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Keys_Falcon_Flansburgh_RMD.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="author" content="Falcon-Flansburgh, Victoria, and Keys, Rob" />
<meta name="date" content="2024-07-10" />
<title>Statistical Analysis of the Relationship Between Mental Health, Emotional Wellbeing, and Athletic Identity</title>
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
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) 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<div id="header">
<h1 class="title toc-ignore">Statistical Analysis of the Relationship
Between Mental Health, Emotional Wellbeing, and Athletic Identity</h1>
<h4 class="author">Falcon-Flansburgh, Victoria, and Keys, Rob</h4>
<h4 class="date">2024-07-10</h4>
</div>
<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>The Covid-19 pandemic forced lock downs and quarantines for nearly
the entire population, and we attempt to reveal the connection between
post-pandemic mental health and the level of athletics in an
individual’s life. We use multiple statistical techniques to analyze the
data, such as multiple linear regression, logistic regression,
regression trees, and ANOVA tests.</p>
<p>Our chosen data set was collected in May of 2021 through an online
questionnaire, where participants answered about their current emotional
well being and mental health, how athletics affected their
self-identity, the impact athletics have in their life, the length of
their covid isolation, and basic background information. The data set
has over 80 columns of data with 688 participants, and this large size
allows for complex analysis across data columns and an ample sample size
for analysis.</p>
<p><a href="https://figshare.com/articles/dataset/Data_Sheet_1_Comparing_Mental_Health_of_Athletes_and_Non-athletes_as_They_Emerge_From_a_COVID-19_Pandemic_Lockdown_CSV/14623635/1">Data_Sheet_1_Comparing
Mental Health of Athletes and Non-athletes as They Emerge From a
COVID-19 Pandemic Lockdown.CSV (figshare.com)</a> </p>
<p>The code block below loads the needed libraries and one package
install as necessary.</p>
<pre class="r"><code>#install.packages("car") #Uncomment and run once
# Libraries
library(readr)
library(dplyr)</code></pre>
<pre><code>##
## Attaching package: 'dplyr'</code></pre>
<pre><code>## The following objects are masked from 'package:stats':
##
## filter, lag</code></pre>
<pre><code>## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(ggplot2)
library(tidyverse)</code></pre>
<pre><code>## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1</code></pre>
<pre><code>## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors</code></pre>
<pre class="r"><code>library(car)</code></pre>
<pre><code>## Loading required package: carData
##
## Attaching package: 'car'
##
## The following object is masked from 'package:purrr':
##
## some
##
## The following object is masked from 'package:dplyr':
##
## recode</code></pre>
<pre class="r"><code>library(tree)</code></pre>
<p>The code block below imports our data from the file to a
variable.</p>
<pre class="r"><code>originalData <- read_csv('Data_Sheet_1_Comparing Mental Health of Athletes and Non-athletes as They Emerge From a COVID-19 Pandemic Lockdown.csv')</code></pre>
<pre><code>## New names:
## Rows: 753 Columns: 85
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): Mental Health Condition?, Occupation:, Survey Date:, Dates Shielding: dbl
## (80): Respondent ID, Gender:, Age Group:, Country During Lockdown, Marit... lgl
## (1): ...85
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...85`</code></pre>
</div>
<div id="multiple-linear-regression" class="section level1">
<h1>Multiple Linear Regression</h1>
<p><em>Data cleaning</em></p>
<p>The code block below eliminates rows with values that indicate a null
or not understandable response for the 4 variables about to be used in
the next code block.</p>
<pre class="r"><code># Data cleanup
Data <- originalData %>% filter(originalData$`Emotional Wellbeing` < 999)
Data <- Data %>% filter(Data$`I consider myself an athlete` < 999)
Data <- Data %>% filter(Data$`most of my friends are athletes` < 999)
Data <- Data %>% filter(Data$`Sport is the most important part of my life` < 999)
Data</code></pre>
<pre><code>## # A tibble: 477 × 85
## `Respondent ID` `Gender:` `Age Group:` `Country During Lockdown`
## <dbl> <dbl> <dbl> <dbl>
## 1 11785667914 2 2 2
## 2 11785634332 2 3 1
## 3 11784520014 2 3 2
## 4 11783867710 2 1 2
## 5 11783726076 2 1 2
## 6 11782483021 1 4 2
## 7 11782007234 2 5 1
## 8 11781906506 2 4 1
## 9 11781898061 2 4 2
## 10 11780175533 1 3 1
## # ℹ 467 more rows
## # ℹ 81 more variables: `Mental Health Condition?` <chr>, `Occupation:` <chr>,
## # `Marital Status:` <dbl>, `Smoking Status:` <dbl>,
## # `Five Fruit and Veg` <dbl>, `Hours sleep:` <dbl>, `Survey Date:` <chr>,
## # `Shielded?` <dbl>, `Dates Shielding:` <chr>,
## # `Weeks Social Distancing` <dbl>, `# in lockdown bubble:` <dbl>,
## # `Athlete/Non-Athlete` <dbl>, `AIMS_ TOTAL` <dbl>, …</code></pre>
<p><em>Model Building</em></p>
<p>The code block below uses multiple linear regression to measure the
effects of self-identifying as an athlete, having athlete friends, and
having sports as the most important aspect of your life on the effect of
emotional wellbeing.</p>
<pre class="r"><code>Emo_Data <- Data %>% select(`Emotional Wellbeing`, `I consider myself an athlete`, `most of my friends are athletes`, `Sport is the most important part of my life`) #create a dataframe with just the relevant variables
Emo_Well_Fit <- lm(Emo_Data$`Emotional Wellbeing` ~ Emo_Data$`I consider myself an athlete` + Emo_Data$`most of my friends are athletes` + Emo_Data$`Sport is the most important part of my life`) #create the model
summary(Emo_Well_Fit)</code></pre>
<pre><code>##
## Call:
## lm(formula = Emo_Data$`Emotional Wellbeing` ~ Emo_Data$`I consider myself an athlete` +
## Emo_Data$`most of my friends are athletes` + Emo_Data$`Sport is the most important part of my life`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7537 -1.4871 0.6343 1.8252 6.2480
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 9.85940 0.47462
