Geom_line(aes(y = effect + 1.96 *se. # use ggplot2 instead of base graphics ggplot(tmp, aes(x = Petal.Width, y = "effect" )) + What = "effect", n = 10, draw = FALSE ) # 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 ) Depending on the mode, each byte represents 2, 4 or 8 pixels of the screen. The video memory is located between the addresses C000 to FFFF, ie has a size of 3FFF (16,383) bytes. # more complex model m <- lm(Sepal.Length ~ Sepal.Width * Petal.Width * I(Petal.Width ^ 2 ), The Amstrad CPC has three video modes : 'Mode 0' 160×200 pixels with 16 colors, 'Mode 1' 320×200 pixels with 4 colors and 'Mode 2' 640×200 pixels with 2 colors. # 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 Plot a pixel to screen / extern void LIB cplot(int x, int y. What = c("prediction", "classprediction", "stackedprediction", "effect"), int y) smallc / Colour graphics, only few targets are supported / / ZX. It contains two C compilers, an assembler / linker / librarian, data compression tools and a utility for processing the raw binaries into forms needed by specific targets. I am using SCCZ80 for this as I am a bit more familiar with it and that SCCZ80 uses SECTIONS (specification of memory address) more fully than ZSDCC. Z88DK is a complete development toolkit for the z80, z180 and rabbit processors. There are strengths and weaknesses in both compilers. Se.lty = if (match.arg(se.type) = "lines") 1L else 0L, For those that commonly use Z88dk, there are 2 different compilers built-in SCCZ80 and ZSDCC. 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.
0 Comments
Leave a Reply. |