sjPlot (version 1.8.3)

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(..., file = NULL, labelPredictors = NULL,
  labelDependentVariables = NULL, stringPredictors = "Predictors",
  stringDependentVariables = "Dependent Variables",
  showHeaderStrings = FALSE, stringModel = "Model",
  stringIntercept = "(Intercept)", stringObservations = "Observations",
  stringOR = "OR", stringCI = "CI", stringSE = "std. Error",
  stringP = "p", digits.est = 2, digits.p = 3, digits.ci = 2,
  digits.se = 2, digits.summary = 3, exp.coef = TRUE,
  pvaluesAsNumbers = TRUE, boldpvalues = TRUE, showConfInt = TRUE,
  showStdError = FALSE, ci.hyphen = " – ",
  separateConfColumn = TRUE, newLineConf = TRUE, group.pred = TRUE,
  showAbbrHeadline = TRUE, showPseudoR = FALSE, showLogLik = FALSE,
  showAIC = FALSE, showAICc = FALSE, showChi2 = FALSE,
  showHosLem = FALSE, showFamily = FALSE, remove.estimates = NULL,
  cellSpacing = 0.2, cellGroupIndent = 0.6, encoding = NULL, CSS = NULL,
  useViewer = 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
y1 <- ifelse(swiss$Fertility < median(swiss$Fertility), 0, 1)
y2 <- ifelse(swiss$Infant.Mortality < median(swiss$Infant.Mortality), 0, 1)
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 ~ swiss$Education + swiss$Examination+swiss$Catholic,
              family = binomial(link = "logit"))
fitOR2 <- glm(y2 ~ swiss$Education + swiss$Examination+swiss$Catholic,
              family = binomial(link = "logit"))
fitOR3 <- glm(y3 ~ swiss$Education + swiss$Examination+swiss$Catholic,
              family = binomial(link = "logit"))

# open HTML-table in RStudio Viewer Pane or web browser
sjt.glm(fitOR1,
        fitOR2,
        labelDependentVariables = c("Fertility",
                                    "Infant Mortality"),
        labelPredictors = 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,
        labelDependentVariables = c("Fertility",
                                    "Infant Mortality",
                                    "Agriculture"),
        labelPredictors = c("Education", "Examination", "Catholic"),
        separateConfColumn = 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,
        labelDependentVariables = c("Fertility",
                                    "Infant Mortality",
                                    "Agriculture"),
        labelPredictors = c("Education", "Examination", "Catholic"))


# --------------------------------------------
# User defined style sheet
# --------------------------------------------
sjt.glm(fitOR1, fitOR2, fitOR3,
        labelDependentVariables = c("Fertility",
                                    "Infant Mortality",
                                    "Agriculture"),
        labelPredictors = c("Education", "Examination", "Catholic"),
        showHeaderStrings = 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,
        showAIC = TRUE,
        showFamily = 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,
        pvaluesAsNumbers = FALSE,
        showAIC = TRUE,
        showFamily = 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,
        pvaluesAsNumbers = FALSE,
        separateConfColumn = FALSE,
        showAIC = TRUE,
        showFamily = TRUE,
        showPseudoR = 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$e42dep <- to_fac(efc$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$c172code <- to_fac(efc$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)