I am looking for a function in R, that will take the output from the betareg regression, and plot an interaction plot with 2 continuous variables, where one of these variables is displayed as the mean and the SD on the plot.
I have been trying to use interaction_plot(), but this just returns a mess for the continuous variable. Other examples from ggplot require a regression object from lm.
Related
I used R to compute variable importance plot for random forest model. When I looked at the mean decrease in Gini plot, I was confused by the number on the x axis. Why are the numbers so large? I thought they were supposed to represent percentage of decrease. My model ran normally, and I used the below code to create the plot:
Variable Importance Plot
I tried the following code:
Plot <- importance(rf_best_model, scale=T)
varImp.rf =dotchart(sort(Plot[,4]), xlim=c(0,10000), xlab="MeanDecreaseGini")
varImp
I have a dataframe which is a time series. I am using the function lm to build a multivariate regression model.
linearmodel <- lm(Y~X1+X2+X3, data = data)
I want to plot the residuals of this linearmodel on the y-axis and time on the x-axis using a simple function, with the lm() object as the input.
Standard residual plotting functions like the one in car package (car::residualPlot) gives residuals on the Y-axis and fitted-values on the Y-axis.
Ideally, I need the residuals on the Y-axis and the timescale on the X-axis. But I understand that the function lm() is time agnositc. So, I can live with if the residuals are on Y-axis in the same order as the data input and nothing on the X-axis
Is there a plotting function which i can use by passing the linearmodel object into the function (not something where i can extract the residuals and use ggplot2). So for example: plot<- plotresidualsinorder(linearmodels) should give me the residuals on Y-axis in the same order of the data input?
I want to use this plot in R-shiny ultimately.
My research led me to car package, which is wonderful in its own right, but doesn't have the function to solve my problem.
Many thanks in advance for the help.
You can use the Residual Plot information. For the proposed solution, we need to apply the lm function to a formula that describes your Y variables by the variables X1+X2+X3, and save the linear regression model in a new linearmodel variable. Finally, we compute the residual with the resid function. In your case, the following solution can be representative for your problem.
Proposed solution:
linearmodel <- lm(Y~X1+X2+X3, data = data)
lm_resid <- resid(linearmodel)
plot(data$X1+X2+X3, lm_resid,
ylab="Residuals", xlab="Time",
main="Data")
abline(0, 0)
For any help concerning how does the resid function works, you can try:
help(resid)
Calisto's solution will work, but there is a more simple and straightforward solution. The lm function already give to you the regression residuals. So you may simply pass:
plot(XTime, linearmodel$residuals, main = "Residuals")
XTime is the Date variable of your dataset, maybe you may require to format that with POSIX functions: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/as.POSIX*
Add parameters as you need to share it on R-shiny.
When performing linear mixed models, I have had to square-root(log) transform the data to achieve a normal distribution. Having performed the LMMs, I now want to plot the results onto a graph, but on the original scale i.e. not square-root(log) transformed.
Apparently I can use my raw (untransformed data) on a graph, and to create the predicted regression line I can use the coefficients from my LMM output to get backtransformed predicted y-values for each of my x values. This is where I'm stuck - I have no idea how to do this. Can anyone help?
Is Plot residuals vs predicted response equivalent to Plot residuals vs fitted ?
If so, then would be plotted by plot(lm) and plot(predict(lm)), where lm is the linear model ?
Am I correct?
Maybe little off-topic, but as an addition: package named ggfortify might come handy. Super easy to use, like this:
library(ggfortify)
autoplot(mod3)
Yields an output with the most important things you need to know, if your model violates the lm assumptions or not. An example output here:
Yes, the fitted values are the predicted responses on the training data, i.e. the data used to fit the model, so plotting residuals vs. predicted response is equivalent to plotting residuals vs. fitted.
As for your second question, the plot would be obtained by plot(lm), but before that you have to run par(mfrow = c(2, 2)). This is because plot(lm) outputs 4 plots, one of which is the one you want, i.e the residuals vs fitted plot. The command above divides the output screen into four facets, so each plot will be shown in one. The plot you are looking for will appear in the top left.
The visreg package in R can produce plots for various regression models. When creating conditional plots for each predictor, the other predictors are — by default — held at their median values, although this value can be changed by the user. In the documentation, an example is given (Fig. 5) that shows the effect of choosing values other than the median. The model's predictions change depending on the chosen value, as do the data that are plotted. My question is this: how are the data transformed between these plots? Are they simply adjusted according to the model?
The conditional plot shows partial residuals and not the original data as I thought. Consequently, the points that are plotted are determined according to the equation given in that URL.