aim4_85
Brittany Jaso
10/11/2018
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## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
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## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package## Learn more about sjPlot with 'browseVignettes("sjPlot")'.all4 <- read.csv("all4.csv", header = TRUE)
all_75 <- subset(all4, nd_resprate >= 0.75)
all_70 <- subset(all4, nd_resprate >= 0.70)
md3 <- read.csv("md3.csv", header = TRUE)
aa <- all_70[!is.na(all_70["NAD_Mean"]),]
# Aim 4 Analyses
#### Correlation matrix of the individual item means
indiv_means <- aa[c("anxious_mean", "nervous_mean", "upset_mean", "sluggish_mean",
"irritable_mean", "content_mean", "relaxed_mean", "excited_mean",
"happy_mean", "attentive_mean")]
indiv_means <- data.frame(indiv_means)
View(indiv_means)
indiv_means_cor <- cor(indiv_means, y= NULL, use="complete.obs", method = "pearson")
corrplot(indiv_means_cor, type = "upper", order = "hclust",
tl.col = "black")
indiv_means <- data.frame(indiv_means)
corrplot.mixed(indiv_means_cor, lower.col = "black", number.cex = .7)
corrplot(indiv_means_cor, method = "number", type = "upper")
indiv_means_matrix <- as.matrix(indiv_means)
rcorr(indiv_means_matrix, type="pearson")## anxious_mean nervous_mean upset_mean sluggish_mean
## anxious_mean 1.00 0.93 0.76 0.63
## nervous_mean 0.93 1.00 0.83 0.63
## upset_mean 0.76 0.83 1.00 0.67
## sluggish_mean 0.63 0.63 0.67 1.00
## irritable_mean 0.78 0.78 0.89 0.77
## content_mean -0.42 -0.44 -0.56 -0.41
## relaxed_mean -0.63 -0.56 -0.49 -0.39
## excited_mean -0.11 -0.08 -0.12 -0.17
## happy_mean -0.40 -0.43 -0.56 -0.41
## attentive_mean -0.28 -0.27 -0.32 -0.49
## irritable_mean content_mean relaxed_mean excited_mean
## anxious_mean 0.78 -0.42 -0.63 -0.11
## nervous_mean 0.78 -0.44 -0.56 -0.08
## upset_mean 0.89 -0.56 -0.49 -0.12
## sluggish_mean 0.77 -0.41 -0.39 -0.17
## irritable_mean 1.00 -0.51 -0.50 -0.15
## content_mean -0.51 1.00 0.75 0.69
## relaxed_mean -0.50 0.75 1.00 0.53
## excited_mean -0.15 0.69 0.53 1.00
## happy_mean -0.51 0.95 0.74 0.73
## attentive_mean -0.37 0.68 0.52 0.63
## happy_mean attentive_mean
## anxious_mean -0.40 -0.28
## nervous_mean -0.43 -0.27
## upset_mean -0.56 -0.32
## sluggish_mean -0.41 -0.49
## irritable_mean -0.51 -0.37
## content_mean 0.95 0.68
## relaxed_mean 0.74 0.52
## excited_mean 0.73 0.63
## happy_mean 1.00 0.69
## attentive_mean 0.69 1.00
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## n= 85
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##
## P
## anxious_mean nervous_mean upset_mean sluggish_mean
## anxious_mean 0.0000 0.0000 0.0000
## nervous_mean 0.0000 0.0000 0.0000
## upset_mean 0.0000 0.0000 0.0000
## sluggish_mean 0.0000 0.0000 0.0000
## irritable_mean 0.0000 0.0000 0.0000 0.0000
## content_mean 0.0000 0.0000 0.0000 0.0001
## relaxed_mean 0.0000 0.0000 0.0000 0.0002
## excited_mean 0.3177 0.4690 0.2633 0.1225
## happy_mean 0.0001 0.0000 0.0000 0.0001
## attentive_mean 0.0091 0.0135 0.0030 0.0000
## irritable_mean content_mean relaxed_mean excited_mean
## anxious_mean 0.0000 0.0000 0.0000 0.3177
## nervous_mean 0.0000 0.0000 0.0000 0.4690
## upset_mean 0.0000 0.0000 0.0000 0.2633
## sluggish_mean 0.0000 0.0001 0.0002 0.1225
## irritable_mean 0.0000 0.0000 0.1620
## content_mean 0.0000 0.0000 0.0000
## relaxed_mean 0.0000 0.0000 0.0000
## excited_mean 0.1620 0.0000 0.0000
## happy_mean 0.0000 0.0000 0.0000 0.0000
## attentive_mean 0.0006 0.0000 0.0000 0.0000
## happy_mean attentive_mean
## anxious_mean 0.0001 0.0091
## nervous_mean 0.0000 0.0135
## upset_mean 0.0000 0.0030
## sluggish_mean 0.0001 0.0000
## irritable_mean 0.0000 0.0006
## content_mean 0.0000 0.0000
## relaxed_mean 0.0000 0.0000
## excited_mean 0.0000 0.0000
## happy_mean 0.0000
## attentive_mean 0.0000indiv_mssd <- aa[c("anxious_mssd", "nervous_mssd", "upset_mssd", "sluggish_mssd",
"irritable_mssd", "content_mssd", "relaxed_mssd", "excited_mssd",