## Emo_Data$`I consider myself an athlete` 0.27919 0.10263
## Emo_Data$`most of my friends are athletes` 0.14985 0.10660
## Emo_Data$`Sport is the most important part of my life` -0.30602 0.09584
## t value Pr(>|t|)
## (Intercept) 20.773 < 2e-16 ***
## Emo_Data$`I consider myself an athlete` 2.720 0.00676 **
## Emo_Data$`most of my friends are athletes` 1.406 0.16046
## Emo_Data$`Sport is the most important part of my life` -3.193 0.00150 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.946 on 473 degrees of freedom
## Multiple R-squared: 0.03873, Adjusted R-squared: 0.03263
## F-statistic: 6.352 on 3 and 473 DF, p-value: 0.0003152</code></pre>
<pre class="r"><code>avPlots(Emo_Well_Fit) #create graphs for each variable's relationship with the predicted variable</code></pre>
<p><img 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" width="672" /></p>
<p><em>Analysis</em></p>
<p>Looking at the individual variable graphs, we can see a slight
positive correlation between both ‘I consider myself an athlete’ and
‘Most of my friends are athletes’ with Emotional well being, and a
slight negative correlation between ‘Sport is the most important part of
my life’ with Emotional well being. By comparing the p values to 0.05 to
determine significance, we can see that both ‘I consider myself an
athlete’ and ‘Sport is the most important part of my life’ are
significant, and we can say they impact emotional well being. Despite
both being sport-related metrics, we can see that they have opposite
signs, indicating that they have opposite effects on emotional well
being, which makes sense that being an athlete and working out will
improve your mental health, but valuing a sport above all else and then
not being able to play during the lockdown would negatively affect
you.</p>
</div>
<div id="linear-regression" class="section level1">
<h1>Linear Regression</h1>
<p><em>Model Building</em></p>
<p>The code block below uses linear regression to predict emotional
wellbeing for athletes and non-athletes using weeks spent social
distancing, as well as plotting the results.</p>
<pre class="r"><code>ggplot(Data, aes(x=Data$`Weeks Social Distancing`, y=Data$`Emotional Wellbeing`, color=factor(Data$`Athlete/Non-Athlete`)))+
geom_point()+
geom_smooth(method=lm, se=FALSE)+
scale_color_manual(name="",values = c("coral", "blue"), labels = c('Athletes', 'Non-Athletes')) #cleans up legend</code></pre>
<pre><code>## `geom_smooth()` using formula = 'y ~ x'</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>Emo_Weeks_Fit <- lm(Data$`Emotional Wellbeing` ~ Data$`Weeks Social Distancing`*as.factor(Data$`Athlete/Non-Athlete`), data = Data)
summary(Emo_Weeks_Fit)</code></pre>
<pre><code>##
## Call:
## lm(formula = Data$`Emotional Wellbeing` ~ Data$`Weeks Social Distancing` *
## as.factor(Data$`Athlete/Non-Athlete`), data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7158 -1.6729 0.5344 1.7133 5.0711
##
## Coefficients:
## Estimate
## (Intercept) 10.45823
## Data$`Weeks Social Distancing` 0.04294
## as.factor(Data$`Athlete/Non-Athlete`)2 0.72296
## Data$`Weeks Social Distancing`:as.factor(Data$`Athlete/Non-Athlete`)2 -0.22184
## Std. Error
## (Intercept) 0.57011
## Data$`Weeks Social Distancing` 0.11579
## as.factor(Data$`Athlete/Non-Athlete`)2 1.21077
## Data$`Weeks Social Distancing`:as.factor(Data$`Athlete/Non-Athlete`)2 0.23882
## t value
## (Intercept) 18.344
## Data$`Weeks Social Distancing` 0.371
## as.factor(Data$`Athlete/Non-Athlete`)2 0.597
## Data$`Weeks Social Distancing`:as.factor(Data$`Athlete/Non-Athlete`)2 -0.929
## Pr(>|t|)
## (Intercept) <2e-16
## Data$`Weeks Social Distancing` 0.711
## as.factor(Data$`Athlete/Non-Athlete`)2 0.551
## Data$`Weeks Social Distancing`:as.factor(Data$`Athlete/Non-Athlete`)2 0.353
##
## (Intercept) ***
## Data$`Weeks Social Distancing`
## as.factor(Data$`Athlete/Non-Athlete`)2
## Data$`Weeks Social Distancing`:as.factor(Data$`Athlete/Non-Athlete`)2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.997 on 473 degrees of freedom
## Multiple R-squared: 0.005038, Adjusted R-squared: -0.001273
## F-statistic: 0.7983 on 3 and 473 DF, p-value: 0.4952</code></pre>
<p><em>Analysis</em></p>
<p>The regression line for athletes and non athletes shows that
athletes’ emotional wellbeing has a positive correlation with weeks
spent social distancing, while non-athletes’ emotional wellbeing has a
negative correlation. This could be due to a variety of factors
associated with being an athlete, but would suggest that athletes seemed
to handle the social reduction better than non-athletes, actually having
social distancing improve their mental health, compared to the expected
result of a negative correlation like non-athletes showed. However, none
of the p values are less than 0.05, and therefore this data is
statistically insignificant, and we cannot draw any conclusions from
it.</p>
</div>
<div id="logistic-regression" class="section level1">
<h1>Logistic Regression</h1>
<p><em>Data cleaning</em></p>
<p>The code block below creates a dataset clean of null/unusable data
for the desired variables. It then splits it into training and testing
data for the model to use.</p>
<pre class="r"><code>logData = originalData %>% select(`Athlete/Non-Athlete`,`Emotional Wellbeing`,`Social Wellbeing`,`Psychological Wellbeing`)
logData = logData %>% filter(logData$`Athlete/Non-Athlete` < 999)
logData = logData %>% filter(logData$`Emotional Wellbeing` < 999)
logData = logData %>% filter(logData$`Social Wellbeing` < 999)
logData = logData %>% filter(logData$`Psychological Wellbeing` < 999)
train <- sample(1:nrow(logData), nrow(logData)*0.75)
log_train <- logData[train, ]
log_test <- logData[-train, ]</code></pre>
<p><em>Model Building</em></p>
<p>This code block creates a logistic regression model using 3
“well-being” measures to attempt to predict whether the respondent is an
athlete or non/athlete, and then tests the accuracy of the model.</p>
<pre class="r"><code>log_model <- glm(as.factor(`Athlete/Non-Athlete`) ~ `Emotional Wellbeing` + `Social Wellbeing` + `Psychological Wellbeing`, data = log_train, family = binomial())
summary(log_model)</code></pre>
<pre><code>##
