When developing more complex models it is often desirable to
report a *p*-value for the model as a whole as well as an *R-square*
for the model.

#### p-values for models

The *p*-value for a model determines the significance
of the model compared with a null model. For a linear model, the null model is
defined as the dependent variable being equal to its mean. So, the *p*-value
for the model is answering the question, *Does this model explain the data
significantly better than would just looking at the average value of the
dependent variable?*

#### R-squared and pseudo R-squared

The *R-squared* value is a measure of how well the model
explains the data. It is an example of a *goodness-of-fit* statistic.

*R-squared* for linear (ordinary least squares) models

In R, models fit with the *lm* function are linear
models fit with *ordinary least squares* (OLS). For these models, *R-squared*
indicates the proportion of the variability in the dependent variable that is
explained by model. That is, an *R-squared* of 0.60 indicates that 60% of
the variability in the dependent variable is explained by the model.

##### Pseudo *R-squared*

For many types of models, *R-squared* is not defined.
These include relatively common models like *logistic regression* and the *cumulative
link models* used in this book. For these models, pseudo *R-squared*
measures can be calculated. A pseudo *R-squared* is not directly
comparable to the *R-squared* for OLS models. Nor can it can be
interpreted as the proportion of the variability in the dependent variable that
is explained by model. Instead, pseudo *R-squared* measures are relative
measures among similar models indicating how well the model explains the data.

This book uses three pseudo *R-squared* measures:
McFadden, Cox and Snell (also referred to as ML), Nagelkerke (also referred to
as Cragg and Uhler).

In general I favor the Nagelkerke pseudo *R-squared*,
but there is no agreement as to which pseudo *R-squared* measurement
should be used.

*p*-values and *R-squared* values.

*p*-values and *R-squared* values measure
different things. The *p*-value indicates if there is a significant
relationship described by the model, and the *R-squared* measures the
degree to which the data is explained by the model. It is therefore possible
to get a significant *p*-value with a low *R-squared* value. This
often happens when there is a lot of variability in the dependent variable, but
there are enough data points for a significant relationship to be indicated.

### Packages used in this chapter

The packages used in this chapter include:

• psych

• lmtest

• boot

• rcompanion

The following commands will install these packages if they are not already installed:

if(!require(psych)){install.packages("psych")}

if(!require(lmtest)){install.packages("lmtest")}

if(!require(boot)){install.packages("boot")}

if(!require(rcompanion)){install.packages("rcompanion")}

### Example of model *p*-value, *R-squared*,
and pseudo *R-squared*

The following example uses some hypothetical data of a
sample of people for which typing speed (*Words.per.minute*) and age were
measured. After plotting the data, we decide to construct a polynomial model
with *Words.per.minute* as the dependent variable and *Age* and *Age ^{2}*
as the independent variables. Notice in this example that all variables are
treated as interval/ratio variables, and that the independent variables are also continuous variables.

The data will first be fit with a linear model with the *lm*
function. Passing this model to the *summary *function will display the *p*-value
for the model and the *R-squared* value for the model.

The same data will then be fit with a *generalized linear
model* with the *glm* function. This type of model allows more
latitude in the types of data that can be fit, but in this example, we’ll use
the *family=gaussian* option, which will mimic the model fit with the *lm*
function, though the underlying math is different.

Importantly, the *summary* of the *glm* function
does not produce a *p*-value for the model nor an *R-squared* for the
model.

For the model fit with *glm*, the *p*-value can be
determined with the *anova* function comparing the fitted model to a null
model. The null model is fit with only an intercept term on the right side of the
model. As an alternative, the *nagelkerke* function described below also
reports a *p*-value for the model, using the likelihood ratio test.

There is no *R-squared* defined for a *glm*
model. Instead a pseudo *R-squared* can be calculated. The function *nagelkerke*
produces pseudo *R-squared* values for a variety of models. It reports three
types: McFadden, Cox and Snell, and Nagelkerke. In general I recommend using
the Nagelkerke measure, though there is no agreement on which pseudo *R-squared*
measurement to use, if any at all.

The Nagelkerke is the same as the Cox and Snell, except that the value is adjusted upward so that the Nagelkerke has a maximum value of 1.

It has been suggested that a McFadden value of 0.2–0.4 indicates a good fit.

Note that these models makes certain assumptions about the distribution of the data, but for simplicity, this example will ignore the need to determine if the data met these assumptions.

