![]() There are no major changes from SeisLab 3.01. If it is lower then this release is newer. Otherwise, compare the distribution ID with that of this release (15.09.20). If you get the error message "Undefined function or variable 'ddid'." then you have the very first release of SeisLab. In case you already have SeisLab installed you can find its distribution ID by typing "ddid" at the Matlab prompt. Generally, I make an effort to avoid functions from toolboxes however, I am aware of at least one call to a function in the Optimization Toolbox. Also, SeisLab 2.01, which works with Matlab 6.1 (2001) and higher, can still be downloaded from the Matlab File Exchange (file 8827). To export the above plot in Postscript and PNG files named demo.eps and demo.png, respectively, the following program can be used. Presently, I ran limited tests under them under R2015a, but I try not to use Matlab syntax introduced after R2007a. The plotting class CPlot enables high-level creation and manipulation of plots within the Ch language environment. Several versions of Matlab were released during their development. The functions come with a manual in PDF format and scripts with examples. They use standardized structures to represent seismic data and well data and thus allow simple concatenation of function calls. These functions read and write seismic data in standard SEG-Y format, read and write well logs in LAS-format 2.0 (also read LAS-format 3.0), and perform many of the manipulations usually performed on these data types. Document printing is now managed by Imprints 20+ Wepa printers, and their on-campus poster printing service. Geom_line(aes(y = effect - 1.96 *se.A set of about 170 functions (plus support functions called by them) for analysis and display of exploration-seismic data and well logs. As of July 15, 2021, IT Services has retired poster printing (CPLOT) and computer lab document printing. Geom_line(aes(y = effect + 1.96 *se.effect)) + # use ggplot2 instead of base graphics ggplot(tmp, aes(x = Petal.Width, y = "effect" )) + Is it true that the cplot function in R cannot deal with a factor variable, having two levels I would like to make a plot with the average marginal effect of 'z" (0 or 1) for each value. ![]() 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 ) # more complex model m <- lm(Sepal.Length ~ Sepal.Width * Petal.Width * I(Petal.Width ^ 2 ), # prediction from several angles m <- lm(Sepal.Length ~ Sepal.Width, data = iris) To export the above plot in Postscript and PNG files named demo.eps and demo. Ylim = if (match.arg(what) %in% c("prediction", "stackedprediction")) c(0, 1.04) The plotting class CPlot enables high-level creation and manipulation of plots within the Ch language environment. 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), This makes it easy to tell the absolte value precisely. The contour abs (z) 1 is emphasized, other abs contours are at 2, 4, 8, etc. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. This avoids streaks of colors occurring with other color spaces, e.g., HSL. How to use the ipcolor function in cplot To help you get started, we’ve selected a few cplot examples, based on popular ways it is used in public projects. Currently methods exist for “lm”, “glm”, “loess” class models. cplot uses OKLAB, a perceptually uniform color space for the argument colors. 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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