In R, after creating a linear model using the function model <- lm() and plotting it using plot(model), you will get back 4 graphs each displaying your model differently. Can anyone explain what these graphs mean?
plot.lm can produce 6 different diagnostic plots, controlled by the which parameter. These are:
a plot of residuals against fitted values
a Normal Q-Q plot
a Scale-Location plot of sqrt(| residuals |) against fitted values
a plot of Cook's distances versus row labels
a plot of residuals against leverages
a plot of Cook's distances against leverage/(1-leverage)
By default it will produce numbers 1, 2, 3 and 5, pausing between plots in interactive mode.
You can see them all in one go if you set up the graphics device for multiple plots, eg:
mdl <- lm(hp~disp,mtcars)
par(mfrow=c(3,2))
plot(mdl,which=1:6)
Interpretation of these plots is a question for Cross Validated, though ?plot.lm gives some basic information.
Related
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?
Basically, I want to graph my residual error.
I have my linear regression line, say it's called "regline".
When I plot regline to get a graph of my residual error, I type:
plot(regline)
The problem is, I get 4 different graphs. Residuals vs. fitted, normal Q-Q, Scale-Location, Residuals vs. Leverage, but I only want the first one. I only want residuals vs. fitted. WHen I create an R Markdown, it shows all 4. How can I make it so that wehen i create an R markdown, I only get Residuals vs. fitted, rather than all 4.
Thanks
See ?plot.lm.
which
if a subset of the plots is required, specify a subset of the numbers 1:6, see caption below (and the ‘Details’) for the different kinds.
So you want:
plot(regline, which = 1)
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.
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.
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.