Summary of Linear Regression Models as HTML Table

Linear model summaries as HTML table

The sjt.lm() function prints summaries of linear models (fitted with the lm() function) as nicely formatted html-tables.

Before starting, sample data is loaded and sample models are fitted:

# fit first model
fit1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)
# fit second model 
fit2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + c172code, data = efc)
# Note that both models share the same predictors and only differ 
# in their dependent variable. See examples of stepwise models 
# later...

The simplest way of producing the table output is by passing the fitted models as parameter. By default, estimates (B), confidence intervals (CI) and p-values (p) are reported. The models are named Model 1 and Model 2.

sjt.lm(fit1, fit2)

sjt.lm(fit1, fit2, depvar.labels = c("Barthel-Index", "Negative Impact"))

sjt.lm(
  fit1,
  fit2,
  show.header = TRUE,
  string.est = "Estimate",
  string.ci = "Conf. Int.",
  string.p = "p-value",
  string.dv = "Response",
  string.pred = "Coefficients",
  string.interc = "Konstante",
  depvar.labels = c("Barthel-Index", "Negative Impact")
)

Changing summary style and content

You can change the table style with specific parameters, e.g. to include CI into the same table cell as the estimates, print asterisks instead of numeric p-values etc.

sjt.lm(
  fit1,
  fit2,
  separate.ci.col = FALSE, # ci in same cell as estimates
  show.std = TRUE,         # also show standardized beta values
  p.numeric = FALSE        # "*" instead of numeric values
)       

Custom variable labels

In the above example, the original variable labels are long and not so pretty. You can change variable labels either with sjmisc::set_label(), which will affect all future plots and tables, or pass own labels via pred.labels.

sjt.lm(fit1, fit2, pred.labels = c("Carer's Age", "Hours of Care", "Carer's Sex", "Educational Status"))

Compare models with different predictors

In some cases, for instance stepwise regressions, you have different predictors on the same response. The proper grouping of predictors, resp. rows, is done automatically.

First, let’s fit some example models.

# fit first model
fit1 <- lm(neg_c_7 ~ c160age + c172code + c161sex, data = efc)
# fit second model
fit2 <- lm(neg_c_7 ~ c160age + c172code + c161sex + c12hour, data = efc)
# fit second model
fit3 <- lm(neg_c_7 ~ c160age + c172code + e42dep + tot_sc_e, data = efc)

Note that printing tables with fitted models, which have different predictors do not automatically detect variable labels (maybe this will be implemented in a future package version).

sjt.lm(fit1, fit2, fit3, 
       separate.ci.col = FALSE,
       show.aic = TRUE,
       show.fstat = TRUE)

More space bewteen model columns

Especially when fitting and summarizing some more models, it might help to increase the distance between the columns that separate the models. This can be done by tweaking the css.separatorcol style-sheet:

sjt.lm(fit1, fit2, fit3, CSS = list(css.separatorcol = 'padding-right:1.5em; padding-left:1.5em;'))

Automatic grouping of categorical predictors

In case you have categorical variables with more than two factor levels, the sjt.lm() function automatically groups the category levels to give a better overview of predictors in the table.

By default, automatic grouping is activated. To disable this feature, use group.pred = FALSE as parameter.

To demonstrate this feature, we first convert two predictors to factors (what they actually are, indeed). To do this, we use the sjmisc::to_factor() function, which converts numerical variables into factors, however, does not remove the variable and value label attributes.

# make education categorical
efc$c172code <- to_factor(efc$c172code)
# make dependency categorical
efc$e42dep <- to_factor(efc$e42dep)
# fit first model again (with c172code as factor)
fit1 <- lm(barthtot ~ c160age + c12hour + c172code + c161sex + e42dep, data = efc)
# fit second model again (with c172code as factor)
fit2 <- lm(neg_c_7 ~ c160age + c12hour + c172code + c161sex + e42dep, data = efc)

Now we can print the table.

sjt.lm(fit1, fit2)

Removing estimates from the output

With remove.estimates, specific estimates can be removed from the table output. This may make sense in case you have stepwise regression models and only want to compare the varying predictors but not the controls. remove.estimates either accepts the row indices of the rows of the table output that should be removed, or the coefficient’s names.

When using numeric indices, the estimates’ index number relates to the same order as coef(fit).

Note that automatic grouping of categorical predictors (argument group.pred) does not yet work properly when removing estimates! See also Example 6 for further details.

data(efc)
# make education categorical
efc$c172code <- to_factor(efc$c172code)
# make education categorical
efc$e42dep <- to_factor(efc$e42dep)
# make prettier variable labels
set_label(efc$c172code) <- "Education"
set_label(efc$e42dep) <- "Dependency"
# fit first model
fit1 <- lm(neg_c_7 ~ c160age + c172code + c161sex, data = efc)
# fit second model
fit2 <- lm(neg_c_7 ~ c160age + c172code + c161sex + c12hour, data = efc)
# fit third model
fit3 <- lm(neg_c_7 ~ c160age + c172code + e42dep + tot_sc_e, data = efc)

sjt.lm(fit1, fit2, fit3, group.pred = FALSE)

sjt.lm(fit1, fit2, fit3, group.pred = FALSE, CSS = list(css.topcontentborder = "+font-size: 0px;"))

sjt.lm(fit1, fit2, fit3, group.pred = FALSE, remove.estimates = 2)

sjt.lm(fit1, fit2, fit3, group.pred = FALSE, remove.estimates = c("c160age", "c161sex"))

sjt.lm(fit1, fit2, fit3, group.pred = FALSE, remove.estimates = c(2,5,6,10))

sjt.lm(fit1, fit2, fit3,
       pred.labels = c("mid education", "hi education", "Hours of Care", 
                       "slight dependency", "moderate dependency", 
                       "severe dependency", "Service Usage"),
       remove.estimates = c("c160age", "c161sex"))