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POp-value matrix of x and y variables from anova output
 

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  1. POp-value matrix of x and y variables from anova output
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    <p>I have many X and Y variables (something like, 500 x 500). The following just small data: </p> <pre><code>yvars &lt;- data.frame (Yv1 = rnorm(100, 5, 3), Y2 = rnorm (100, 6, 4), Yv3 = rnorm (100, 14, 3)) xvars &lt;- data.frame (Xv1 = sample (c(1,0, -1), 100, replace = T), X2 = sample (c(1,0, -1), 100, replace = T), Xv3 = sample (c(1,0, -1), 100, replace = T), D = sample (c(1,0, -1), 100, replace = T)) </code></pre> <p>I want to extact p-values and make a matrix like this: </p> <pre><code> Yv1 Y2 Yv3 Xv1 X2 Xv3 D </code></pre> <p>Here is my attempt to loop the process:</p> <pre><code>prob = NULL anova.pmat &lt;- function (x) { mydata &lt;- data.frame(yvar = yvars[, x], xvars) for (i in seq(length(xvars))) { prob[[i]] &lt;- anova(lm(yvar ~ mydata[, i + 1], data = mydata))$`Pr(&gt;F)`[1] } } sapply (yvars,anova.pmat) Error in .subset(x, j) : only 0's may be mixed with negative subscripts What could be the solution ? </code></pre> <p><strong>Edit:</strong></p> <p>For the first Y variable:</p> <p>For first Y variable: </p> <pre><code>prob &lt;- NULL mydata &lt;- data.frame(yvar = yvars[, 1], xvars) for (i in seq(length(xvars))) { prob[[i]] &lt;- anova(lm(yvar ~ mydata[, i + 1], data = mydata))$`Pr(&gt;F)`[1] } prob [1] 0.4995179 0.4067040 0.4181571 0.6291167 </code></pre> <p>Edit again:</p> <pre><code>for (j in seq(length (yvars))){ prob &lt;- NULL mydata &lt;- data.frame(yvar = yvars[, j], xvars) for (i in seq(length(xvars))) { prob[[i]] &lt;- anova(lm(yvar ~ mydata[, i + 1], data = mydata))$`Pr(&gt;F)`[1] } } Gives the same result as above !!! </code></pre>
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    1. COThe usual place to start is to get your function to work with one instance of the loop variable(s). Then you can deterrmine whether the loop body makes sense. At the moment I cannot figure out what you attempting, but you shoud try calling anova.pmat with one "x" at a time. (This effort may be revealing in that it should give you a better sense of hte false discovery rate with multiple testing.)
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    2. COI'm having trouble understanding what you're trying to do with `mydata <- data.frame(yvar = yvars[, x], xvars)`.
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    3. CO@DWin please see my recent edits
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