9 Ways to Get Help in R Language

In this article, we will discuss 9 ways to get help in R Language. R Language has a very useful and advanced help system that helps the R user to understand the R language and lets him know how programming should be done in the R language.

Get Help in R Language

To get help in R language you need to click the Help button on the toolbar of RGui (R Graphical User Interface) windows. If you have internet access on your PC you can type CRAN in Google and search for the help you need at CRAN.

Use of “?” for Help

On the other hand, if you know the name of the function, you need to type the question mark (?) followed by the name of the required function on the R command line prompt. For example to get help about “lm” function type ?lm and then press the ENTER key from the keyboard.
help(lm) or ?lm have the same search results in the R language.

help.start()

Getting General help in R write the following command at the R command prompt

help.start()

## Output
help.start()
starting httpd help server ... done
If nothing happens, you should open
‘http://127.0.0.1:13825/doc/html/index.html’ yourself
9 ways to get help in R Language

Sometimes it is difficult to remember the precise name of the function, but you know the subject on which you need help for example data input. Use the help.search function (without question mark) with your query in double quotes like this:

help.search("data input")

Press the ENTER key, you will see the names of the R functions associated with the query.  After that, you can easily use ?lm to get help in R.

Use of find(” “)

Getting help in R, find, and apropos are also useful functions. The find function tells you what package something is in: for example

find("cor") gives output that the cor in the stats package.

Use of apropos()

The apropos function returns a character vector giving the names of all objects in the search list that match your inquiry (potentially partial) i.e., this command lists all functions containing your string. For example

apropos("lm")

will give the list of all functions containing the string lm

Use of example()

example(lm) will provide an example of your required function that is in this case, an example of the function lm()

Online Help

There is a huge amount of information about R on the web. On CRAN you will find a variety of help/ manuals. There are also answers to FAQs (Frequently Asked Questions) and R News (contains interesting articles, book reviews, and news of forthcoming releases. The search facility of the site allows you to investigate the contents of the R documents, functions, and searchable mail archives.

You can search your required function or string in help manuals and archived mailing lists by using

RSiteSearch("read.csv")

Get Vignettes

vignette is an R jargon for documentation and is written in the spirit of sharing knowledge, and
assisting new users in learning the purpose and use of a package. To get some help in R try ?vignette. Vignettes are optional supplemental documentation, that’s why not all packages come with vignettes.

vignette()          # will show available vignettes
vignette("foo")     # will show specific vignette

Now you have learned about getting help in R, now you can continue with the other R tutorials. It is possible that you do not understand something discussed in the coming R tutorials. If this happens then you should use the built-in help system before going to the internet. In most cases, the help system of R Language will give you enough information about the required function that you have searched for.

Some Sources of R Help/ Manual/ Documentations

https://cran.r-project.org/manuals.html

https://cran.r-project.org/other-docs.html

https://www.r-project.org/help.html

https://cran.r-project.org/bin/windows/base/rw-FAQ.html

Weighted Least Squares In R: A Quick WLS Tutorial

Introduction to Weighted Least Squares in R

This post will discuss the implementation of Weighted Least Squares (WLS) in R. The OLS method minimizes the sum of squared residuals, while the WLS weights the square residuals. The WLS technique is used when the OLS assumption related to constant variance in the errors is violated.

The WLS technique is also known as weighted linear regression it is a generalization of ordinary least squares (OLS) and linear regression in which knowledge of the variance of observations is incorporated into the regression.

WLS in R Language

Let us perform the WLS in R.

Here we will use the mtcars dataset. You need to load this data set first using the attach() function. For example,

attach(mtcars)

Consider the following example regarding the weighted least squares in which the reciprocal of $wt$ variable is used as weights. Here two different weighted models are performed and then check the fit of the model using anova() function.