"happy_mssd", "attentive_mssd")]
indiv_mssd <- data.frame(indiv_mssd)
View(indiv_mssd)
indiv_mssd_cor <- cor(indiv_mssd, y= NULL, use="complete.obs", method = "pearson")
corrplot(indiv_mssd_cor, type = "upper", order = "hclust",
tl.col = "black")
indiv_mssd_matrix <- as.matrix(indiv_mssd)
rcorr(indiv_mssd_matrix, type="pearson")## anxious_mssd nervous_mssd upset_mssd sluggish_mssd
## anxious_mssd 1.00 0.84 0.59 0.63
## nervous_mssd 0.84 1.00 0.50 0.57
## upset_mssd 0.59 0.50 1.00 0.35
## sluggish_mssd 0.63 0.57 0.35 1.00
## irritable_mssd 0.71 0.67 0.60 0.67
## content_mssd 0.63 0.51 0.72 0.41
## relaxed_mssd 0.72 0.57 0.67 0.48
## excited_mssd 0.68 0.64 0.54 0.61
## happy_mssd 0.69 0.63 0.69 0.48
## attentive_mssd 0.61 0.64 0.43 0.64
## irritable_mssd content_mssd relaxed_mssd excited_mssd
## anxious_mssd 0.71 0.63 0.72 0.68
## nervous_mssd 0.67 0.51 0.57 0.64
## upset_mssd 0.60 0.72 0.67 0.54
## sluggish_mssd 0.67 0.41 0.48 0.61
## irritable_mssd 1.00 0.58 0.63 0.65
## content_mssd 0.58 1.00 0.62 0.72
## relaxed_mssd 0.63 0.62 1.00 0.67
## excited_mssd 0.65 0.72 0.67 1.00
## happy_mssd 0.67 0.87 0.68 0.73
## attentive_mssd 0.64 0.52 0.54 0.68
## happy_mssd attentive_mssd
## anxious_mssd 0.69 0.61
## nervous_mssd 0.63 0.64
## upset_mssd 0.69 0.43
## sluggish_mssd 0.48 0.64
## irritable_mssd 0.67 0.64
## content_mssd 0.87 0.52
## relaxed_mssd 0.68 0.54
## excited_mssd 0.73 0.68
## happy_mssd 1.00 0.56
## attentive_mssd 0.56 1.00
##
## n= 85
##
##
## P
## anxious_mssd nervous_mssd upset_mssd sluggish_mssd
## anxious_mssd 0.0000 0.0000 0.0000
## nervous_mssd 0.0000 0.0000 0.0000
## upset_mssd 0.0000 0.0000 0.0011
## sluggish_mssd 0.0000 0.0000 0.0011
## irritable_mssd 0.0000 0.0000 0.0000 0.0000
## content_mssd 0.0000 0.0000 0.0000 0.0001
## relaxed_mssd 0.0000 0.0000 0.0000 0.0000
## excited_mssd 0.0000 0.0000 0.0000 0.0000
## happy_mssd 0.0000 0.0000 0.0000 0.0000
## attentive_mssd 0.0000 0.0000 0.0000 0.0000
## irritable_mssd content_mssd relaxed_mssd excited_mssd
## anxious_mssd 0.0000 0.0000 0.0000 0.0000
## nervous_mssd 0.0000 0.0000 0.0000 0.0000
## upset_mssd 0.0000 0.0000 0.0000 0.0000
## sluggish_mssd 0.0000 0.0001 0.0000 0.0000
## irritable_mssd 0.0000 0.0000 0.0000
## content_mssd 0.0000 0.0000 0.0000
## relaxed_mssd 0.0000 0.0000 0.0000
## excited_mssd 0.0000 0.0000 0.0000
## happy_mssd 0.0000 0.0000 0.0000 0.0000
## attentive_mssd 0.0000 0.0000 0.0000 0.0000
## happy_mssd attentive_mssd
## anxious_mssd 0.0000 0.0000
## nervous_mssd 0.0000 0.0000
## upset_mssd 0.0000 0.0000
## sluggish_mssd 0.0000 0.0000
## irritable_mssd 0.0000 0.0000
## content_mssd 0.0000 0.0000
## relaxed_mssd 0.0000 0.0000
## excited_mssd 0.0000 0.0000
## happy_mssd 0.0000
## attentive_mssd 0.0000all_indiv <- data.frame(indiv_means, indiv_mssd)
all_indiv_cor <- cor(all_indiv, y= NULL, use="complete.obs", method = "pearson")
corrplot(all_indiv_cor, type = "upper", order = "hclust",
tl.col = "black")
corrplot.mixed(all_indiv_cor, lower.col = "black", number.cex = .7)
####PCA for item means using an oblique rotation
means.pca.oblique <- principal(indiv_means, nfactors = 1, rotate = "oblimin")