Input =("

Age Words.per.minute

12 23

12 32

12 25

13 27

13 30

15 29

15 33

16 37

18 29

22 33

23 37

24 33

25 43

27 35

33 30

42 25

53 22

")

Data = read.table(textConnection(Input),header=TRUE)

### Check the data frame

library(psych)

headTail(Data)

str(Data)

summary(Data)

### Remove unnecessary objects

rm(Input)

##### Plot the data

plot(Words.per.minute ~ Age,

data = Data,

pch=16,

xlab = "Age",

ylab = "Words per minute")

##### Prepare data

### Create new variable for the square of Age

Data$Age2 = Data$Age ^ 2

### Double check data frame

library(psych)

headTail(Data)

Age Words.per.minute Age2

1 12 23 144

2 12 32 144

3 12 25 144

4 13 27 169

... ... ... ...

14 27 35 729

15 33 30 1089

16 42 25 1764

17 53 22 2809

#### Linear model

model = lm (Words.per.minute ~ Age + Age2,

data=Data)

summary(model) ### Shows coefficients,

### p-value for model, and R-squared

Multiple R-squared: 0.5009, Adjusted R-squared: 0.4296

F-statistic: 7.026 on 2 and 14 DF, p-value: 0.00771

### p-value and (multiple) R-squared value

##### Simple plot of data and model

For bivariate data, the function *plotPredy* will plot
the data and the predicted line for the model. It also works for polynomial
functions, if the *order* option is changed.

library(rcompanion)

plotPredy(data = Data,

y = Words.per.minute,

x = Age,

x2 = Age2,

model = model,

order = 2,

xlab = "Words per minute",

ylab = "Age")

#### Generalized linear model

model = glm (Words.per.minute ~ Age + Age2,

data = Data,

family = gaussian)

summary(model) ### Shows coefficients

##### Calculate *p*-value for model

In R, the most common way to calculate the *p*-value
for a fitted model is to compare the fitted model to a null model with the *anova*
function. The null model is usually formulated with just a constant on the
right side.

null = glm (Words.per.minute ~ 1, ### Create
null model

data = Data, ### with
only a constant on the right side

family = gaussian)

anova (model,

null,

test="Chisq") ###
Tests options "Rao", "LRT",

### "Chisq", "F",
"Cp"

### But some work with only some model
types

Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 14 243.07

2 16 487.06 -2 -243.99 0.0008882 ***

##### Calculate pseudo *R-squared* and *p*-value for model

An alternative test for the *p*-value for a fitted
model, the *nagelkerke* function will report the *p*-value for a model
using the likelihood ratio test.

The *nagelkerke* function also reports the McFadden,
Cox and Snell, and Nagelkerke pseudo *R-squared* values for the model.

library(rcompanion)

nagelkerke(model)

$Pseudo.R.squared.for.model.vs.null

Pseudo.R.squared

McFadden 0.112227

Cox and Snell (ML) 0.500939

Nagelkerke (Cragg and Uhler) 0.501964

$Likelihood.ratio.test

Df.diff LogLik.diff Chisq p.value

-2 -5.9077 11.815 0.0027184

##### Likelihood ratio test for *p*-value for model

The *p*-value for a model by the likelihood ratio test
can also be determined with the *lrtest* function in the *lmtes*t
package.

library(lmtest)

lrtest(model)

#Df LogLik Df Chisq Pr(>Chisq)

1 4 -46.733

2 2 -52.641 -2 11.815 0.002718 **

##### Simple plot of data and model

library(rcompanion)

plotPredy(data = Data,

y = Words.per.minute,

x = Age,

x2 = Age2,

model = model,

order = 2,

xlab = "Words per minute",

ylab = "Age",

col = "red") ###
line color

### Optional analyses: Confidence intervals for *p*-values
and R-squared values

It is relatively easy to produce confidence intervals for *p*-values,
*R-squared* values, or other parameters from model fitting, such as coefficients
for regression terms. This can be accomplished with bootstrapping. Here the *boot.ci*
function from the *boot* package is used.

The code below is a little complicated, but relatively flexible.

*Function* can contain any function of interest, as long
as it includes an input vector or data frame (*input *in this case) and an
indexing variable (*index* in this case). *Stat* is set to produce
the actual statistic of interest on which to perform the bootstrap (*r.squared*
from the *summary* of the *lm* in this case).

The code *Function(Data, 1:n)* is there simply to test
*Function* on the data frame *Data*. In this case, it will produce
the output of *Function* for the first *n* rows of *Data*.
Since *n* is defined as the length of the first column in *Data*,
this should return the value for *Stat* for the whole data frame, if *Function*
is set up correctly.

