formegasil.blogg.se - Linear regression in rstudio

Linear regression in rstudio code#

# Add the fitted values as a new column in the dataframeĭiamonds $value.lm <- diamonds.lm $fitted.values We’ll assign the result of the function to a new object called diamonds.lm: Because value is the dependent variable, we’ll specify the formula as formula = value ~ weight + clarity + color. To estimate each of the 4 weights, we’ll use lm(). $\beta_$ is the baseline value of a diamond with a value of 0 in all independent variables. The linear model will estimate each diamond’s value using the following equation: Our goal is to come up with a linear model we can use to estimate the value of each diamond (DV = value) as a linear combination of three independent variables: its weight, clarity, and color. # weight clarity color value value.lm weight.c clarity.c value.g190 18.5 Chapter 8: Matrices and Dataframes.18.4 Chapter 7: Indexing vectors with.17.4 Loops over multiple indices with a design matrix.17.3 Updating a container object with a loop.17.2 Creating multiple plots with a loop.17.1.2 Adding the integers from 1 to 100.16.4.4 Storing and loading your functions to and from a function file with source().16.4.2 Using stop() to completely stop a function and print an error.16.3 Using if, then statements in functions.16.2.3 Including default values for arguments.16.2 The structure of a custom function.16.1 Why would you want to write your own function?.15.5.2 Transforming skewed variables prior to standard regression.15.5.1 Adding a regression line to a plot.

15.5 Logistic regression with glm(family = "binomial".

15.4 Regression on non-Normal data with glm().

15.3 Comparing regression models with anova().

15.2.6 Getting an ANOVA from a regression model with aov().

15.2.5 Center variables before computing interactions!.

15.2.4 Including interactions in models: y ~ x1 * x2.

15.2.3 Using predict() to predict new data from a model.

15.2.2 Getting model fits with fitted.values.

15.2.1 Estimating the value of diamonds with lm().

14.7 Repeated measures ANOVA using the lme4 package.

14.6 Getting additional information from ANOVA objects.

14.5 Type I, Type II, and Type III ANOVAs.

14.1 Full-factorial between-subjects ANOVA.

13.5.1 Getting APA-style conclusions with the apa function.

13.1 A short introduction to hypothesis tests.

12.3.1 Complex plot layouts with layout().

12.3 Arranging plots with par(mfrow) and layout().

11.10 Test your R might! Purdy pictures.

11.8 Saving plots to a file with pdf(), jpeg() and png().

11.7.5 Combining text and numbers with paste().

10.6 Test your R might!: Mmmmm…caffeine.

9.6.3 Reading files directly from a web URL.

9.1.1 Why object and file management is so important.

8.7 Test your R might! Pirates and superheroes.

7.3.1 Ex: Fixing invalid responses to a Happiness survey.

7.2.2 Counts and percentages from logical vectors.

6.2.3 Sample statistics from random samples.

6.2.2 Additional numeric vector functions.

4.4.4 Example: Pirates of The Caribbean.

Linear regression in rstudio code#

4.3.1 Commenting code with the # (pound) sign.4.3 A brief style guide: Commenting and spacing.4.2.1 Send code from an source to the console.

1.5.2 Getting R help and inspiration online.