means.pca.oblique## Principal Components Analysis
## Call: principal(r = indiv_means, nfactors = 1, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 h2 u2 com
## anxious_mean 0.80 0.64 0.36 1
## nervous_mean 0.80 0.64 0.36 1
## upset_mean 0.83 0.69 0.31 1
## sluggish_mean 0.73 0.54 0.46 1
## irritable_mean 0.84 0.70 0.30 1
## content_mean -0.83 0.68 0.32 1
## relaxed_mean -0.80 0.63 0.37 1
## excited_mean -0.51 0.26 0.74 1
## happy_mean -0.83 0.68 0.32 1
## attentive_mean -0.66 0.43 0.57 1
##
## PC1
## SS loadings 5.91
## Proportion Var 0.59
##
## Mean item complexity = 1
## Test of the hypothesis that 1 component is sufficient.
##
## The root mean square of the residuals (RMSR) is 0.2
## with the empirical chi square 295.73 with prob < 5e-43
##
## Fit based upon off diagonal values = 0.89summary(means.pca.oblique)##
## Factor analysis with Call: principal(r = indiv_means, nfactors = 1, rotate = "oblimin")
##
## Test of the hypothesis that 1 factor is sufficient.
## The degrees of freedom for the model is 35 and the objective function was 6.39
## The number of observations was 85 with Chi Square = 505.8 with prob < 8.2e-85
##
## The root mean square of the residuals (RMSA) is 0.2biplot(means.pca.oblique)
means.pca.oblique2 <- principal(indiv_means, nfactors = 2, rotate = "oblimin")
means.pca.oblique2## Principal Components Analysis
## Call: principal(r = indiv_means, nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 h2 u2 com
## anxious_mean 0.93 0.02 0.85 0.149 1.0
## nervous_mean 0.95 0.05 0.88 0.125 1.0
## upset_mean 0.90 -0.06 0.85 0.150 1.0
## sluggish_mean 0.76 -0.09 0.63 0.368 1.0
## irritable_mean 0.91 -0.05 0.86 0.137 1.0
## content_mean -0.18 0.86 0.89 0.112 1.1
## relaxed_mean -0.35 0.63 0.69 0.314 1.5
## excited_mean 0.28 0.97 0.81 0.188 1.2
## happy_mean -0.16 0.88 0.91 0.094 1.1
## attentive_mean -0.04 0.80 0.67 0.331 1.0
##
## TC1 TC2
## SS loadings 4.38 3.65
## Proportion Var 0.44 0.37
## Cumulative Var 0.44 0.80
## Proportion Explained 0.55 0.45
## Cumulative Proportion 0.55 1.00
##
## With component correlations of
## TC1 TC2
## TC1 1.00 -0.39
## TC2 -0.39 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
## with the empirical chi square 30.42 with prob < 0.25
##
## Fit based upon off diagonal values = 0.99summary(means.pca.oblique2)##
## Factor analysis with Call: principal(r = indiv_means, nfactors = 2, rotate = "oblimin")
##
## Test of the hypothesis that 2 factors are sufficient.
## The degrees of freedom for the model is 26 and the objective function was 2.46
## The number of observations was 85 with Chi Square = 193.2 with prob < 1.8e-27
##
## The root mean square of the residuals (RMSA) is 0.06
##
## With component correlations of
## TC1 TC2
## TC1 1.00 -0.39
## TC2 -0.39 1.00biplot(means.pca.oblique2)
means.pca.oblique3 <- principal(indiv_means, nfactors = 3, rotate = "oblimin")
