Usage

sjt.glm(..., pred.labels = NULL, depvar.labels = NULL,
  remove.estimates = NULL, group.pred = TRUE, exp.coef = TRUE,
  p.numeric = TRUE, emph.p = TRUE, separate.ci.col = TRUE,
  newline.ci = TRUE, show.ci = TRUE, show.se = FALSE,
  show.header = FALSE, show.col.header = TRUE, show.r2 = FALSE,
  show.icc = FALSE, show.re.var = FALSE, show.loglik = FALSE,
  show.aic = FALSE, show.aicc = FALSE, show.dev = FALSE,
  show.hoslem = FALSE, show.family = FALSE, show.chi2 = FALSE,
  string.pred = "Predictors", string.dv = "Dependent Variables",
  string.interc = "(Intercept)", string.obs = "Observations",
  string.est = "OR", string.ci = "CI", string.se = "std. Error",
  string.p = "p", ci.hyphen = " – ", digits.est = 2,
  digits.p = 3, digits.ci = 2, digits.se = 2, digits.summary = 3,
  cell.spacing = 0.2, cell.gpr.indent = 0.6, CSS = NULL,
  encoding = NULL, file = NULL, use.viewer = TRUE, no.output = FALSE,
  remove.spaces = TRUE)

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (output.complete) and
• the html-table with inline-css for use with knitr (knitr)

for further use.

Details

See 'Details' in sjt.frq.

Examples

# prepare dummy variables for binary logistic regression
swiss$y1 <- ifelse(swiss$Fertility < median(swiss$Fertility), 0, 1)
swiss$y2 <- ifelse(swiss$Infant.Mortality < median(swiss$Infant.Mortality), 0, 1)
swiss$y3 <- ifelse(swiss$Agriculture < median(swiss$Agriculture), 0, 1)

# Now fit the models. Note that both models share the same predictors
# and only differ in their dependent variable (y1, y2 and y3)
fitOR1 <- glm(y1 ~ Education + Examination + Catholic, data = swiss,
              family = binomial(link = "logit"))
fitOR2 <- glm(y2 ~ Education + Examination + Catholic, data = swiss,
              family = binomial(link = "logit"))
fitOR3 <- glm(y3 ~ Education + Examination + Catholic, data = swiss,
              family = binomial(link = "logit"))

# open HTML-table in RStudio Viewer Pane or web browser
sjt.glm(fitOR1, fitOR2, 
        depvar.labels = c("Fertility", "Infant Mortality"),
        pred.labels = c("Education", "Examination", "Catholic"),
        ci.hyphen = " to ")

# open HTML-table in RStudio Viewer Pane or web browser,
# integrate CI in OR column
sjt.glm(fitOR1, fitOR2, fitOR3,
        pred.labels = c("Education", "Examination", "Catholic"),
        separate.ci.col = FALSE)

# open HTML-table in RStudio Viewer Pane or web browser,
# indicating p-values as numbers and printing CI in a separate column
sjt.glm(fitOR1, fitOR2, fitOR3,
        depvar.labels = c("Fertility", "Infant Mortality", "Agriculture"),
        pred.labels = c("Education", "Examination", "Catholic"))

# -------------------------------------------- 
# User defined style sheet
# -------------------------------------------- 
sjt.glm(fitOR1, fitOR2, fitOR3,
        depvar.labels = c("Fertility", "Infant Mortality", "Agriculture"),
        pred.labels = c("Education", "Examination", "Catholic"),
        show.header = TRUE,
        CSS = list(css.table = "border: 2px solid;",
                   css.tdata = "border: 1px solid;",
                   css.depvarhead = "color:#003399;"))

# -------------------------------------------- 
# Compare models with different link functions, 
# but same predictors and response
# -------------------------------------------- 
library(sjmisc)
# load efc sample data
data(efc)
# dichtomozize service usage by "service usage yes/no"
efc$services <- sjmisc::dicho(efc$tot_sc_e, "v", 0, as.num = TRUE)
# fit 3 models with different link-functions
fit1 <- glm(services ~ neg_c_7 + c161sex + e42dep, 
            data = efc, family = binomial(link = "logit"))
fit2 <- glm(services ~ neg_c_7 + c161sex + e42dep, 
            data = efc, family = binomial(link = "probit"))
fit3 <- glm(services ~ neg_c_7 + c161sex + e42dep, 
            data = efc, family = poisson(link = "log"))
            
# compare models
sjt.glm(fit1, fit2, fit3, show.aic = TRUE, show.family = TRUE)

# --------------------------------------------
# Change style of p-values and CI-appearance
# --------------------------------------------
# open HTML-table in RStudio Viewer Pane or web browser,
# table indicating p-values as stars
sjt.glm(fit1, fit2, fit3, p.numeric = FALSE,
        show.aic = TRUE, show.family = TRUE)

# open HTML-table in RStudio Viewer Pane or web browser,
# indicating p-values as stars and integrate CI in OR column
sjt.glm(fit1, fit2, fit3, p.numeric = FALSE, separate.ci.col = FALSE,
        show.aic = TRUE, show.family = TRUE, show.r2 = TRUE)

# ---------------------------------- 
# automatic grouping of predictors
# ---------------------------------- 
library(sjmisc)
# load efc sample data
data(efc)
# dichtomozize service usage by "service usage yes/no"
efc$services <- sjmisc::dicho(efc$tot_sc_e, "v", 0, as.num = TRUE)
# make dependency categorical
efc$e42dep <- to_factor(efc$e42dep)
# fit model with "grouped" predictor
fit <- glm(services ~ neg_c_7 + c161sex + e42dep, data = efc)

# automatic grouping of categorical predictors
sjt.glm(fit)

# ---------------------------------- 
# compare models with different predictors
# ---------------------------------- 
fit2 <- glm(services ~ neg_c_7 + c161sex + e42dep + c12hour, data = efc)
fit3 <- glm(services ~ neg_c_7 + c161sex + e42dep + c12hour + c172code, 
            data = efc)

# print models with different predictors
sjt.glm(fit, fit2, fit3)

efc$c172code <- to_factor(efc$c172code)
fit2 <- glm(services ~ neg_c_7 + c161sex + c12hour, data = efc)
fit3 <- glm(services ~ neg_c_7 + c161sex + c172code, data = efc)

# print models with different predictors
sjt.glm(fit, fit2, fit3, group.pred = FALSE)