Input =("

Age Words.per.minute

12 23

12 32

12 25

13 27

13 30

15 29

15 33

16 37

18 29

22 33

23 37

24 33

25 43

27 35

33 30

42 25

53 22

")

Data = read.table(textConnection(Input),header=TRUE)

Data$Age2 = Data$Age ^ 2

### Check the data frame

library(psych)

headTail(Data)

str(Data)

summary(Data)

### Remove unnecessary objects

rm(Input)

#### Linear model

*R-squared* value

library(boot)

Function = function(input, index){

Input = input[index,]

Result = lm (Words.per.minute ~ Age + Age2,

data = Input)

Stat = summary(Result)$r.squared

return(Stat)}

### Test Function

n = length(Data[,1])

Function(Data, 1:n)

[1] 0.5009385

### Produce Stat estimate by bootstrap

Boot = boot(Data,

Function,

R=5000)

mean(Boot$t[,1])

[1] 0.5754582

### Produce confidence interval by
bootstrap

boot.ci(Boot,

conf = 0.95,

type = "perc")

### Options: "norm",
"basic", "stud", "perc", "bca",
"all"

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS

Based on 1000 bootstrap replicates

Intervals :

Level Percentile

95% ( 0.3796, 0.7802 )

Calculations and Intervals on Original Scale

### Other information

Boot

hist(Boot$t[,1],

col = "darkgray")

*p*-value

Unfortunately the *p*-value for the model is not stored
neatly in the* lm* model object, so it has to be calculated in the *Stat
*definition.

library(boot)

Function = function(input, index){

Input = input[index,]

Result = lm (Words.per.minute ~ Age + Age2,

data = Input)

Stat = pf(summary(Result)$fstatistic[1],

summary(Result)$fstatistic[2],

summary(Result)$fstatistic[3],

lower.tail = FALSE)

return(Stat)}

### Test Function

n = length(Data[,1])

Function(Data, 1:n)

value

0.007710425

### Produce Stat estimate by bootstrap

Boot = boot(Data,

Function,

R=5000)

mean(Boot$t[,1])

[1] 0.00672866

### Produce confidence interval by
bootstrap

boot.ci(Boot,

conf = 0.95,

type = "perc")

### Options: "norm",
"basic", "stud", "perc", "bca",
"all"

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS

Based on 1000 bootstrap replicates

Intervals :

Level Percentile

95% ( 0.0000, 0.0368 )

Calculations and Intervals on Original Scale

### Other information

Boot

hist(Boot$t[,1],

col = "darkgray")

#### Generalized linear model

##### Pseudo *R-squared* value

The *nagelkerke* function produces a list. The second
item in the list is a matrix named

Pseudo.R.squared.for.model.vs.null.

The third element of this matrix is the value for the
Nagelkerke pseudo *R-squared*. So,

nagelkerke(Result, Null)[[2]][3]

* *

yields the value of the Nagelkerke pseudo *R-squared*.

library(boot)

library(rcompanion)

Function = function(input, index){

Input = input[index,]

Result = glm (Words.per.minute ~ Age + Age2,

data = Input,

family="gaussian")

Null = glm (Words.per.minute ~ 1,

data = Input,

family="gaussian")

Stat = nagelkerke(Result, Null)[[2]][3]

return(Stat)}

### Test Function

n = length(Data[,1])

Function(Data, 1:n)

[1] 0.501964

### Produce Stat estimate by bootstrap

Boot = boot(Data,

Function,

R=1000)

### In this case, even 1000
iterations can take a while

mean(Boot$t[,1])

[1] 0.5803598

### Produce confidence interval by
bootstrap

boot.ci(Boot,

conf = 0.95,

type = "perc")

### Options: "norm",
"basic", "stud", "perc", "bca",
"all"

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS

Based on 1000 bootstrap replicates

Intervals :

Level Percentile

95% ( 0.3836, 0.7860 )

Calculations and Intervals on Original Scale

### Other information

Boot

hist(Boot$t[,1],

col = "darkgray")

*p*-value

library(boot)

library(rcompanion)

Function = function(input, index){

Input = input[index,]

Result = glm (Words.per.minute ~ Age + Age2,

data = Input,

family="gaussian")

Null = glm (Words.per.minute ~ 1,

data = Input,

family="gaussian")

Stat = nagelkerke(Result, Null)[[3]][1,4]

return(Stat)}

### Test Function

n = length(Data[,1])

Function(Data, 1:n)

[1] 0.0027184

### Produce Stat estimate by bootstrap

Boot = boot(Data,

Function,

R=1000)

### In this case, even 1000
iterations can take a while

mean(Boot$t[,1])

[1] 0.002723259

boot.ci(Boot,

conf = 0.95,

type = "perc")

### Options: "norm",
"basic", "stud", "perc", "bca",
"all"

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS

Based on 1000 bootstrap replicates

Intervals :

Level Percentile

95% ( 0.0000, 0.0173 )

Calculations and Intervals on Original Scale

### Other information

Boot

hist(Boot$t[,1],

col = "darkgray")

### References

Kabacoff, R.I. Bootstrapping. Quick-R. www.statmethods.net/advstats/bootstrapping.html.