# 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$Catholic,
family = binomial(link = "logit"))
# plot multiple models
sjp.glmm(fitOR1, fitOR2, fitOR3, facet.grid = TRUE)## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
# plot multiple models with legend labels and point shapes instead of value labels
sjp.glmm(fitOR1, fitOR2, fitOR3,
labelDependentVariables = c("Fertility",
"Infant Mortality",
"Agriculture"),
showValueLabels = FALSE,
showPValueLabels = FALSE,
fade.ns = TRUE,
usePShapes = TRUE)## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
# plot multiple models from nested lists argument
all.models <- list()
all.models[[1]] <- fitOR1
all.models[[2]] <- fitOR2
all.models[[3]] <- fitOR3
sjp.glmm(all.models)## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead
# -------------------------------
# Predictors for negative impact
# of care. Data from the EUROFAMCARE
# sample dataset
# -------------------------------
library(sjmisc)
data(efc)
# create binary response
y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)
# create dummy variables for educational status
edu.mid <- ifelse(efc$c172code == 2, 1, 0)
edu.high <- ifelse(efc$c172code == 3, 1, 0)
# create data frame for fitted model
mydat <- data.frame(y = as.factor(y),
sex = as.factor(efc$c161sex),
dep = as.factor(efc$e42dep),
barthel = as.numeric(efc$barthtot),
edu.mid = as.factor(edu.mid),
edu.hi = as.factor(edu.high))
fit1 <- glm(y ~ sex + edu.mid + edu.hi,
data = mydat,
family = binomial(link = "logit"))
fit2 <- update(fit1, . ~ . + barthel)
fit3 <- update(fit2, . ~ . + dep)
sjp.glmm(fit1, fit2, fit3)## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## ymax not defined: adjusting position using y instead
## ymax not defined: adjusting position using y instead