Octave cplot1/5/2024 ![]() Geom_line(aes(y = effect + 1.96 *se. # use ggplot2 instead of base graphics ggplot(tmp, aes(x = Petal.Width, y = "effect" )) + Octave has lots of simple tools that we can use for a better understanding of our algorithm. Whenever we perform a learning algorithm on an Octave environment, we can get a better sense of that algorithm and analyze it. Few simple plots can give us a better way to understand our data. What = "effect", n = 10, draw = FALSE ) Octave has some in-built functions for visualizing the data. # marginal effect of 'Petal.Width' across 'Sepal.Width' # without drawing the plot # this might be useful for using, e.g., ggplot2 for plotting tmp <- cplot(m, x = "Sepal.Width", dx = "Petal.Width" , # marginal effect of each factor level across numeric variable cplot(m, x = "wt", dx = "am", what = "effect" ) # predicted values for each factor level cplot(m, x = "am" ) # factor independent variables mtcars] <- factor(mtcars]) # marginal effect of 'Petal.Width' across 'Petal.Width' cplot(m, x = "Petal.Width", what = "effect", n = 10 ) The octave script with comments shown below to plot the time vs velocity graph. Which plotting system is used is controlled by the graphicstoolkit function. Let’s learn the steps involved to specify markers in the Octave/Matlab plot command with attributes like edge color, face color, and marker size, etc. But, newer versions of Octave offer more modern plotting capabilities using OpenGL. # more complex model m <- lm(Sepal.Length ~ Sepal.Width * Petal.Width * I(Petal.Width ^ 2 ), Earlier versions of Octave provided plotting through the use of gnuplot. ![]() # prediction from several angles m <- lm(Sepal.Length ~ Sepal.Width, data = iris) Ylim = if (match.arg(what) %in% c("prediction", "stackedprediction")) c(0, 1.04) Ylab = if (match.arg(what) = "effect") paste0("Marginal effect of ", dx) else What = c("prediction", "classprediction", "stackedprediction", "effect"), Se.lty = if (match.arg(se.type) = "lines") 1L else 0L, Ylab = if (match.arg(what) = "prediction") paste0("Predicted value") else Xvals = prediction::seq_range(data], n = n), Currently methods exist for “lm”, “glm”, “loess” class models. Cplot: Conditional predicted value and average marginal effect plots for models Descriptionĭraw one or more conditional effects plots reflecting predictions or marginal effects from a model, conditional on a covariate.
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