## Call:
## glm(formula = as.factor(`Athlete/Non-Athlete`) ~ `Emotional Wellbeing` +
## `Social Wellbeing` + `Psychological Wellbeing`, family = binomial(),
## data = log_train)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.09207 0.34041 -0.270 0.787
## `Emotional Wellbeing` 0.01861 0.04304 0.432 0.665
## `Social Wellbeing` 0.01646 0.02199 0.749 0.454
## `Psychological Wellbeing` -0.01358 0.02084 -0.652 0.515
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 715.05 on 515 degrees of freedom
## Residual deviance: 713.96 on 512 degrees of freedom
## AIC: 721.96
##
## Number of Fisher Scoring iterations: 3</code></pre>
<pre class="r"><code>test_pred <- predict(log_model, log_test, type = "response")
prediction <- ifelse(test_pred > 0.50, 1, 0)
confusion_matrix <- table(Predicted = prediction, Actual = log_test$`Athlete/Non-Athlete`)
confusion_matrix</code></pre>
<pre><code>## Actual
## Predicted 1 2
## 0 27 24
## 1 54 67</code></pre>
<p><em>Analysis</em></p>
<p>Interpreting the coefficients of our log model shows that per unit
increase in emotional wellbeing, the log odds of the individual being an
athlete increases by 0.018, per unit increase in social wellbeing, the
log odds of being an athlete increase by 0.042, and per unit increase in
psychological wellbeing, the log odds decrease by 0.036. This would
suggest that athletes have better social well-being, potentially due
team sports and sports communities, but they also have worse emotional
and psychological wellbeing coming out of the lockdown. However, none of
the p values are significant (<0.05), and therefore we cannot draw
conclusions from the data.</p>
</div>
<div id="paired-variables-graphs" class="section level1">
<h1>Paired variables graphs</h1>
<p><em>Data cleaning</em></p>
<p>This code block creates and filters the data to the necessary
variables for our future variable pairing</p>
<pre class="r"><code>mlrData <- originalData %>% select(`MHC-SF OVERALL`,`AIMS_ TOTAL`,`I consider myself an athlete`,`I have many goals related to sport`,`most of my friends are athletes`,`Sport is the most important part of my life`,`I spend more time thinking about sport than anything else`,`I feel bad about myself when I do badly in sport`,`I would be very depressed if I were injured and could not compete in sport`)
mlrData <- mlrData %>% filter(`AIMS_ TOTAL` <= 100, `MHC-SF OVERALL` <= 100,`I consider myself an athlete`<= 100,`I have many goals related to sport`<= 100,`most of my friends are athletes`<= 100,`Sport is the most important part of my life`<= 100,`I spend more time thinking about sport than anything else`<= 100,`I feel bad about myself when I do badly in sport`<= 100,`I would be very depressed if I were injured and could not compete in sport`<= 100)</code></pre>
<p><em>Model Building</em></p>
<p>This code block creates graphs to illustrate the relationship between
a series of athletic variables to ‘MHC-SF Overall’, a comprehensive
mental health statistic created by using other survey answers.</p>
<pre class="r"><code>pairs(mlrData,lower.panel=NULL,)</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p><em>Analysis</em></p>
<p>Its hard to determine any linear relationships from these graphhs due
to the data being largely evenly spread, not indicating a positive or
negative relationship between most variables. There is a strong positive
linear correlation between ‘AIMS_TOTAL’ with all the other predictor
variables, but this is to be expected because ‘AIMS_TOTAL’ is a
comprehensive athletic identity score, and the other predictors are very
similar metrics. The only variable that appears it could have a linear
correlation with ‘MHC-SF’ is ‘My goals are related to sports’, where we
can see a void in the lower left box, indicating some level of a
negative correlation between having athletic goals and mental health,
perhaps a consequence of not being able to meet those goals during the
lockdown.</p>
</div>
<div id="regression-trees" class="section level1">
<h1>Regression Trees</h1>
<p><em>Data cleaning</em></p>
<p>The code block below does some additional cleaning of the data,
selecting usable columns, and splits it into a training set and a test
set for use in regression trees.</p>
<pre class="r"><code>cleanData = originalData[c(2,3,4,7,8,9,10,12,14:27,34:84)]
for(i in 1:73){
cleanData = cleanData %>% filter(cleanData[i] < 100)
}
train <- sample(1:nrow(cleanData), nrow(cleanData)*0.75)
training = cleanData[train,]
testing = cleanData[-train,]</code></pre>
<p><em>Model Building</em></p>
<p>The code block below creates a regression tree to predict “emotional
wellbeing” using all available predictors, minus those too similar to
emotional wellbeing to be useful.</p>
<pre class="r"><code>specific_tree_model <- tree(cleanData$`Emotional Wellbeing` ~ cleanData$`Gender:` + cleanData$`Age Group:` + cleanData$`Country During Lockdown` + cleanData$`Marital Status:` + cleanData$`Smoking Status:` + cleanData$`Five Fruit and Veg` + cleanData$`Hours sleep:` + cleanData$`Shielded?` + cleanData$`Weeks Social Distancing` + cleanData$`# in lockdown bubble:` + cleanData$`Athlete/Non-Athlete` + cleanData$`AIMS_ TOTAL` + cleanData$`Social Identity` + cleanData$`I consider myself an athlete` + cleanData$`I have many goals related to sport` + cleanData$`most of my friends are athletes` + cleanData$`Exclusivity` + cleanData$`Sport is the most important part of my life` + cleanData$`I spend more time thinking about sport than anything else` + cleanData$`Negative Affectivity` + cleanData$`I feel bad about myself when I do badly in sport` + cleanData$`I would be very depressed if I were injured and could not compete in sport` + cleanData$`RES_TOTAL` + cleanData$`I tend to bounce back quickly after hard times` + cleanData$`I have a hard time making it through stressful events*` + cleanData$`It does not take me long to recover from a stressful event` + cleanData$`It is hard for me to snap back when something bad happens*` + cleanData$`I usually come through difficult times with little trouble` + cleanData$`I tend to take a long time to get over setbacks in my life*` + cleanData$`I miss having people around` + cleanData$`There are many people I can trust completely*` + cleanData$`There are enough people I feel close to*` + cleanData$`There are plenty of people I can rely on when I have problems*`, data = training)
plot(specific_tree_model)
text(specific_tree_model,pretty=0,cex=0.6)</code></pre>
