# Not run: require ( 'datasets' ) # prediction from several angles m <- lm ( Sepal.Length ~ Sepal.Width, data = iris ) cplot ( m ) # marginal effect of 'Petal.Width' across 'Petal.Width' m <- lm ( Sepal.Length ~ Sepal.Width * Petal.Width * I ( Petal.Width ^ 2 ), data = head ( iris, 50 )) cplot ( m, x = "Petal.Width", what = "effect", n = 10 ) # factor independent variables mtcars ] <- factor ( mtcars ] ) m <- lm ( mpg ~ am * wt, data = mtcars ) # predicted values for each factor level cplot ( m, x = "am" ) # marginal effect of each factor level across numeric variable cplot ( m, x = "wt", dx = "am", what = "effect" ) # non-linear model m <- glm ( am ~ wt * drat, data = mtcars, family = binomial ) cplot ( m, x = "wt", type = 'response' ) # prediction (response scale) cplot ( m, x = "wt", type = 'link' ) # prediction (link scale) # marginal effect of 'Petal.Width' across 'Sepal.Width' # without drawing the plot # this might be useful if you want even more control over the plots tmp <- cplot ( m, x = "Sepal.Width", dx = "Petal. The overall aesthetic is somewhat similar to to the output produced by theĪ ggplot2 object. Show ways of constraining confidence intervals to these bounds. (again, for logistic regression this means confidence intervals can exceed When examining generalized linear models (e.g., logistic regression models),Ĭonfidence intervals for predictions can fall outside of the response scale What = "effect" average marginal effects (i.e., at observed values) Predictions holding values of the data at their mean or mode, whereas when Note that when what = "prediction", the plots show If 'x' is numeric, these argumentsĪrguments are not passed to geom_line. If 'x' isĪ factor, these arguments will be passed to To ggplot2 geom functions to alter the style of the plot. Logical include a rugplot at the bottom of the graphĪdditional arguments such as colour, linetype, To calculate the predicted value or marginal effect, when x is Name of the third dimension variable over which quantities shouldĭiscrete values of the z variable over which to plotĪn integer specifying the number of points across x at which
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The resulting data is added to the existing plot.Ī numeric vector of values at which to calculate predictions or This might be useful if you want to plot using an alternative If FALSE, the data used in drawing are returned as a list ofĭata.frames. The confidence level required (used to draw uncertaintyĪ logical (default TRUE), specifying whether to draw the The variance-covariance matrix used to calculate confidence intervalsĭata.frame over which to calculate individual marginal effects The quantity to plot: 'prediction', 'effect', 'classprediction', The name of the variable whose effect should be plotted The name of the variable to show on the x-axis There are no ads in this search engine enabler service.Cplot ( object, x = NULL, dx = NULL, what = c ( "prediction", "effect", "classprediction", "stackedprediction" ), type = c ( "response", "link" ), vcov = stats :: vcov ( object ), data = NULL, level = 0.95, draw = TRUE, xvals = NULL, z = NULL, zvals = NULL, n = 25, rugplot = TRUE, at = NULL. ℹ️About GitHub Wiki SEE, a search engine enabler for GitHub WikisĪs GitHub blocks most GitHub Wikis from search engines.
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cplot(f,minX,maxX,minY,maxY)ĬomplexPlot Example cplot(sin, 0, 2 * pi, -1, 1)ĭraws the complex plot of the sine function between 0 and 2pi on the real axis and -1 and 1 on the imaginary axes. Visualizes the given complex function f between the given values for the real and the imaginary axes.ĬomplexPlot Example cplot(sin, 0 - 1i, 2 * pi + 1i)ĭraws the complex plot of the sine function between 0 and 2pi on the real axis and -1 and 1 on the imaginary axes. Example cplot(z => sin(z) * cos(z))ĭraws the complex plot of sin(z) * cos(z) with z = x + iy. The margins and prediction packages are a combined effort to port the functionality of Statas (closed source) margins command to (open source) R.
![r help cplot r help cplot](https://bookdown.org/curleyjp0/psy317l_guides/img/r1.png)
Visualizes the given complex function f between -1 and 1 for the real and the imaginary axes.ĭraws the complex plot of the sine function sin(z) with z = x + iy. the origin is white, 1 is red, −1 is cyan, and a point at the infinity is black. The complex plot assigns a color to each point of complex plane. This can be done in a number of ways, as described on this page.In this case, we’ll use the summarySE() function defined on that page, and also at the bottom of this page.
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In mathematics, a complex function is a function with the complex numbers (see the imaginary numbers and the complex plane) as both its domain and codomain. First, it is necessary to summarize the data.