sjPlot (version 2.0.0)

sjp.lm: Plot estimates, predictions or effects of linear models

Description

Depending on the type, this function plots coefficients (estimates) of linear regressions (including panel models fitted with the plm-function from the plm-package and generalized least squares models fitted with the gls-function from the nlme-package) with confidence intervals as dot plot (forest plot), model assumptions for linear models or slopes and scatter plots for each single coefficient. See type for details.

Usage

sjp.lm(fit, type = "lm", vars = NULL, group.estimates = NULL,
  remove.estimates = NULL, sort.est = TRUE, poly.term = NULL,
  title = NULL, legend.title = NULL, axis.labels = NULL,
  axis.title = NULL, resp.label = NULL, geom.size = NULL,
  geom.colors = "Set1", point.alpha = 0.2, scatter.plot = TRUE,
  wrap.title = 50, wrap.labels = 25, axis.lim = NULL,
  grid.breaks = NULL, show.values = TRUE, show.p = TRUE, show.ci = TRUE,
  show.legend = FALSE, show.loess = FALSE, show.loess.ci = FALSE,
  show.summary = FALSE, digits = 2, vline.type = 2,
  vline.color = "grey70", coord.flip = TRUE, y.offset = 0.15,
  facet.grid = TRUE, completeDiagnostic = FALSE, prnt.plot = TRUE, ...)

Value

Depending on the type, in most cases (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). For type = "ma", an updated model with removed outliers is returned.

Details

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

Gelman A (2008) "Scaling regression inputs by dividing by two standard deviations." Statistics in Medicine 27: 2865–2873. http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf Hyndman RJ, Athanasopoulos G (2013) "Forecasting: principles and practice." OTexts; accessed from https://www.otexts.org/fpp/5/4.

See Also

http://www.strengejacke.de/sjPlot/sjp.lm{sjPlot manual: sjp.lm} for more details and examples of this function; use sjp.poly to see which polynomial degree fits best for possible polynomial terms.

Examples

# --------------------------------------------------
# plotting estimates of linear models as forest plot
# --------------------------------------------------
# fit linear model
fit <- lm(airquality$Ozone ~ airquality$Wind + airquality$Temp + airquality$Solar.R)

# plot estimates with CI
sjp.lm(fit, grid.breaks = 2)

# plot estimates with CI
# and with narrower tick marks
# (because "grid.breaks" was not specified)
sjp.lm(fit)

# ---------------------------------------------------
# plotting regression line of linear model (done
# automatically if fitted model has only 1 predictor)
# ---------------------------------------------------
library(sjmisc)
data(efc)
# fit model
fit <- lm(neg_c_7 ~ quol_5, data=efc)
# plot regression line with label strings
sjp.lm(fit, resp.label = "Burden of care",
       axis.labels = "Quality of life", show.loess = TRUE)

# --------------------------------------------------
# plotting regression lines of each single predictor
# of a fitted model
# --------------------------------------------------
library(sjmisc)
data(efc)
# fit model
fit <- lm(tot_sc_e ~ c12hour + e17age + e42dep, data=efc)

# reression line and scatter plot
sjp.lm(fit, type = "slope")

# reression line w/o scatter plot
sjp.lm(fit, type = "slope", scatter.plot = FALSE)

# --------------------------
# plotting model assumptions
# --------------------------
sjp.lm(fit, type = "ma")

# --------------------------
# grouping estimates
# --------------------------
library(sjmisc)
data(efc)
fit <- lm(barthtot ~ c160age + e17age + c12hour + e16sex + c161sex + c172code,
          data = efc)

# order estimates according to coefficient's order
sjp.lm(fit, group.estimates = c(1, 1, 2, 3, 3, 4),
       geom.colors = c("green", "red", "blue", "grey"), sort.est = FALSE)

fit <- lm(barthtot ~ c160age + c12hour + e17age+ c161sex + c172code + e16sex,
          data = efc)

# force order of estimates according to group assignment
sjp.lm(fit, group.estimates = c(1, 2, 1, 3, 4, 3),
       geom.colors = c("green", "red", "blue", "grey"), sort.est = TRUE)

# --------------------------
# predicted values for response
# --------------------------
library(sjmisc)
data(efc)
efc$education <- to_label(to_factor(efc$c172code))
fit <- lm(barthtot ~ c160age + c12hour + e17age+ education,
          data = efc)

sjp.lm(fit, type = "pred", vars = "c160age")

# with loess
sjp.lm(fit, type = "pred", vars = "e17age", show.loess = TRUE)

# grouped
sjp.lm(fit, type = "pred", vars = c("c12hour", "education"))

# grouped, non-facet
sjp.lm(fit, type = "pred", vars = c("c12hour", "education"),
       facet.grid = FALSE)

# --------------------------
# plotting polynomial terms
# --------------------------
library(sjmisc)
data(efc)
# fit sample model
fit <- lm(tot_sc_e ~ c12hour + e17age + e42dep, data = efc)
# "e17age" does not seem to be linear correlated to response
# try to find appropiate polynomial. Grey line (loess smoothed)
# indicates best fit. Looks like x^3 has a good fit.
# (not checked for significance yet).
sjp.poly(fit, "e17age", 2:4, scatter.plot = FALSE)
# fit new model
fit <- lm(tot_sc_e ~ c12hour + e42dep +
          e17age + I(e17age^2) + I(e17age^3),
          data = efc)
# plot marginal effects of polynomial term
sjp.lm(fit, type = "poly", poly.term = "e17age")

library(splines)
# fit new model with "splines"-package, "bs"
fit <- lm(tot_sc_e ~ c12hour + e42dep + bs(e17age, 3), data = efc)
# plot marginal effects of polynomial term, same call as above
sjp.lm(fit, type = "poly", poly.term = "e17age")