<p><img 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width="672" /></p>
<pre class="r"><code>summary(specific_tree_model)</code></pre>
<pre><code>##
## Regression tree:
## tree(formula = cleanData$`Emotional Wellbeing` ~ cleanData$`Gender:` +
## cleanData$`Age Group:` + cleanData$`Country During Lockdown` +
## cleanData$`Marital Status:` + cleanData$`Smoking Status:` +
## cleanData$`Five Fruit and Veg` + cleanData$`Hours sleep:` +
## cleanData$`Shielded?` + cleanData$`Weeks Social Distancing` +
## cleanData$`# in lockdown bubble:` + cleanData$`Athlete/Non-Athlete` +
## cleanData$`AIMS_ TOTAL` + cleanData$`Social Identity` + cleanData$`I consider myself an athlete` +
## cleanData$`I have many goals related to sport` + cleanData$`most of my friends are athletes` +
## cleanData$Exclusivity + cleanData$`Sport is the most important part of my life` +
## cleanData$`I spend more time thinking about sport than anything else` +
## cleanData$`Negative Affectivity` + cleanData$`I feel bad about myself when I do badly in sport` +
## cleanData$`I would be very depressed if I were injured and could not compete in sport` +
## cleanData$RES_TOTAL + cleanData$`I tend to bounce back quickly after hard times` +
## cleanData$`I have a hard time making it through stressful events*` +
## cleanData$`It does not take me long to recover from a stressful event` +
## cleanData$`It is hard for me to snap back when something bad happens*` +
## cleanData$`I usually come through difficult times with little trouble` +
## cleanData$`I tend to take a long time to get over setbacks in my life*` +
## cleanData$`I miss having people around` + cleanData$`There are many people I can trust completely*` +
## cleanData$`There are enough people I feel close to*` + cleanData$`There are plenty of people I can rely on when I have problems*`,
## data = training)
## Variables actually used in tree construction:
## [1] "cleanData$RES_TOTAL"
## [2] "cleanData$`Hours sleep:`"
## [3] "cleanData$`I have many goals related to sport`"
## [4] "cleanData$`There are enough people I feel close to*`"
## [5] "cleanData$`There are plenty of people I can rely on when I have problems*`"
## [6] "cleanData$`Negative Affectivity`"
## [7] "cleanData$`I miss having people around`"
## [8] "cleanData$`I tend to bounce back quickly after hard times`"
## [9] "cleanData$`I spend more time thinking about sport than anything else`"
## [10] "cleanData$`Athlete/Non-Athlete`"
## Number of terminal nodes: 17
## Residual mean deviance: 4.54 = 1998 / 440
## Distribution of residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.94600 -1.04800 -0.04795 0.00000 1.16700 6.38500</code></pre>
<p>The code block below cross-validates the above tree to determine the
optimal size.</p>
<pre class="r"><code>#cross-validation of the above tree
cv.specific <- cv.tree(specific_tree_model)
cv.specific</code></pre>
<pre><code>## $size
## [1] 17 16 14 13 12 11 10 9 8 7 6 5 4 3 2 1
##
## $dev
## [1] 3703.456 3643.665 3591.319 3570.079 3555.190 3488.528 3469.535 3422.597
## [9] 3422.597 3428.623 3432.365 3266.242 3263.844 3356.941 3481.344 4041.769
##
## $k
## [1] -Inf 42.03636 46.93887 48.66117 51.07952 61.21364 64.56256
## [8] 67.61043 67.72487 72.14536 91.02252 99.93542 128.93241 188.88915
## [15] 240.15443 713.69477
##
## $method
## [1] "deviance"
##
## attr(,"class")
## [1] "prune" "tree.sequence"</code></pre>
<pre class="r"><code>plot(cv.specific$size,cv.specific$dev,type='b')</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAAA51BMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrY6AAA6AGY6OgA6Ojo6OmY6OpA6Zjo6ZmY6ZpA6ZrY6kLY6kNtmAABmADpmOgBmOjpmOmZmZjpmZmZmZpBmkLZmkNtmtttmtv+QOgCQOjqQZgCQZjqQZmaQkGaQkJCQkLaQtraQttuQ29uQ2/+2ZgC2Zjq2kDq2kGa2kJC2kLa2tma2tpC2tra2ttu229u22/+2/9u2///bkDrbkGbbtmbbtpDbtrbbttvb25Db27bb29vb2//b/9vb////tmb/25D/27b/29v//7b//9v///8qBu67AAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3dfWPbRoLYYSqRazX2dp2L69wq3sRXp7dxrWy6jWI32th3VmpZMfn9P08BvokvkiwMBgPM4Hn+2EgKAYII+Vu8DiczAIJM+l4AgFwJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQg07IBOAKKJn6joc4yo77UNlCV6o2LPMKYO/g8DGC0BBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAJ67aO6GagKKIuAXvcgBQXuQECvecxk5weA62QY0On529PT01/O3wdMe4eXu5FNAQVuk1tA3z3fGEz/0aumk98poI0eDoxXXgG9fLLzfSSHL5vNQECBeLIK6MVRHc0Hxwtf1L8cfNdoDgIKxJNTQD8+roL5YuMPb6qgfvZrk1kIKBBPTgE928tlndSvmsxCQIF4Mgro9NlksrvDfjGZ3GtyNl5AgXgyCmi1ubm3v37d327jMiYgHgG95jEupAfuIqOAVrvwB7tXLXWwC+9WTuCOMgro7GSvlvVh0ftNZmEwESCenAL64agq6OuNP1xW/dzbKL2VKALx5BTQ+jqmqpjHP5zWfl5cSd/oKiYBBSLKKqCz3452buU8+LbZDAQUiCevgM6mP20m9OBp0xGZBBSIJ7OAVqbvTn86Pj5+evoqYDw7AQXiyS+grQgoEI+AAgTKMKAdj0gPcEe5BbT7EekB7iivgKYYkR7gjrIKaJIR6QHuKKeAphmRHuCOcgpomhHpgx4LjFFGAU00In3gg4HxySigqQZUDnswMD4CGunBwPhkFNBkI9IHPRgYn4wCmm5E+pAHA+OTU0BTj0gvoMCtcgpo8hHpFRS4TVYBTT0ivYACt8kroIlHpBdQ4DaZBXSWdER6AQVuk19AG5hco9n0XS0ZUAIBvXX6rpYMKEGGAU04Ir2CArfILaBpR6QXUOAWeQU09Yj0AgrcIquAJh+RXkCBW+QU0PQj0gsocIucApp4RPrmDwfGJaOAJh+RPuDxwJhkFNDkAyoHPB4YEwGN+nhgTDIKaPIR6QMeD4xJRgFNPiJ9wOOBMckpoKlHpA94PDAmOQU0+Yj0AgrcJquAph6RPmQCYDzyCmjiEelDJgDGI7OAzpKOSB8yATAe+QW0FQEF4hHQyBMA45FvQKdvT39pvBMvoEA8+Qa06V2cc81froICNxHQ6FMAY5FTQP843/SuCuir6p+/N5mFgALxZBTQevTka3Q6GpOAAjcT0OhTAGORUUDnN3IeHK/85Why8Ofqn990OZydgAI3yymg89GXDlfDMSU6iaSgwE2yCuhs9s9qs/Ovix8FFOhZZgGdXT5ZjQkqoEDPcgvobPr35SB2Agr0LLuAzmYfqo3QR+8FFOhbhgGdTX+sNkJfCCjQsxwDurig6c9HAgr0Ks+Azi9oangN/VzIy1VQ4HqZBnR+QZOAAr3KNqCzy++b3YQ0J6BAPPkGNIiAAvEIaCfTAGMgoJ1MA4yBgHY0EVA+Ae1oIqB8AtrRRED5BLSjiYDyCWhHEwHlE9COJgLKJ6CdTQWUTkA7mwoonYB2NhVQOgHtbCqgdALa2VRA6QS0s6mA0gloZ1MBpRPQDicDyiagHU4GlE1AO5wMKJuAdjgZUDYB7XAyoGwC2uFkQNkEtNPpgJIJaKfTASUT0E6nA0omoJ1OB5RMQDudDiiZgHY6HVAyAe14QqBcAtrxhEC5BLTjCYFyCWjHEwLlEtCOJwTKJaAdTwiUS0A7nhAol4B2PiVQKgHtfEqgVALa+ZRAqQS08ymBUglo51MCpRLQzqcESiWgCSYFyiSgCSYFyiSgCSYFyiSgCSYFyiSgCSYFyiSgCSYFyiSgSaYFSiSgSaYFSiSgSaYFSiSgSaYFSiSgSaYFSiSgSaYFSiSgSaYFSiSgiSYGyiOgiSYGyiOgiSYGyiOgiSYGyiOgiSYGyiOgiSYGyiOgyaYGSiOgyaYGSiOgyaYGSiOgyaYGSiOgyaYGSiOgyaYGSiOgCScHyiKgCScHyiKgCScHyiKgCScHyiKgCScHyiKgCScHyiKgCScHypJhQKfnb09PT385fx8wbduXq6DAldwC+u755MqjV00nF1AgnrwCevlksu3wZbMZCCgQT1YBvTiqo/ngeOGL+peD7xrNQUCBeHIK6MfHVTBfbPzhTRXUz35tMgsBBeLJKaBne7msk/pVk1kIKBBPRgGdPptMdnfYLyaTe03Oxrd+uQoKrGUU0Gpzc29//bq/3UZAgXgEtBkBBdYyCmi1C3+we9WSXXigPxkFdHayV8v6sOj9JrMQUCCenAL64agq6OuNP1xW/dzbKL2VgALx5BTQ+jqmqpjHP5zWfl5cSd/oKqYIL1dBgZWsAjr77WjnVs6Db5vNQECBePIK6Gz602ZCD542HZFJQIF4MgtoZfru9Kfj4+Onp68CxrMTUCCe/ALaioAC8Qho8jkApcgwoH2OSC+gwJXcAtrziPRxZgGUIa+A9j4ifZxZAGXIKqD9j0gfZxZAGXIK6ABGpI8zC6AMOQV0ACPSx5kFUIaMAjqIEekFFFjLKKCDGFA50jyAEghoYwIKLGQU0EGMSB9pHkAJMgroIEakjzQPoAQ5BXQII9JHmgdQgpwCOogR6QUUWMkqoEMYkT7WTID8pQjo9B8hAyddq/8R6WPNBMhfioB+fDw5fBGvof2OSB9rJkD+EgW08rDx2HPtF+UaUWYbYSZA/pIcA32zGIXu4NHr/X/XJQEFupToJNL0py8Cj1reaPrul98bTySgQDzpzsL/sTz/0+5w6NvT0/lm7GJo5YO/Nl04AQWiSXoZ0+XzRUMfhSZ0cRnTvffLoZUnDW/kjPVyFRSopb4OdP6dRs0GQb5ytqzm/fnQyosx6RvdySmgQESJAzp983V4QOtbOQ9/+Lnaef/L8g6kuqiNvtNDQIF4kgZ0Xs/wM0nLwUTqEURWG54nPQwmIqDAQrqArr6QuPl3ES+tR6S/uBpCpNooTT6cnYACC4kCujp9dPjiun97N+vBkzdGUe5jQGUBBRaS3Au/vICp5UWgwwmoggK1ZLdytr8NaT0ifbXf3u8uvIACtUQBfdhi131tdcbo5GoY0LNeTiIJKFBLsgv/S5z7Ny+qcH55fv7jZPLgalu0j8uYBBSoZTWg8snq9qP/rML56PT0eeNbkQQUiCfhvfCnp9WW6LT5ACBXpj8uTka9XN+T1PSafAEF4kkV0N++WNTu4+PDNieT3n3/4OH8XP4/w+6qj/VyFRRIFtAfV5uLHx83/B7Nm0zffn/8Q+PNWQEF4kkT0HqP+/Dfj6qA1rcTNRxBKSYBBeJJEtD6ZPm31cZnfcByfUNmLwQUiCdJQBcXcC4CWl+M1GwIuhu8O+1rRPqI8wFyluQ60MUtRMuANr15aMf5fNrl3aGNb60XUCCeRHci1elcBrTp7eubLusrPw9fXl0ROvmy4cIJKBBNVgH9sByU5GV9T9LD4+Ojq3s677pwsV6uggJZ7cLPByV58EW1DfpkcS1UfWF9s4uiBBSIJ9VJpK/WAW06/seVasrPXi+3Qxcn8utT+o02QQUUiCdJQC+W19DXAb1o+jVGa+sLoM6uLiW9aHhVqYAC8SQJaJ2+gxd1QKd/b/5VxCvrg6fVJuj93b/ddeEEFIgmzZ1I86OXK6G3cm6OSC+gQP8S3Qs//ybNheDBRNa30U//7cGflhux1cZoPwFVUCDdcHaXz7+Yf7FH6Hdyzlbnorb0NCJ91DkBucppQOWLvd3/y8d9nYUXUCCrgM6PA/zp6gzU/H7OXkakjzonIFc5BXT28cnWEPT1qamGZ6QEFIin24BO355eJ/hL5qptzu2ANj0jJaBAPN0GdOvypSvBg4lsm/6j8Qn9iC9XQWH0cg5oyMIJKBBNol345/UVTD+cnv7Po8nB0/Bd+PYLJ6BANMm+E+lf3q9/bDYCXVQCCsSTajCRq8vdT0r4TqS4swLylHA80KWWX+nRjoAC8SQckf7a3xITUCAeAR3EvIAcJdqF3zjs2XQM5KgEFIgnyUmks41LPxuP/xGVgALxJAlofT394ivcp78d9XkdvYACEaW5DvRicQfSg1Yj0scgoEA8iUZj+vCk/Yj0MQgoEE+y4ezezUek/7zNiPQRRH25Cgojl9V4oO0JKBCPgA5kZkB+kgb03enpaW+XgM4JKBBP9wG9fP5fF5ct1VcwVR71mVABBeLpOqDTH1fXfZ6th1Pu8TS8gALxdB3Qk9UA9B+q7c/DH35+PunzTs7IL1dBYdw6Dmh9Bf1iKOWT5ZigdUiLGA80+tyA3HQc0JPVje9XYzCdlTKYiIDC2HX8nUjroZSrDc/loPTVT2UMZyegMHadBvRjfQPn5w9q1Y77wfyHB/Mbkh487KehAgrEkyqg6x8EFChF598Lv9iFr35YHfgs5TuRos8NyE2ik0gbX2Z8tvkVnalFfrkKCqPWcUDPFsN/bpw4KukyJgGFces4oPVY9JPP68Oeiw3Qd88nxYxIH392QF66vhPpw+IG+OVhz3lPSxmRPv7sgP6tbjq/22OjP/v2r5df1838cnHaqA7oo1JGpI8/O6B36y/PuMunO8Vwdue/r36afv+33/f+dUoCCtxmHc47FdSAyoOaH9CrjWwK6B4BBW6x+ZG+w8c7WUCn/1EfBv34r3/rdUh6AQVuMcyAXn69uHipvjXpr7GfsQEBBW4xyIBeHE3WAb26J6kHAgrcYogBrS8GXV7J9MePvV4IKqDALYYY0JPNMZRPCroXXkGhLAMM6NWwyrWCRmPqYoZAjwZ4GdPV13ns/5aYgAK3Gd6F9AIK5GJwt3JWu/AbI9hdlPOlcl3MEOjXoAYTqZ1tnHmvz8j3dx2TgALxJAno/OrPRy/Oz8/fPp/0uQEqoEBEaS6kX40KOlfQeKCdzBHIRapbOZ+v+/moz5vhBRTYE/wxTjeYyNvvj4+Pv3nR61giAgrsuusZo2snjboks5ENZyegkLsW/RTQ4c0RSKdNPgV0gHMEkmnXz44DurjraH4V06aC7kRSUMhXy3wK6CBnCaTQup9dB/TrBw9/rf9320MBBXrWPp+dHwOdxp57SwIKzMXoZ8cBnT7rcXf9OgIKzCLlM9kx0KFkVECBaP0U0GHOE+hQpHwmCOjkOwEFBiTW5ues85NIJ9WSfv5F/T/FnoUXUMhKxH52HdCLyXWKug5UQCEjMfPZ/a2cb74QUGAo4vazhy+V65WAwohFzqeADnSeQHzR+5nmWzm/P/6m33GU1zqJnYJCBuLn03B2g50pEFUX/RTQoc4UiKiTfBrObrAzBeLpqJ8COtSZAvF09SE1HuhAZwoMn2Ogg50rMHQCOti5AkMnoIOdKxBida4lzXNFn+P1f57+R30t/cd//Vuvl9QLKBTu6mx1kieLPsfr/nj59eLU+8fHk4O/xn7GBgQUyrYOZ5KCpgnoxdFkHdDJ5Kt2TzA9f3t6evrLecimrIBC0TayWUxAP1T9PPhyHrw/fqx+fBk+93fPN64nffSq8cJ1s0oVFIZh87OY4HOZJKAnk8m99xu/3A+d9+WTnUvyDxu2WEChaAUGdPpsc6Oz2hy9F3giqT4SMJk8OF6YD9V88F2zhRNQKFmBAd0eDzR8dND6AOrBi40/vDlqeluogELRBPRGZ3u5rJPa6JSUgELRCgxotQs/udrTvpgE7sJvzyZsZgIKRSswoPWm4/ogaH1GPuw6pus2XZtuzgooFK3Ey5jmV38+enF+fv62vgop8BzSgAOqoDAQJV5I/+Fo49Kj0MtAt0/mLwxkF15AoSd792wWeSvn1fXvj4Jvhj/Zq2V9WLTRRaUCCuW4vpRlDiby9vvj4+NvXrQYS6Tejr33euMPl8+abs8KKBQiZSdvWYjoc4w9wytn8yMAxz+c1n5eXEnf7ISUgEIJhhDPxXJEn2PsGW74bfNY6rym3zabgYBC7gax6bmULqB/nJ7+8n42/b3VzKc/bZ2Oetr0gEBnK30Y/zWhdEOq5yxdQH+r97fn39B5+PraB9zZ9N3pT8fHx09PXwUcThVQyNbA4llLFNAf56978RXHbUaza0tAIU8DrOcsVUDrsz+H/35UBbS+8Ch0MKYIBBQyNMh41pINqPxttfFZ3zR03R3tzQxvRPouZwwjN8xNz6VUAyrfny0DWt88FDyg8lBHpBdQ6MSg6zlLOqDyMqAtBlQe7Ij0nc4ZRmrg8awlHA90GdDw8UCHOyJ9p3OGERr6pudSTgEd7oj0nc4ZxiaTes7y2oUf7oj0nc4ZxiSfeNZSnUT6ah3Qs9CTSAMekb7TOcNo5FXPWaKAXiyvoa8DWv0ceBnTkAdUFlBoK7d41pIEtN52PHhRB3T690mRI9ILKLSR3abnUpo7kebf6bEeAqTAEem7nTUULdd6zpLdC19vg64u3QweTGTAI9J3O2soV77xrCUbzu7yeX3d5kHzm4euDHhE+m5nDWXKeNNzKasBlYc7In23s4YC5V/PWWYBHe6I9N3OGgpTRDxr6QI6fVdtNp63nPlgR6QXULijYuo5SxfQN6thQB62HJB+oCPSdzxvKERB8aylCejGSfimRy1bLco1uny27uYNBShp03MpTUBP6v3tP//t9H//JWlBBRTSu/6zVmA9Z+lu5Zx8udjhnv4YfiX9lj/ehuzDCyh07IatlfLiWUs4Iv11vzT17uv5nZurc0mHLz45xc7CCSh0at3JIoO5K9F4oBsbnS1GpJ8+Xwxot3FI9ctmsxJQ6NRGNsfwiUg4oPK1vzUx7+bymz0nB8fHx/VmaLOt2U7/k47h/QK32/wUjOATkWhA5SgBrQ+l/sv7xT/nJ6LqsZ0GcyvnKN4u8AkC2naO+3862xwCNPxbOU+W3dw4itr0gKqAQqcEtO0c9//08cnks9X189ubo02sDqVuHlJtekBVQKFTAtp2jtf8bfp8ddf65ZPgq5hW+/6bxwCGM6By1zOHLAho2znu/eXj1w8e1GfMP1/84+DBwsOGG6KrWG5uwwooDIqAtp3j3l+2xqO/0nRPfv2lcidXG7EDGpF+FG8X+ASXMbWd495fIgV0/bXGH45WZ47qpg7ka427nzsM0N4tRy6kbznH2DNcq0M8/0KQs9VlTI3vCxVQiOfGezZLvO39WjkFdHZRXzn/8Ifz879XJX368/OjxiOTCCjEcXMlR9TPvAI6/1akbQ1HdhJQaG9M25i3SxvQ+is5HrUZUXl7RPphDSYioJTh9jqK56ZEAX3zpD79M/9SuLaj2f3x8/HX86ugvvlhWMPZCShFuO0YpnjuSBPQxfnz1Q544J1IMXT8X94bi+zdeBZdPK+RJKB1OatqzjPa+MqjqAQUbnXzdZzqeY1Ug4ncez+/aPN+m8FEtr07/eX3xhMJKNxqZHcStZVoOLv6uGe9Hfpdm+Hs5s7nBz6XJ5MGdhLJG47sCWgjCQdUPlucP2oT0MvndTVfLr6lbu7LhgsnoHAbAW0kYUBPFqePWgR0eRbq4GU9pPLDxYj0Q7qQ3huO7AloI+kCujwE2uI7keb31D/4otoGXY6JN7RbOb3jyJ6ANpLoGOjku9Uh0BbfylmfxX+93A79bj3nAQ0m4h1H9gS0kVRn4Q9f/Y/FF8L9fbL5/R5NrIezO7saw25Qw9klmD/EtXdp582XMXGNNF/p8Xh14/r8p8AN0PXB06vh7IY1oHKC+cOn3fGK9417onf+vPMDN0tzJ9Li7E+1rVgHtOFXua+tY1n9IKBwg9tuxdx9xG23a3oz30Gie+Gnb46/eVX98+N/f/QqdMbrL5Ob/tuDPy0jXJVZQGHD7VuQt7dz+zFdLWFJchrO7mT/jNHZkL7WOMH84RM+cSumNMaVU0Av9i5aunw8sLPwCkrPbj6Lrp0dyCmg9Wn4yZ+ujqDO7+dsdk2pgFI4lyEllVNAZx+fbI2FV5+Raji4qIBSOAFNKn1A29wLX21zbgf0sOHw9gJK4QQ0qbwCum36j8bfDiKgFE5Ak8o5oAEElMIJaFI9HAP947z5QMixdP+O8p6lV27FTCqrk0jtCSilcytmSgKa3TPArVwvn1CS4ez+EXj3e3wCSvn0M51EAyofvhhGQwUUiCdRQOuv4AgeRSQiAQXiSXIM9M2TxXcZPWp83WZsCfKmoDAaqYaz++mLRUOf9rsrL6BAPOnOwv+x+Cr3fg+HCihl8rbrR9LLmC6fLxr6qLeECiglcs69L6mvA333fDIp+VZOASU9/exN4oBO33wtoBCVfPYnaUDn9ez1TJKAUhqbn31KF9D5znt9ALTP60FTvNW8nUlIP3uVKKCr00eHL2I/XTMCSlHks2dJ7oVfXsDU90WgMwGlLPrZt2S3cg7gNqSZgFIU+exdooA+7HnXfUVAKYbNzwFIsgv/S++77isCSin0cwgMqJzpkzBy8jkIyQI6/Y96M/Tjv/6t161RAaUINj8HItVlTF8vbj/6+Hhy8NfYz9iAgFIC/RyKNAG9OJqsAzqZfBX7Ke9OQCmAfA5GkoB+qPp58OV83/2PH6sfX8Z+zjsTULJn83NAkgT0ZDK5937jl/uxn/POBJTc6eeQJLmM6dnmRme1OXqv5MFEkj0LoySfg5LoQvqNAey2f0tMQMmazc+BEdBsn4Xx0c+hSbQLP/lu/dvFxC48