# Weighted Model 1
w_model1 <- lm(mpg ~ wt + hp, data = mtcars)

# Weighted Model 2
w_model2 <- lm(mpg ~ wt + hp, data = mtcars, weights = 1/wt)

Check/ Test The Model Fit

To check the model fit, summary statistics of the fitted model, and different diagnostic plots of the fitted model, one can use the built-in functions as,

anova(w_model1, w_model2 )
summary(w_model1)
summary(w_model2)

plot(w_model1)
plot(w_model2)

The output of the First Weighted Model is:

Call:
lm(formula = mpg ~ wt + hp, data = mtcars)

Residuals:
   Min     1Q Median     3Q    Max 
-3.941 -1.600 -0.182  1.050  5.854 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 37.22727    1.59879  23.285  < 2e-16 ***
wt          -3.87783    0.63273  -6.129 1.12e-06 ***
hp          -0.03177    0.00903  -3.519  0.00145 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.593 on 29 degrees of freedom
Multiple R-squared:  0.8268,	Adjusted R-squared:  0.8148 
F-statistic: 69.21 on 2 and 29 DF,  p-value: 9.109e-12

The output of the Second Weighted Model is

Call:
lm(formula = mpg ~ wt + hp, data = mtcars, weights = 1/wt)

Weighted Residuals:
    Min      1Q  Median      3Q     Max 
-2.2271 -1.0538 -0.3894  0.6397  3.7627 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 39.002317   1.541462  25.302  < 2e-16 ***
wt          -4.443823   0.688300  -6.456 4.59e-07 ***
hp          -0.031460   0.009776  -3.218  0.00317 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.554 on 29 degrees of freedom
Multiple R-squared:  0.8389,	Adjusted R-squared:  0.8278 
F-statistic: 75.49 on 2 and 29 DF,  p-value: 3.189e-12

Graphical Representation of Models

The graphical representation of both models is:

Weighted Least Squares (Model 1)
Diagnostic Plots for WLS model: Model-1
Weighted Least Squares Model-2
Diagnostic Plots for WLS model: Model-2

FAQS Weighted Least Square in R

  1. How weighted least squares can be performed in R Lanague?
  2. How lm() function can be used to conduct a WLS in R?
  3. What are the important arguments for performing a weighted least squares model in R?

Learn about Generalized Least Squares

Curvilinear Regression in R: A Quick Reference

Introduction to Curvilinear Regression in R Language

In this post, we will learn about some basics of curvilinear regression in R.

The curvilinear/non-linear regression analysis is used to determine if there is a non-linear trend exists between $X$ and $Y$.

Adding more parameters to an equation results in a better fit to the data. A quadratic and cubic equation will always have higher $R^2$ than the linear regression model. Similarly, a cubic equation will usually have higher $R^2$ than a quadratic one.

Logarithmic and Polynomial Relationships

The logarithmic relationship can be described as follows:
$$Y=m\, log(x)++c$$
the polynomial relationship can be described as follows:
$$Y=m_1x + m_2x^2 + m_3x^3 + m_nx^n + c$$

The logarithmic example is more akin to a simple regression, whereas the polynomial example is multiple regression. Logarithmic relationships are common in the natural world; you may encounter them in many circumstances. Drawing the relationships between response and predictor variables as a scatter plot is generally a good starting point.

Consider the following data that are related in a curvilinear form,

GrowthNutrient
22
94
116
128
1310
1416
1722
1928
1730
1836
2048

Performing Curvilinear Regression in R

Let us perform a curvilinear regression in R language.

Growth <- c(2, 9, 11, 12, 13, 14, 17, 19, 17, 18, 20)
Nutrient <- c(2, 4, 6, 8, 10, 16, 22, 28, 30, 36, 48)
data <- as.data.frame(cbind(Growth, Nutrient))

ggplot(data, aes(Nutrient, Growth) ) +
  geom_point() +
  stat_smooth()
Curvilinear Regression in R

The Scatter plot shows the relationship appears to be a logarithmic one.

Linear Regression in R

Let us carry out a linear regression using the lm() function by taking the $\log$ of the predictor variable rather than the basic variable itself.

data <- cbind(Growth, Nutrient)
mod  <- lm(Growth~log(Nutrient, data))
summary(mod)

##
Call:

lm(formula = Growth ~ log(Nutrient), data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2274 -0.9039  0.5400  0.9344  1.3097 
Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.6914     1.0596   0.652     0.53    
log(Nutrient)   5.1014     0.3858  13.223 3.36e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.229 on 9 degrees of freedom
Multiple R-squared:  0.951,     Adjusted R-squared:  0.9456 
F-statistic: 174.8 on 1 and 9 DF,  p-value: 3.356e-07

FAQS about Curvilinear Regression in R

  1. Write in detail about curvilinear regression models.
  2. How visually one can guess the curvilinear relationship between the response and predictor variable?
  3. What may be the consequences, if a curvilinear relationship is estimated using a simple linear regression model?

Learn about Performing Linear Regression in R

Learn Statistics