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panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor,.) { usr <- par("usr") on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) Cor <- abs(cor(x, y)) # Remove abs function if desired txt <- paste0(prefix, format(c(Cor, 0.123456789), digits = digits)[1]) if(missing(cex.cor)) { cex.cor <- 0.4 / strwidth(txt) } text(0.5, 0.5, txt, cex = 1 + cex.c.

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panel.cor(c (1, 2, 3), c (4, 5, 6)) Run the code above in your browser using DataCamp Workspace. Powered by.

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panel.cor: accessory function for pairs () to display Pearson. In KOGMWU: Functional Summary and Meta-Analysis of Gene Expression Data Description Usage Arguments References View source: R/panel.cor.R Description works as upper.panel or lower.panel argument of pairs () (package graphics). Usage Arguments References

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upper.panel=panel.cor, lower.panel=panel.lm) 自定义后的散点图矩阵主对角线上用了直方图，从中我们可以看到四个变量各自的分布情况；上三角面板显示了各个变量之间的相关系数，相关系数越大字号越大；下三角为散点图，并用不同颜色的点标记出鸢尾花的不同类型.

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Description The functions here can be used to customize upper and lower triangles in correlation matrices. In particular panel.cor.res provides correlation coefficients (any alternative from cor can be used) and p -values for correlation tests. The function panel.lm puts linear fitted lines from simple linear regression in scatterplots.

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Did you run the panel.cor? pairs(z,upper.panel=panel.cor) Error in pairs.default(z, upper.panel = panel.cor) : object 'panel.cor' not found panel.cor <- function(x, y.

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panel.cor is a panel function modified from that described in the help file for the pairs function from the graphics package. This has been customized both to show that one can make such customizations, and to enable this one to be used to calculate the correlations between the variables included in a pairs plot. Usage

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mydiag.panel: Function for making fancy correlation matrices, adapted from. omit.outliers: Function that will find outliers in a vector (default: > 3 SD. panel.cor: Function for plotting cor values onto a correlation matrix; plot_lm: Plots xvar against yvar, and title is the P-value; read_qpcr_data: Reads qPCR data into a dataframe

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1 Answer Sorted by: 0 r2 <- cor.test (x, y)$p.value r3 <- symnum (r2, cutpoints = c (0, 0.001, 0.01, 0.05, 1), symbols = c ("***","**","*","")) can be used to obtain p value. so the whole panel.cor function looks like this:

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panel.cor: Correlation coefficient panel for pairs function Description Correlation coefficient panel for pairs function. Usage panel.cor (x, y, digits = 2, prefix = "", cex.cor) Arguments x first data series y second data series digits number of digits to plot prefix text to prefix the coefficients cex.cor character expansion factor pairs

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panel.cor: Correlation coefficient panel for pairs functionIn xcms: LC-MS and GC-MS Data Analysis. panel.cor. : Correlation coefficient panel for pairs function. Description Usage Arguments Author (s) See Also. View source: R/functions-xcmsSet.R.

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Shamelessly adapted from the pairs help page. Uses panel.cor, panel.cor.scale, and panel.hist, all taken from the help pages for pairs. Also adapts the ellipse function from John Fox's car package. pairs.panels is most useful when the number of variables to plot is less than about 6-10. It is particularly useful for an initial overview of the data.

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Description works as upper.panel or lower.panel argument of pairs () (package graphics). Usage panel.cor (x, y, digits=2, cex.cor) Arguments x x element of the pairs () matrix y y element of the pairs () matrix digits number of non-zero digits to leave at the end cex.cor scaling factor for displayed text References

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Description panel.cor is a panel function modified from that described in the help file for the pairs function from the graphics package. This has been customized both to show that one can make such customizations, and to enable this one to be used to calculate the correlations between the variables included in a pairs plot. Usage

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panel.cor: A Panel Function for Showing Correlations Description This function is called by pairs20x to show correlation coefficients in panels. Text size is shown proportional to the strength of the correlation. Usage panel.cor (x, y, digits=2, prefix="", cex.cor) Arguments x A vector of values y A vector of values digits

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The most common high level function used to produce plots in R is (rather unsurprisingly) the plot () function. For example, let's plot the weight of petunia plants from our flowers data frame which we imported in Chapter 3. flowers <- read.table(file = 'data/flower.txt', header = TRUE, sep = "\t", stringsAsFactors = TRUE) plot(flowers$weight)