BJDP4UlyEuls48x7fUa+v+uYBJRs6ecAJQno/OrPRy/Oz8/f1qMq97cBKqBkSz6HKM2F9B8WA4Iu9HgZqICSKZufw5RsRPp1P/v7TuOZtJEp/RyodIOJvP3++Pj4mxf9jmznXUiWvHEHynB2AIEEFCCQgAIEElCAQAIKA7O6YKXv5eDTBBSG5eqS6b6XhE8SUBiUdTgVNAMCCkOykU3v1uETUBiSzbeot+vgCWgnz+IQFoEENCsC2sWTKCihBDQrAtrBczgJQDABzYqAxn8KJwEIJ6BZEdAun8IHgKa8f7IioF0+hQ8ATdmDyYqAdvkUPgF8yt7ZRsfQcyKgXT6FTwC3uOFyDVdxZERAu3wKHwFucFsl9TMfAtrlU/gMjNKnAmgLsxwCGv8pnAQYt7tsXHprFEJAO3gOJwHG7Mb//uJZIAHt4kl8UMbr5j0Qb4kCCWgnz6Kfo+UY+KgIKMQkoKMioBCTgI6KgEJMAjoqAgoxCeioCCjEJKCjIqAQkxspRkVAISo3UoyJgEJcbqQYEQGFyPRzPAS00CcFuieghT4p0D0BLfZZScbu+mgJaJJnHfRKoR3HO0dMQNM87aDXCi3I56gJaNHPS8fkc+QEtPAnpkvyOXYCmuqJB71eCCGfZBjQ6fnb09PTX87fB0zb3xveZ6009t7JL6Dvnl/dKDd59Krp5D2+433YiiKf1PIK6OWTybbDl81m0Od73uetHPLJQlYBvTiq37cPjhe+qH85+K7RHPoNqI9cGeSTlZwC+vFxFcwXG394UwX1s1+bzKLX9/1oPnRlD6ZR8EujsZwCeraXyzqpXzWZRb/v/JF87ooezq3U10WYjAI6fTaZ7O6wX0wm95qcje87oGP46BU9oLB8siWjgFabm3v769f97TY9v/vH8OmL8JUWg92AHeRC0ScBTarv50+g/ZeqDfUQwAAXib5lFNBqF/5g96qlvHbhh7AAnWsd0IEeApBPrpFRQGcne7WsD4vebzKL3j8C5X8I2wZ0qN9qWf5/OQLkFNAPR1VBX2/84bLq595G6a36/xD0vwQdax/QdtNDQjkFtL6OqSrm8Q+ntZ8XV9I3uoppCJ/IASxCp24O4ORmd5q+wSK0O4Q6zEOwDFFWAZ39drTzwTv4ttkMBvChKP2D2X9A256EGupJLAYor4DOpj9tJvTgadMRmYbwmSj8g9n2GGbvJ6EGehKLQcosoJXpu9Ofjo+Pn56+ChjPbhAfiUEsRHdaBuiuW7CffPq2/S3+PxQR5BfQVobxkRjGUnSm3S5w34cAnMSiAQHtQen7hq0OId5xC1BAGYIMA5rniPRbSi9oK4kOAdx0OEBAaSC3gOY7Iv2WoSzHIOV9CIBRySugWY9Iv2UwCzJELQ8BXP9zqukZlawCmveI9FvsxHdFQEkop4DmPiL9luEsSWHueBKqs+kZlZwCmv2I9FsGtChlaXkSyoX0NJBRQPMfkX6Lj2dX2p2EcisnDWQU0BIGVN7kA9qVtv3TT+5KQPszqIUBmssooEWMSL9lWEsDNJVRQIsYkX5LeXuJxb0guFVOAS1iRPotxRW0tGVnaVEAABAySURBVNcDt8spoGWMSL9laMvTUmEvBz4lq4AWMSL9tsEtUCtlvRr4pLwCWsSI9FuK2okv6bXAXWQW0FkJI9JvGd4ShSvptcBd5BfQBj4xcNkwDHCRApXzSuCOBLRvQ1ymMMW8ELirfAM6fXv6S+Od+CF+xkspaCEvAxrIN6BN7+KcG+SHfJAL1VwhLwMaENABGOZSNVTEi4BmcgroH+eb3lUBfVX98/cmsxjmp7yInfgSXgM0lFFA69GTr5HvaExXBrpYTRTwEqAxAR2EoS7X3eX/CqC5jAI6v5Hz4HjlL0eTgz9X//wm3+HsrmS/E5/78kOQnAI6H33pcDUcU0EnkWb5FzTzxYcwWQV0Nvtntdn518WPZQV0wEt2F3kvPYTKLKCzyyerMUELC+iQF+2Tct9+hkC5BXQ2/ftyELvyAjrcZfuUjBcd2sguoLPZh2oj9NH74gKacUGzXXBoKcOAzqY/VhuhL4oL6LAX7jbZLji0lGNAFxc0/fmotIAOfOlulOliQ3t5BnR+QVPDa+jnhv1ZH/bS3SjTxYb2Mg3o/IKm4gKaJ+uU8co2oLPL75vdhDTnwx6fdcp45RvQID7s0VmljFi+AX13+kujkezmfNqjs0oZsewCej7fbV9+vfHhi4ZT+7THZo0yZnkF9PJ5Xc2Xs9nJajC7L5vNwMc9NmuUMcsqoB/mm52Tg5cX1f8+PD6uf/2q0Rx83COzQhm1nAI6H1H5wRfVNuiTKqKz5S1JL5vMwuc9MiuUUcspoGeTyWevl9uh383/Mn3WcBM0j8/7YL/Dfk8OywjdySigdS3n3axCem95BejF1Y93ksUH/urrSvpekk/KYBGhQxkFdD16SLUJen/3b3eUwwd+Hc7hF3TwCwjdyjGg1Q8FB3Qjm0Nf2uEXHrqVV0AXZ4ym//bgT8v99g8Nh2TK4BO/uYgDX9yBLx50LqOA1hd/7p4xOpusN0bvJIOPfD4BHfbSQQI5BfRi76Kly8flnYUXUMhGTgGtT8NP/nR10n1+P2ejk/A5fOazCeigFw6SyCmgs49PtsYArS+sb3YdfQ4fegGFbGQV0Hqbczugh6+bzSCDD30uAR3yskEieQV02/QfDfOZxac+l8uYBr1wkEbOAQ2Qw6c+jwvph7xskIqADk8Wt3IOeuEgEQEdIP2EPAgoIaxImAkoQaxHqAkoAaxHqAkozVmNMCegNGc1wpyA0pi1CAsCSmPWIiwIaA6GtdjDWhrokYDmYFAX1Q9qYaBXApqFIUVrQIsCPRPQPAynoINZEOifgGZiMAUdynLAAAhoLgZS0GEsBQyDgGZjGAUdxELAQAhoPoZQ0AEsAgyHgGZkAAXtfwlgQAQ0J70XtO/nh2ER0Kz0XdDc1x/EJaB56beg2a8+iEtAM9NrQfNffRCVgOamx4IWsPYgKgHNTn8FLWHtQUwCmp++ClrEyoOYBDRDPRW0jJUHEQlojnopaCHrDiIS0Cz1UNC+L0GFARLQPPUR0NTPCIMnoJlK/UrKWXMQj4BmKvUedTlrDuIR0FylLWhBKw7iEdBsJS1oSSsOohHQfCUsaFHrDaIR0IylK2hZ6w1iEdCcpSpoYasNYhHQrCUqaGmrDSIR0LwlKWhxaw0iEdDMpShoeWsN4hDQ3HVf0AJXGsQhoNnrvKAlrjSIQkDz13FBi1xnEIWAFqDbgpa5ziAGAS1BlwUtdJVBDAJahA4LWuoqgwgEtAzGi4ceCGghFBTSE9BSKCgkJ6DFKPm1wTAJKEAgAQUIJKAAgQQUIJCAsmey1PdywNAJKLsmEwWFOxHQArXr33pCBYVPENDytNuC3JhsFGsLWhDQ4rTcgtycaAyrC1oQ0NK03YIUULgzAS1N2wAKKNyZgJZGQCEZAS3NzQGc3OxO0wM7BLQ0AgrJCGhp2u/Cu4wJ7khAS9N6C9KF9HBXAlqa9luQbuWEOxLQ4rTfgtRPuBsBLY8tSEhEQAukn5CGgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBRhdQgHiiNyr2DGPqe2UDZYneqNgzpAeOdbRj/bUz4vU33ldekhG/gaOw/toZ8fob7ysvyYjfwFFYf+2MeP2N95WXZMRv4Cisv3ZGvP7G+8pLMuI3cBTWXzsjXn/jfeUlGfEbOArrr50Rr7/xvvKSjPgNHIX1186I1994X3lJRvwGjsL6a2fE62+8r7wkI34DR2H9tTPi9TfeV16SEb+Bo7D+2hnx+hvvKy/JiN/AUVh/7Yx4/Y33lZdkxG/gKKy/dka8/sb7yksy4jdwFNZfOyNef+N95SUZ8Rs4CuuvnRGvv/G+8pKM+A0chfXXzojX33hfeUlG/AaOwvprZ8Trb7yvvCQjfgNHYf21M+L1N95XXpIRv4GjsP7aGfH6G+8rL8mI38BRWH/tjHj9jfeVA7QkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIBmbvpssvZd3wuTm4+PP/v16rfL50eTycGj1/0tT242199I34gCmrmPj0f5vo2i+sxvBPS3o8VaPPhrj4uUla31N9I3ooBm7mIyyvdtDNOTyUYALqzHhm5cf2NagQKaubNRvV1jmu9zrgNQb0AdVnvv755sVoGbba+/sb4RBTRzJz7uYd7M99jXK6/6/N97X/9Qd+GrHpcrFzvrb6xvRAHNW/VxX3zuaeSy2tCcPHqy/tBX6/Hg5eLHD0dW6Sftrr/RvhEFNG/Vnuf9vpchR9UG58G3GydBqvW4+vxvtJSb7K6/0b4RBTRvF5PJd5dfV1sDD1/0vShZOTv48v3mWeRqPa4//yfjPJrXyO76G+0bUUDzdjY5+Mvy1OejMe5BhfqjXlnbAV0f+Bzp6ZBGdtffaN+IApq3k42LR0Z5DKqNjQBsRvPCWaS72QzoWN+IApq1+pTxwdP39V00Ex/7pgS0nY31N9o3ooBm7ePj9QmPM9cvNiWg7WyfhBvnG1FAS1FvAzh014iAtrN9K+zGX0f0RhTQYpz53DckoO1cH9BxvREFtBg+9005C9/ODQEd1RtRQIsxqvdtFK4DbUdABbQgNpyacidSOzfvwo/njSigWds43LRxHpS72b4Mx73wTW0fQx7nG1FAs1Z92Jdv4XpwxlHejNzC9p00RmNqamP9jfaNKKBZm1+//O37xTCWI/r//Th2BsOYHL4yHmgTuxfSj/GNKKB5+3B0dQed7aaGto7hGZG+sc31N9Y3ooBm7sOTia/yCbR9EuSfvhOpoa31N9I3ooDmbvqmHkTs86e/970g+dk5i+xbORvaXn/jfCMKKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUMZl+uyzX/teBoohoORu+myybOL0py8mk4Mvr/6w99A3X08qD198ak5wJwJK7tbZq36o3b+xgx+eTJbuvb91TnA3Akru1tm7WMTxq5s6+OFoMrm1oAJKQwJKMc5u2rJcOqn275/+v2ef/d+fqpJ+l2yxKJiAUoyz27P48fHk4OXiJNLFJ1ILdyOgFOMTAa324KtqzgNqX504BJTOTH+rT4o/el3/vBG3Rcg2H/ZgMpl8/vT31b+9P7v8uj6b/vv2Ix6+eH/dnJc1rLYvl4dAN0/Lbz5uI6A3PPdiwuW5qM1jpXsLAHMCSldW57wP6nLOu7iws6F4dWrnq+Wv95d/qna4tx9x+PqaOd8S0O3HbezC3/DcNwV0bwFgQUDpyLpo8xBubBVu7z5fPWzR1SpW/+XZxoRbJ88XE27P+eaA7jyuLvf8JNKvNz33DQHdWwBYElA6Up/z/nY2u6y2Auttz/V258a26Gzx98NX1T8vn23Uqt7S++1oMWFducMX9W708g+7c14XefUUqz/sPG5VxoP/ddNz77T9bBHM/QWAJQGlG4sd5qsf1t082d6DX/+6rFcd0MWBx+qnesKrM+bLOe3O+aaA7j6uvlNpuS358PW1z70d0IvlluveAsCKgNKNi/XW2qJrVZzmGaoitLUfXF+8uXlkcZnN2sn80OTJVbQu5n+4Zs7XBnT3cfN/VZ9Vqn153XNvBfRidbHo3gLAioDSjb1ris7W23NbDZrfPnTw57+tTnBvnKOfF7AK7r2Nf3d/f843BfT6q5qmzw7+2+qs0e5zbwa03hKeL+j+AsCKgNKNk9293So+X13395PViZ7FtUQbjZr/uHmiZ3FSZ3cONwV075mWj/7s1+mPy73znefeCGj9tPfXP+2elocFAaUbe/la7MNvbs8t//7T1rVE2wG9937zBvZYAV0dHdh97q1LBVbLub8AsCKgdGM/X/N9+GuPIr55flWx3S3Q3cvu9+ccEtCrZ9l87quAnlxdsbS/ALAioHRj/whkVaLvbqjabPbHz88Xm3fXHAPdufgy9BjoyfKU03ZAN597c07rxdxfAFgRULpxdQ58FasqT/f39uC3DztWP1aPXv1hXrx6Z3q7xLtz/vRZ+PXJp/urgJ5t3/K5fO7VHy4mG8+5vwCwIqB04+pypJPVXvvZ5LP/sxej9b+9Cuiye8uU1pezL681Olvt5G/N+aaA7j5u8fs8oB8WlyjtPvfGpagbxxn2FgBWBJSOLO8Dqs94L7fz5qdjdneH6629+bAh7xY3DM0f9Oj11X0/9Vnwg2+rzdY/Ns6db875U3cirZdgPR7ob8uL9Xef++qm0M1M7i8ALAkoHdm4/GeZo/mtlPev/vUiRifrh602/z4/2vh9PdD81Zx25nxjQHeXYPOCpGufezHhxh8Xh0b3FgCWBJSuXD7Zjc7ZxrHFq/srn63idFj/Xh+vXBbrcLmxenG0k6/tOd8Y0L0lWP++2pDcee4bArq/ALAgoHRmcd/kw6ubJTdOEG3eV/7ueR2o5Wibi2uXqtJ9/u3VjH7aHLVzd843B3R/Cd78pX6qB0/XJ7K2nvumgO4vAMwJKMPS9d2SvheeiASUYRFQMiKgDIuAkhEBZVgMeERGBJRhEVAyIqAMi4CSEQEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQg0P8HEcstkLl4cXsAAAAASUVORK5CYII=" width="672" /></p>
