sjPlot (version 1.7)

sjp.glmm: Plot odds ratios (forest plots) of multiple fitted glm's

Description

Plot odds ratios (forest plots) of multiple fitted glm's with confidence intervalls in one plot.

Usage

sjp.glmm(..., title = NULL, labelDependentVariables = NULL,
  legendDepVarTitle = "Dependent Variables", legendPValTitle = "p-level",
  stringModel = "Model", axisLabels.y = NULL, axisTitle.x = "Odds Ratios",
  axisLimits = NULL, breakTitleAt = 50, breakLabelsAt = 25,
  breakLegendAt = 20, gridBreaksAt = 0.5, transformTicks = FALSE,
  geom.size = 3, geom.spacing = 0.4, geom.colors = "Dark2", nsAlpha = 1,
  usePShapes = FALSE, interceptLineType = 2,
  interceptLineColor = "grey70", coord.flip = TRUE, showIntercept = FALSE,
  showAxisLabels.y = TRUE, showValueLabels = TRUE, labelDigits = 2,
  showPValueLabels = TRUE, hideLegend = FALSE, facet.grid = FALSE,
  printPlot = TRUE)

Value

(Insisibily) returns the ggplot-object with the complete plot (plot) as well as the data frame that was used for setting up the ggplot-object (df).

See Also

• sjp.glm
• sjt.glm
• sjp.lmm

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 all 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"))

# plot multiple models
sjp.glmm(fitOR1, fitOR2, fitOR3, facet.grid=TRUE)

# plot multiple models with legend labels and point shapes instead of value  labels
sjp.glmm(fitOR1, fitOR2, fitOR3,
         labelDependentVariables=c("Fertility", "Infant Mortality", "Agriculture"),
         showValueLabels=FALSE,
         showPValueLabels=FALSE,
         usePShapes=TRUE,
         nsAlpha=0.2)