Source: R/sjPlotPolynomials.R
sjp.poly.Rd
This function plots a scatter plot of a term poly.term against a response variable x and adds - depending on the amount of numeric values in poly.degree - multiple polynomial curves. A loess-smoothed line can be added to see which of the polynomial curves fits best to the data.
sjp.poly(x, poly.term, poly.degree, poly.scale = FALSE, fun = NULL, axis.title = NULL, geom.colors = NULL, geom.size = 0.8, show.loess = TRUE, show.loess.ci = TRUE, show.p = TRUE, show.scatter = TRUE, point.alpha = 0.2, point.color = "#404040", loess.color = "#808080", prnt.plot = TRUE)
Arguments
| x | A vector, representing the response variable of a linear (mixed) model; ora linear (mixed) model as returned by |
|---|---|
| poly.term | If |
| poly.degree | Numeric, or numeric vector, indicating the degree of the polynomial.If |
| poly.scale | Logical, if |
| fun | Linear function when modelling polynomial terms. Use |
| axis.title | Character vector of length one or two (depending onthe plot function and type), used as title(s) for the x and y axis.If not specified, a default labelling is chosen. To set multipleaxis titles (e.g. with |
| geom.colors | User defined color palette for geoms. If |
| geom.size | size resp. width of the geoms (bar width, line thickness or point size,depending on plot type and function). Note that bar and bin widths mostlyneed smaller values than dot sizes. |
| show.loess | Logical, if |
| show.loess.ci | Logical, if |
| show.p | Logical, if |
| show.scatter | Logical, if |
| point.alpha | Alpha value of point-geoms in the scatter plots. Only applies,if |
| point.color | Color of of point-geoms in the scatter plots. Only applies,if |
| loess.color | Color of the loess-smoothed line. Only applies, if |
| prnt.plot | logical, if |
Value
(Insisibily) returns
plotthe ggplot-object with the complete plot
dfthe data frame that was used for setting up the ggplot-object
cutpointsa data frame that indicates x-values and predicted y-values of each direction change in the loess curvature
Details
For each polynomial degree, a simple linear regression on x (resp. the extracted response, if x is a fitted model) is performed, where only the polynomial term poly.term is included as independent variable. Thus, lm(y ~ x + I(x^2) + ... + I(x^i)) is repeatedly computed for all values in poly.degree, and the predicted values of the reponse are plotted against the raw values of poly.term. If x is a fitted model, other covariates are ignored when finding the best fitting polynomial.
This function evaluates raw polynomials, not orthogonal polynomials. Polynomials are computed using the poly function, with argument raw = TRUE.
To find out which polynomial degree fits best to the data, a loess-smoothed line (in dark grey) can be added (with show.loess = TRUE). The polynomial curves that comes closest to the loess-smoothed line should be the best fit to the data.
See also
To plot marginal effects of polynomial terms, call sjp.lm with type = "poly", or sjp.lmer respectively for linear mixed models.
Examples
library(sjmisc)data(efc)# linear fit. loess-smoothed line indicates a more# or less cubic curvesjp.poly(efc$c160age, efc$quol_5, 1)
#> Polynomial degrees: 1#> ---------------------#> p(x^1): 0.000#>
# quadratic fitsjp.poly(efc$c160age, efc$quol_5, 2)
#> Polynomial degrees: 2#> ---------------------#> p(x^1): 0.078#> p(x^2): 0.533#>
# linear to cubic fitsjp.poly(efc$c160age, efc$quol_5, 1:4, show.scatter = FALSE)
#> Polynomial degrees: 1#> ---------------------#> p(x^1): 0.000#> #> Polynomial degrees: 2#> ---------------------#> p(x^1): 0.078#> p(x^2): 0.533#> #> Polynomial degrees: 3#> ---------------------#> p(x^1): 0.012#> p(x^2): 0.001#> p(x^3): 0.000#> #> Polynomial degrees: 4#> ---------------------#> p(x^1): 0.777#> p(x^2): 0.913#> p(x^3): 0.505#> p(x^4): 0.254#>
# fit sample modelfit <- lm(tot_sc_e ~ c12hour + e17age + e42dep, data = efc)# inspect relationship between predictors and responsesjp.lm(fit, type = "slope", show.loess = TRUE, show.scatter = FALSE)
#> Warning: 'sjp.lm' is deprecated.#> Use 'plot_model' instead.#> See help("Deprecated")
#> This plot type has been removed, as it was misleading. Use `plot_model` with `type = "pred"` to plot marginal effects, or with `type = "slope"` for model diagnostisc.
# "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^4 has the best fit,# however, only x^3 has significant p-values.sjp.poly(fit, "e17age", 2:4, show.scatter = FALSE)
#> Polynomial degrees: 2#> ---------------------#> p(x^1): 0.734#> p(x^2): 0.721#> #> Polynomial degrees: 3#> ---------------------#> p(x^1): 0.010#> p(x^2): 0.011#> p(x^3): 0.011#> #> Polynomial degrees: 4#> ---------------------#> p(x^1): 0.234#> p(x^2): 0.267#> p(x^3): 0.303#> p(x^4): 0.343#>
# NOT RUN {# fit new modelfit <- lm(tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3), data = efc)# plot marginal effects of polynomial termsjp.lm(fit, type = "poly", poly.term = "e17age")# }