<p>The code block below prunes the tree to its optimal size, and tests
the pruned vs non-pruned trees to prove its optimization.</p>
<pre class="r"><code>#pruning the above tree
prune.specific <- prune.tree(specific_tree_model, best = 6)
plot(prune.specific)
text(prune.specific,pretty=0, cex = 0.6)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>summary(prune.specific)</code></pre>
<pre><code>##
## Regression tree:
## snip.tree(tree = specific_tree_model, nodes = c(13L, 10L, 4L,
## 7L))
## Variables actually used in tree construction:
## [1] "cleanData$RES_TOTAL"
## [2] "cleanData$`Hours sleep:`"
## [3] "cleanData$`There are enough people I feel close to*`"
## [4] "cleanData$`There are plenty of people I can rely on when I have problems*`"
## Number of terminal nodes: 6
## Residual mean deviance: 5.892 = 2657 / 451
## Distribution of residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.96100 -1.24400 0.05376 0.00000 1.75600 6.03700</code></pre>
<pre class="r"><code>#getting mse for non-pruned tree
prediction <- round(predict(specific_tree_model, newdata = testing))
mse <- mean((testing$`Emotional Wellbeing` - prediction)^2)
print("Testing mse of non-pruned tree")</code></pre>
<pre><code>## [1] "Testing mse of non-pruned tree"</code></pre>
<pre class="r"><code>mse</code></pre>
<pre><code>## [1] 13.08315</code></pre>
<pre class="r"><code>#getting mse for pruned tree
prediction <- round(predict(prune.specific, newdata = testing))
mse <- mean((testing$`Emotional Wellbeing` - prediction)^2)
print("Testing mse of pruned tree")</code></pre>
<pre><code>## [1] "Testing mse of pruned tree"</code></pre>
<pre class="r"><code>mse</code></pre>
<pre><code>## [1] 11.07877</code></pre>
<p><em>Analysis</em></p>
<p>Our pruned tree did significantly better on the test data than the
non-pruned, meaning we can say it is the optimal size and we can analyze
the pruned tree’s nodes to see the variables most found to be predictive
of mental health. While the non-pruned tree had nodes directly related
to athletics, pruning the tree actually removes these, leaving us with
only 4 variables: resilience, hours of sleep, ‘there are enough people I
feel close to’, and ‘there are plenty of people I can rely on when I
have problems’. These variables could be linked to athletics, but this
tree shows that athletics are not the most significant predictors of
mental health.</p>
<p><em>Model Building</em></p>
<p>The code block below creates a regression tree to predict emotional
wellbeing using only variables related to sports and athletics.</p>
<pre class="r"><code>#this tree only uses predictors that are related to sports and athletics
morespecific_tree_model <- tree(cleanData$`Emotional Wellbeing` ~ cleanData$`Athlete/Non-Athlete` + cleanData$`AIMS_ TOTAL` + cleanData$`I consider myself an athlete` + cleanData$`I have many goals related to sport` + cleanData$`most of my friends are athletes` + cleanData$`Sport is the most important part of my life` + cleanData$`I spend more time thinking about sport than anything else` + cleanData$`I feel bad about myself when I do badly in sport` + cleanData$`I would be very depressed if I were injured and could not compete in sport`, data = training)
plot(morespecific_tree_model)
text(morespecific_tree_model,pretty=0,cex=0.7)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>summary(morespecific_tree_model)</code></pre>
<pre><code>##
## Regression tree:
## tree(formula = cleanData$`Emotional Wellbeing` ~ cleanData$`Athlete/Non-Athlete` +
## cleanData$`AIMS_ TOTAL` + cleanData$`I consider myself an athlete` +
## cleanData$`I have many goals related to sport` + cleanData$`most of my friends are athletes` +
## cleanData$`Sport is the most important part of my life` +
## cleanData$`I spend more time thinking about sport than anything else` +
## cleanData$`I feel bad about myself when I do badly in sport` +
## cleanData$`I would be very depressed if I were injured and could not compete in sport`,
## data = training)
## Variables actually used in tree construction:
## [1] "cleanData$`I feel bad about myself when I do badly in sport`"
## [2] "cleanData$`most of my friends are athletes`"
## [3] "cleanData$`I have many goals related to sport`"
## [4] "cleanData$`I consider myself an athlete`"
## [5] "cleanData$`AIMS_ TOTAL`"
## Number of terminal nodes: 6
## Residual mean deviance: 7.895 = 3561 / 451
## Distribution of residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -10.4100 -1.6530 0.3469 0.0000 2.0000 6.0000</code></pre>
<p>The code below cross-validates and prunes the tree to its optimal
size, as well as a third tree that was pruned to a manually selected
value that is experimentally more accurate. Then tests all 3 models for
their MSE.</p>
<pre class="r"><code>#finding optimal tree size
cv.tree(morespecific_tree_model)</code></pre>
<pre><code>## $size
## [1] 6 5 4 3 1
##
## $dev
## [1] 4521.798 4242.766 4238.638 4144.171 4172.747
##
## $k
## [1] -Inf 62.56712 67.10020 99.87626 119.41986
##
## $method
## [1] "deviance"
##
## attr(,"class")
## [1] "prune" "tree.sequence"</code></pre>
<pre class="r"><code>tree_size = c(6,5,4,3,1)
optimalSize=c()
#for(i in 1:100){
optimalSize = c(optimalSize,tree_size[which.min(cv.tree(morespecific_tree_model)$dev)])
#}
table(optimalSize)</code></pre>
<pre><code>## optimalSize
## 5
## 1</code></pre>
<pre class="r"><code>#getting mean square error rate for non-pruned tree
prediction = round(predict(morespecific_tree_model, newdata = testing))
mse <- mean((testing$`Emotional Wellbeing` - prediction)^2)
print("Testing mse of non-pruned tree")</code></pre>
<pre><code>## [1] "Testing mse of non-pruned tree"</code></pre>
<pre class="r"><code>mse</code></pre>
<pre><code>## [1] 9.374179</code></pre>
<pre class="r"><code>#creating pruned tree
newtree=prune.tree(morespecific_tree_model,best=5)
plot(newtree)
text(newtree,pretty=0,cex=0.7)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#getting mse for pruned tree
prediction = round(predict(newtree, newdata = testing))
mse <- mean((testing$`Emotional Wellbeing` - prediction)^2)
print("Testing mse of pruned tree")</code></pre>
<pre><code>## [1] "Testing mse of pruned tree"</code></pre>
<pre class="r"><code>mse</code></pre>
<pre><code>## [1] 9.295405</code></pre>
<pre class="r"><code>#creating extra pruned tree
manualTree=prune.tree(morespecific_tree_model,best=3)
plot(manualTree)
text(manualTree,pretty=0,cex=0.7)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#getting mse for pruned tree
prediction = round(predict(manualTree, newdata = testing))
mse <- mean((testing$`Emotional Wellbeing` - prediction)^2)
print("Testing mse of manually pruned tree")</code></pre>
<pre><code>## [1] "Testing mse of manually pruned tree"</code></pre>
<pre class="r"><code>mse</code></pre>
<pre><code>## [1] 9.113786</code></pre>
<p><em>Analysis</em></p>
<p>Creating a tree of only sport-related metrics both allows us to see
that it actually has a lower mean squared error rate than the tree
created with more metrics, and also see the sport variables considered
to be the most significant predictors. ‘I feel bad about myself when I
do badly in sport’, ‘most of my friends are athletes’, ‘I consider
myself an athlete’, and ‘AIMS_TOTAL’ were all variables used in the
pruned tree as predictors. Analyzing the predictions, we can see the
best mental health comes from not letting sports/losing significantly
decrease your mood, but still having a community of athlete friends. We
can also see the worst mental health comes from those who are affected
by losing, but also do not consider themselves athletes.</p>
</div>
<div id="anova-test" class="section level1">
<h1>ANOVA test</h1>
<p><em>Model Building</em></p>
<p>The code below uses an ANOVA test to see if there is a significant
difference in the means of Emotional Wellbeing for Athletes
vs. Non-Athletes.</p>
<pre class="r"><code>#ANOVA Comparison between the mean well being of non athletes and the mean well being of athletes
cleanData$`Athlete/Non-Athlete`[cleanData$`Athlete/Non-Athlete` == 1] <- "Athlete"
cleanData$`Athlete/Non-Athlete`[cleanData$`Athlete/Non-Athlete` == 2] <- "Non-Athlete"
aov_results <- aov(cleanData$`Emotional Wellbeing` ~ factor(cleanData$`Athlete/Non-Athlete`))
summary(aov_results)</code></pre>
<pre><code>## Df Sum Sq Mean Sq F value Pr(>F)
## factor(cleanData$`Athlete/Non-Athlete`) 1 12 12.078 1.368 0.243
## Residuals 455 4017 8.829</code></pre>
<pre class="r"><code>boxplot(cleanData$`Emotional Wellbeing` ~ factor(cleanData$`Athlete/Non-Athlete`), col = c("red", "blue"))</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>The code below uses an ANOVA to see if there is a signifcant
difference in the means of Resiliency Total for Athletes
vs. Non-Athletes.</p>
<pre class="r"><code>#ANOVA Comparison between the mean resliency of non athletes and the mean well being of athletes
aov_results <- aov(cleanData$`RES_TOTAL` ~ factor(cleanData$`Athlete/Non-Athlete`))
summary(aov_results)</code></pre>
<pre><code>## Df Sum Sq Mean Sq F value Pr(>F)
## factor(cleanData$`Athlete/Non-Athlete`) 1 34 33.73 1.395 0.238
## Residuals 455 11002 24.18</code></pre>
<p><em>Analysis</em></p>
<p>Comparing the sum squares, we can see that the noise caused by
residuals is significantly greater than the effect of being an athlete,
and that is also represented in the p-value of 0.238, which is greater
than 0.05 and thus is not statistically significant, and we cannot say
that there is a significant difference between athletes and non
athletes.</p>
</div>
<div id="multiple-linear-regression-part-2" class="section level1">
<h1>Multiple Linear Regression (part 2!)</h1>
<p><em>Model Building</em></p>
<p>The code below runs a multiple linear regression with Emotional
Wellbeing as the response variable and belonging to a community, the
number of people in the lockdown bubble, and having trusting
relationships as the predictors.</p>
<pre class="r"><code>#MLR
fit <- lm(cleanData$`Emotional Wellbeing` ~ cleanData$`That you belonged to a community (like a social group or your neighbourhood)` + cleanData$`# in lockdown bubble:` + cleanData$`That you had warm and trusting relationships with others`)
summary(fit)</code></pre>
<pre><code>##
## Call:
## lm(formula = cleanData$`Emotional Wellbeing` ~ cleanData$`That you belonged to a community (like a social group or your neighbourhood)` +
## cleanData$`# in lockdown bubble:` + cleanData$`That you had warm and trusting relationships with others`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4071 -1.3605 0.3082 1.6406 6.6395
##
## Coefficients:
## Estimate
## (Intercept) 5.72560
## cleanData$`That you belonged to a community (like a social group or your neighbourhood)` 0.80909
## cleanData$`# in lockdown bubble:` -0.09490
## cleanData$`That you had warm and trusting relationships with others` 0.72991
## Std. Error
## (Intercept) 0.44167
## cleanData$`That you belonged to a community (like a social group or your neighbourhood)` 0.07891
## cleanData$`# in lockdown bubble:` 0.07583
## cleanData$`That you had warm and trusting relationships with others` 0.09977
## t value
## (Intercept) 12.963
## cleanData$`That you belonged to a community (like a social group or your neighbourhood)` 10.253
## cleanData$`# in lockdown bubble:` -1.252
## cleanData$`That you had warm and trusting relationships with others` 7.316
## Pr(>|t|)
## (Intercept) < 2e-16
## cleanData$`That you belonged to a community (like a social group or your neighbourhood)` < 2e-16
## cleanData$`# in lockdown bubble:` 0.211
## cleanData$`That you had warm and trusting relationships with others` 1.17e-12
##
## (Intercept) ***
## cleanData$`That you belonged to a community (like a social group or your neighbourhood)` ***
## cleanData$`# in lockdown bubble:`
## cleanData$`That you had warm and trusting relationships with others` ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.298 on 453 degrees of freedom
## Multiple R-squared: 0.406, Adjusted R-squared: 0.4021
## F-statistic: 103.2 on 3 and 453 DF, p-value: < 2.2e-16</code></pre>
<pre class="r"><code>avPlots(fit)</code></pre>