ggfortify

HP Fortify : ASP.NET Bad Practices: Non-Serializable Object Stored in Session

由 徘徊边缘 提交于 2021-02-08 15:15:31
问题 The method set_UserActiveEnvironments() in HttpContextHelper.cs stores a non-serializable object as an HttpSessionState attribute on line 47, which can damage application reliability By default, ASP.NET servers store the HttpSessionState object, its attributes and any objects they reference in memory. This model limits active session state to what can be accommodated by the system memory of a single machine. In order to expand capacity beyond these limitations, servers are frequently

HP Fortify : ASP.NET Bad Practices: Non-Serializable Object Stored in Session

由 旧巷老猫 提交于 2021-02-08 15:14:28
问题 The method set_UserActiveEnvironments() in HttpContextHelper.cs stores a non-serializable object as an HttpSessionState attribute on line 47, which can damage application reliability By default, ASP.NET servers store the HttpSessionState object, its attributes and any objects they reference in memory. This model limits active session state to what can be accommodated by the system memory of a single machine. In order to expand capacity beyond these limitations, servers are frequently

Plotting multiple density distributions on one plot

由 不打扰是莪最后的温柔 提交于 2020-12-06 12:16:45
问题 For teaching purposes I'm looking to create and plot multiple distributions on to one graph. The code I've been using to do this is: library(ggplot2) library(ggfortify) # Create an initial graph with 1 distribution p3 <- ggdistribution(dnorm, seq(-5, 10,length=1000), colour='blue', mean=0.15, sd=0.24, fill='blue') # Update p3 with second distribution p3 <- ggdistribution(dnorm, seq(-5, 10,length=1000), mean = 1.11, sd = 0.55, colour='green', fill='green',p=p3) # View p3 p3 Initially, this

Plotting multiple density distributions on one plot

由 旧时模样 提交于 2020-12-06 12:14:42
问题 For teaching purposes I'm looking to create and plot multiple distributions on to one graph. The code I've been using to do this is: library(ggplot2) library(ggfortify) # Create an initial graph with 1 distribution p3 <- ggdistribution(dnorm, seq(-5, 10,length=1000), colour='blue', mean=0.15, sd=0.24, fill='blue') # Update p3 with second distribution p3 <- ggdistribution(dnorm, seq(-5, 10,length=1000), mean = 1.11, sd = 0.55, colour='green', fill='green',p=p3) # View p3 p3 Initially, this

Plotting multiple density distributions on one plot

由 江枫思渺然 提交于 2020-12-06 12:14:14
问题 For teaching purposes I'm looking to create and plot multiple distributions on to one graph. The code I've been using to do this is: library(ggplot2) library(ggfortify) # Create an initial graph with 1 distribution p3 <- ggdistribution(dnorm, seq(-5, 10,length=1000), colour='blue', mean=0.15, sd=0.24, fill='blue') # Update p3 with second distribution p3 <- ggdistribution(dnorm, seq(-5, 10,length=1000), mean = 1.11, sd = 0.55, colour='green', fill='green',p=p3) # View p3 p3 Initially, this

How to implement the facet grid feature using the ggfortify library on a time series data?

由 旧巷老猫 提交于 2019-12-24 21:34:38
问题 I am using RStudio and I have a time series data ( ts object) called data1 . Here is how data1 looks: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 135 172 179 189 212 47 301 183 247 292 280 325 2015 471 243 386 235 388 257 344 526 363 261 189 173 2016 272 267 197 217 393 299 343 341 315 305 384 497 To plot the above, I have run this code: plot (data1) and I get the following plot: I want to have a plot that is broken by Year and I was thinking of implementing the facet_grid feature

Change loadings (arrows) length in PCA plot using ggplot2/ggfortify?

由 心已入冬 提交于 2019-12-21 16:22:48
问题 I have been struggling with rescaling the loadings (arrows) length in a ggplot2/ggfortify PCA. I have looked around extensively for an answer to this, and the only information I have found either code new biplot functions or refer to other entirely different packages for PCA (ggbiplot, factoextra), neither of which address the question I would like to answer: Is it possible to scale/change size of PCA loadings in ggfortify? Below is the code I have to plot a PCA using stock R functions as

R: ggfortify: “Objects of type prcomp not supported by autoplot”

由 五迷三道 提交于 2019-12-07 04:38:34
问题 I am trying to use ggfortify to visualize the results of a PCA I did using prcomp. sample code: iris.pca <- iris[c(1, 2, 3, 4)] autoplot(prcomp(iris.pca)) Error: Objects of type prcomp not supported by autoplot. Please use qplot() or ggplot() instead. What is odd is that autoplot is specifically designed to handle the results of prcomp - ggplot and qplot can't handle objects like this. I'm running R version 3.2 and just downloaded ggfortify off of github this AM. Can anyone explain this

R: ggfortify: “Objects of type prcomp not supported by autoplot”

由 拈花ヽ惹草 提交于 2019-12-05 10:09:57
I am trying to use ggfortify to visualize the results of a PCA I did using prcomp. sample code: iris.pca <- iris[c(1, 2, 3, 4)] autoplot(prcomp(iris.pca)) Error: Objects of type prcomp not supported by autoplot. Please use qplot() or ggplot() instead. What is odd is that autoplot is specifically designed to handle the results of prcomp - ggplot and qplot can't handle objects like this. I'm running R version 3.2 and just downloaded ggfortify off of github this AM. Can anyone explain this message? I'm guessing that you didn't load the required libraries, the code below: library(devtools) install

Change loadings (arrows) length in PCA plot using ggplot2/ggfortify?

由 …衆ロ難τιáo~ 提交于 2019-12-04 07:01:29
I have been struggling with rescaling the loadings (arrows) length in a ggplot2/ggfortify PCA. I have looked around extensively for an answer to this, and the only information I have found either code new biplot functions or refer to other entirely different packages for PCA (ggbiplot, factoextra), neither of which address the question I would like to answer: Is it possible to scale/change size of PCA loadings in ggfortify? Below is the code I have to plot a PCA using stock R functions as well as the code to plot a PCA using autoplot/ggfortify. You'll notice in the stock R plots I can scale
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