sjp.lm: Plot estimates or predicted values 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) 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", sort.est = TRUE, title = NULL,
  axisLabels.x = NULL, axisLabels.y = NULL, showAxisLabels.y = TRUE,
  axisTitle.x = "Estimates", axisLimits = NULL, geom.colors = "Set1",
  geom.size = NULL, interceptLineType = 2, interceptLineColor = "grey70",
  remove.estimates = NULL, breakTitleAt = 50, breakLabelsAt = 25,
  gridBreaksAt = NULL, coord.flip = TRUE, showValueLabels = TRUE,
  labelDigits = 2, showPValueLabels = TRUE, showModelSummary = FALSE,
  showCI = TRUE, pointAlpha = 0.2, showScatterPlot = TRUE,
  showLoess = FALSE, showLoessCI = FALSE, poly.term = NULL,
  showOriginalModelOnly = TRUE, completeDiagnostic = FALSE,
  printPlot = TRUE)

Arguments

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

Examples

Run this code

# --------------------------------------------------
# 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, gridBreaksAt = 2)

# plot estimates with CI
# and with narrower tick marks
# (because "gridBreaksAt" 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,
       axisLabels.x = "Quality of life",
       axisLabels.y = "Burden of care",
       showLoess = 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 = "pred")

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

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

# --------------------------
# 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, showScatterPlot = 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")

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning