Main Analysis
Julie
12/23/2019
Question
What are the independent effects of meditation, sleep, exercise, and cycle on the following DVs:
mood - general (-3 to +3 scale)
anger (0 to 3 scale)
anxiety (0 to 3 scale)
energy (0 to 3 scale)
motivation (0 to 3 scale)
creativity (0 to 3 scale)
focus (0 to 3 scale)
clarity of mind (0 to 3 scale)
stability - amount of fluctuation throughout the day (0 to 3 scale)
breakout - skin (-3 to 0 scale)
Regression Output
# mood
lm_mood <- summary(lm(mood ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_mood##
## Call:
## lm(formula = mood ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1739 -0.5618 0.1758 0.6667 1.9724
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1541358 0.1622262 7.114 2.11e-12 ***
## meditationMins 0.0007096 0.0004821 1.472 0.14137
## sleepHours -0.0396881 0.0197535 -2.009 0.04478 *
## exerciseMins 0.0018174 0.0005258 3.456 0.00057 ***
## cycleProp -0.0988869 0.0999263 -0.990 0.32260
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9195 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.01901, Adjusted R-squared: 0.01519
## F-statistic: 4.976 on 4 and 1027 DF, p-value: 0.0005641# anger
lm_anger <- summary(lm(anger ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_anger##
## Call:
## lm(formula = anger ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4359 -0.2770 -0.1845 0.1313 2.6247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.927e-02 8.024e-02 0.489 0.625
## meditationMins -2.534e-05 2.385e-04 -0.106 0.915
## sleepHours 1.010e-02 9.771e-03 1.033 0.302
## exerciseMins 3.196e-04 2.601e-04 1.229 0.219
## cycleProp 2.685e-01 4.943e-02 5.432 6.96e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4548 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.03105, Adjusted R-squared: 0.02728
## F-statistic: 8.229 on 4 and 1027 DF, p-value: 1.562e-06# anxiety
lm_anxiety <- summary(lm(anxiety ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_anxiety##
## Call:
## lm(formula = anxiety ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.06903 -0.63898 0.03361 0.51895 1.98749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1371270 0.1227993 9.260 <2e-16 ***
## meditationMins -0.0004059 0.0003649 -1.112 0.2662
## sleepHours -0.0097306 0.0149526 -0.651 0.5153
## exerciseMins -0.0003236 0.0003980 -0.813 0.4164
## cycleProp -0.1603951 0.0756405 -2.120 0.0342 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6961 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.006585, Adjusted R-squared: 0.002716
## F-statistic: 1.702 on 4 and 1027 DF, p-value: 0.1473# motivation
lm_motivation <- summary(lm(motivation ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_motivation##
## Call:
## lm(formula = motivation ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.36172 -0.30966 0.03448 0.46911 1.76878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4037703 0.1148178 12.226 <2e-16 ***
## meditationMins 0.0001072 0.0003412 0.314 0.7535
## sleepHours -0.0097285 0.0139808 -0.696 0.4867
## exerciseMins 0.0005293 0.0003722 1.422 0.1552
## cycleProp -0.1577987 0.0707242 -2.231 0.0259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6508 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.007182, Adjusted R-squared: 0.003315
## F-statistic: 1.857 on 4 and 1027 DF, p-value: 0.1158# energy
lm_energy <- summary(lm(energy ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_energy##
## Call:
## lm(formula = energy ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4888 -0.3513 0.1034 0.3467 1.6368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3930313 0.0868022 16.048 <2e-16 ***
## meditationMins -0.0002392 0.0002580 -0.927 0.3540
## sleepHours -0.0017668 0.0105695 -0.167 0.8673
## exerciseMins 0.0007247 0.0002813 2.576 0.0101 *
## cycleProp -0.0689568 0.0534675 -1.290 0.1974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.492 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.008733, Adjusted R-squared: 0.004872
## F-statistic: 2.262 on 4 and 1027 DF, p-value: 0.06068# creativity
lm_creativity <- summary(lm(creativity ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_creativity##
## Call:
## lm(formula = creativity ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.66932 -0.36374 -0.25620 0.04287 2.58773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0999002 0.1895006 -0.527 0.59844
## meditationMins -0.0007102 0.0003866 -1.837 0.06715 .
## sleepHours 0.0806920 0.0245802 3.283 0.00114 **
## exerciseMins -0.0003077 0.0005635 -0.546 0.58543
## cycleProp -0.2367765 0.1211531 -1.954 0.05153 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6309 on 318 degrees of freedom
## (1138 observations deleted due to missingness)
## Multiple R-squared: 0.06178, Adjusted R-squared: 0.04998
## F-statistic: 5.235 on 4 and 318 DF, p-value: 0.000427# focus
lm_focus <- summary(lm(focus ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_focus##
## Call:
## lm(formula = focus ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.50080 -0.36831 0.07038 0.48443 1.73354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5291633 0.1096004 13.952 < 2e-16 ***
## meditationMins -0.0004817 0.0003257 -1.479 0.13945
## sleepHours -0.0127714 0.0133455 -0.957 0.33880
## exerciseMins 0.0008963 0.0003552 2.523 0.01179 *
## cycleProp -0.1750443 0.0675104 -2.593 0.00965 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6212 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.01509, Adjusted R-squared: 0.01126
## F-statistic: 3.935 on 4 and 1027 DF, p-value: 0.003549# clarity of mind
lm_clarityOfMind <- summary(lm(clarityOfMind ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_clarityOfMind##
## Call:
## lm(formula = clarityOfMind ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.42264 -0.33059 -0.04332 0.54837 1.82445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.403e+00 1.158e-01 12.116 <2e-16 ***
## meditationMins 1.787e-05 3.442e-04 0.052 0.9586
## sleepHours -7.960e-03 1.410e-02 -0.564 0.5726
## exerciseMins 7.394e-04 3.754e-04 1.970 0.0492 *
## cycleProp -1.777e-01 7.135e-02 -2.491 0.0129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6565 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.00971, Adjusted R-squared: 0.005853
## F-statistic: 2.517 on 4 and 1027 DF, p-value: 0.03992# stability
lm_stability <- summary(lm(stability ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_stability##
## Call:
## lm(formula = stability ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5339 -0.3883 0.2505 0.7153 1.4137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6968312 0.1486634 11.414 < 2e-16 ***
## meditationMins 0.0006230 0.0004418 1.410 0.159
## sleepHours 0.0858748 0.0181020 4.744 2.39e-06 ***
## exerciseMins -0.0004927 0.0004819 -1.022 0.307
## cycleProp -0.1426426 0.0915720 -1.558 0.120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8427 on 1027 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.02568, Adjusted R-squared: 0.02188
## F-statistic: 6.766 on 4 and 1027 DF, p-value: 2.244e-05# breakouts
lm_breakout <- summary(lm(breakout ~ meditationMins + sleepHours + exerciseMins + cycleProp, Diary))
lm_breakout##
## Call:
## lm(formula = breakout ~ meditationMins + sleepHours + exerciseMins +
## cycleProp, data = Diary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2330 -0.3952 -0.1422 0.3970 1.0231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9960639 0.1561578 -6.379 5.84e-10 ***
## meditationMins 0.0011549 0.0005981 1.931 0.0543 .
## sleepHours 0.0247742 0.0194010 1.277 0.2025
## exerciseMins -0.0008535 0.0007304 -1.168 0.2435
## cycleProp -0.0542452 0.0994995 -0.545 0.5860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5174 on 340 degrees of freedom
## (1116 observations deleted due to missingness)
## Multiple R-squared: 0.0189, Adjusted R-squared: 0.00736
## F-statistic: 1.638 on 4 and 340 DF, p-value: 0.1643Visualizations:
MEDITATION
Nothing!! Literally - meditation predicts nothing LOL
ggplot(Diary, aes(x = meditationMins, y = mood)) +
geom_jitter(alpha = .1, height = .1, width = .1) +
geom_smooth(method = "lm") +
labs(y = "General mood (-3 to +3)",
x = "Meditation (Minutes)") +
ggtitle("General Mood as a Function of Meditation") +
xlim(1,120) # limit to max of 120 to avoid long tail (max in data is 630 from meditation retreats) 
diaryLong <- Diary %>%
pivot_longer(cols = c("anger", "anxiety", "energy", "motivation", "creativity", "focus", "clarityOfMind", "stability"), names_to = "emotionType", values_to = "intensity")
ggplot(diaryLong, aes(x = meditationMins, y = intensity, color = emotionType)) +
geom_jitter(alpha = .1, height = .1, width = .1) +
geom_smooth(method = "lm") +
labs(y = "Intensity (0-3)",
x = "Meditation (Minutes)",
color = "Affective Dimension") +
ggtitle("Various DVs as a Function of Meditaton") +
xlim(1,120) # limit to max of 120 to avoid long tail (max in data is 630 from meditation retreats)
SLEEP
More sleep –> WORSE(!) general mood (beta = -0.03969, p = 0.04478).
More sleep –> more creativity (beta = 0.08069, p = 0.00114).
More sleep –> more stability (beta = 0.08587, p = 0).
ggplot(Diary, aes(x = sleepHours, y = mood)) +
geom_jitter(alpha = .1, height = .1, width = .1) +
geom_smooth(method = "lm") +
labs(y = "General mood (-3 to +3)",
x = "Sleep (Hours)") +
ggtitle("General Mood as a Function of Sleep") 
ggplot(diaryLong, aes(x = sleepHours, y = intensity, color = emotionType)) +
geom_jitter(alpha = .1, height = .1, width = .1) +
geom_smooth(method = "lm") +
labs(y = "Intensity (0-3)",
x = "Sleep (Hours)",
color = "Affective Dimension") +
ggtitle("Various DVs as a Function of Sleep") 
EXERCISE
More exercise –> better general mood (beta = 0.00182, p = 5.710^{-4}).
More exercise –> more energy (beta = 7.210^{-4}, p = 0.01014).
More exercise –> more focus (beta = 910^{-4}, p = 0.01179).
More exercise –> more clarity of mind (beta = 7.410^{-4}, p = 0.04916).
ggplot(Diary, aes(x = exerciseMins, y = mood)) +
geom_jitter(alpha = .1, height = .1, width = .1) +
geom_smooth(method = "lm") +
labs(y = "General mood (-3 to +3)",
x = "Exercise (Mins)") +
ggtitle("General Mood as a Function of Exercise")
ggplot(diaryLong, aes(x = exerciseMins, y = intensity, color = emotionType)) +
geom_jitter(alpha = .1, height = .25, width = .5) +
geom_smooth(method = "lm") +
labs(y = "Emotion intensity (0-3)",
x = "Exercise (Mins)",
color = "Affective Dimension") +
ggtitle("Various DVs as a Function of Exercise") 
CYCLE
Later in cycle –> more anger (beta = 0.26849, p = 0).
Later in cycle –> LESS anxiety (beta = -0.1604, p = 0.0342).
Later in cycle –> less motivation (beta = -0.1578, p = 0.02588).
Later in cycle –> less focus (beta = -0.17504, p = 0.00965).
Later in cycle –> less clarity of mind (beta = -0.17774, p = 0.01289).
ggplot(Diary, aes(x = cycleProp, y = mood)) +
geom_jitter(alpha = .1, height = .1, width = .1) +
geom_smooth(method = "lm") +
labs(y = "General mood (-3 to +3)",
x = "Single Cycle Timespan") +
ggtitle("General Mood as a Function of Cycle")
ggplot(diaryLong, aes(x = cycleProp, y = intensity, color = emotionType)) +
geom_jitter(alpha = .1, height = .2) +
geom_smooth(method = "lm") +
labs(y = "Emotion intensity (0-3)",
x = "Single Cycle Timespan",
color = "Affective Dimension") +
ggtitle("Various DVs as a Function of Cycle") 