means.pca.oblique3## Principal Components Analysis
## Call: principal(r = indiv_means, nfactors = 3, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 TC3 h2 u2 com
## anxious_mean 0.94 -0.01 -0.15 0.89 0.105 1.1
## nervous_mean 0.96 0.02 -0.13 0.91 0.090 1.0
## upset_mean 0.89 -0.07 0.06 0.85 0.150 1.0
## sluggish_mean 0.69 -0.05 0.56 0.89 0.110 1.9
## irritable_mean 0.88 -0.05 0.21 0.88 0.115 1.1
## content_mean -0.19 0.87 0.06 0.90 0.101 1.1
## relaxed_mean -0.38 0.67 0.34 0.83 0.168 2.1
## excited_mean 0.28 0.96 -0.02 0.81 0.188 1.2
## happy_mean -0.16 0.89 0.04 0.91 0.088 1.1
## attentive_mean 0.01 0.76 -0.44 0.83 0.166 1.6
##
## TC1 TC2 TC3
## SS loadings 4.31 3.67 0.74
## Proportion Var 0.43 0.37 0.07
## Cumulative Var 0.43 0.80 0.87
## Proportion Explained 0.49 0.42 0.08
## Cumulative Proportion 0.49 0.92 1.00
##
## With component correlations of
## TC1 TC2 TC3
## TC1 1.00 -0.37 0.08
## TC2 -0.37 1.00 -0.10
## TC3 0.08 -0.10 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.05
## with the empirical chi square 16.2 with prob < 0.58
##
## Fit based upon off diagonal values = 0.99summary(means.pca.oblique3)##
## Factor analysis with Call: principal(r = indiv_means, nfactors = 3, rotate = "oblimin")
##
## Test of the hypothesis that 3 factors are sufficient.
## The degrees of freedom for the model is 18 and the objective function was 2.3
## The number of observations was 85 with Chi Square = 178.92 with prob < 1.6e-28
##
## The root mean square of the residuals (RMSA) is 0.05
##
## With component correlations of
## TC1 TC2 TC3
## TC1 1.00 -0.37 0.08
## TC2 -0.37 1.00 -0.10
## TC3 0.08 -0.10 1.00means.pca.oblique4 <- principal(indiv_means, nfactors = 4, rotate = "oblimin")
means.pca.oblique4## Principal Components Analysis
## Call: principal(r = indiv_means, nfactors = 4, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC2 TC1 TC3 TC4 h2 u2 com
## anxious_mean 0.13 0.92 0.12 0.09 0.96 0.043 1.1
## nervous_mean 0.05 0.79 0.08 0.26 0.92 0.078 1.2
## upset_mean -0.31 0.25 0.10 0.71 0.95 0.046 1.7
## sluggish_mean 0.12 0.17 0.81 0.24 0.91 0.093 1.3
## irritable_mean -0.16 0.27 0.32 0.58 0.92 0.084 2.2
## content_mean 0.92 -0.07 -0.01 -0.17 0.94 0.058 1.1
## relaxed_mean 0.45 -0.77 0.07 0.28 0.90 0.099 1.9
## excited_mean 0.80 0.06 -0.15 0.28 0.81 0.186 1.3
## happy_mean 0.94 -0.03 -0.04 -0.17 0.96 0.041 1.1
## attentive_mean 0.42 0.04 -0.71 0.27 0.89 0.111 2.0
##
## TC2 TC1 TC3 TC4
## SS loadings 3.18 2.74 1.69 1.55
## Proportion Var 0.32 0.27 0.17 0.15
## Cumulative Var 0.32 0.59 0.76 0.92
## Proportion Explained 0.35 0.30 0.18 0.17
## Cumulative Proportion 0.35 0.65 0.83 1.00
##
## With component correlations of
## TC2 TC1 TC3 TC4
## TC2 1.00 -0.39 -0.42 0.06
## TC1 -0.39 1.00 0.44 0.45
## TC3 -0.42 0.44 1.00 0.26
## TC4 0.06 0.45 0.26 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.03
## with the empirical chi square 7.04 with prob < 0.8
##
## Fit based upon off diagonal values = 1summary(means.pca.oblique4)##
## Factor analysis with Call: principal(r = indiv_means, nfactors = 4, rotate = "oblimin")
##
## Test of the hypothesis that 4 factors are sufficient.
## The degrees of freedom for the model is 11 and the objective function was 1.32
## The number of observations was 85 with Chi Square = 101.58 with prob < 8.7e-17
##
## The root mean square of the residuals (RMSA) is 0.03
##
## With component correlations of
## TC2 TC1 TC3 TC4
## TC2 1.00 -0.39 -0.42 0.06
## TC1 -0.39 1.00 0.44 0.45
## TC3 -0.42 0.44 1.00 0.26
## TC4 0.06 0.45 0.26 1.00biplot(means.pca.oblique4)
PCA for item MSSD
mssdpca.oblique <- principal(indiv_mssd, nfactors = 1, rotate = "oblimin")
mssdpca.oblique## Principal Components Analysis
## Call: principal(r = indiv_mssd, nfactors = 1, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 h2 u2 com
## anxious_mssd 0.88 0.77 0.23 1
## nervous_mssd 0.81 0.66 0.34 1
## upset_mssd 0.75 0.56 0.44 1
## sluggish_mssd 0.71 0.51 0.49 1
## irritable_mssd 0.84 0.71 0.29 1
## content_mssd 0.81 0.66 0.34 1
## relaxed_mssd 0.81 0.66 0.34 1
## excited_mssd 0.86 0.73 0.27 1
## happy_mssd 0.87 0.75 0.25 1
## attentive_mssd 0.77 0.59 0.41 1
##
## PC1
## SS loadings 6.60
## Proportion Var 0.66
##
## Mean item complexity = 1
## Test of the hypothesis that 1 component is sufficient.
##
## The root mean square of the residuals (RMSR) is 0.09
## with the empirical chi square 56.63 with prob < 0.012
##
## Fit based upon off diagonal values = 0.98summary(mssdpca.oblique)##
## Factor analysis with Call: principal(r = indiv_mssd, nfactors = 1, rotate = "oblimin")
##
## Test of the hypothesis that 1 factor is sufficient.
## The degrees of freedom for the model is 35 and the objective function was 1.89
## The number of observations was 85 with Chi Square = 149.28 with prob < 4.6e-16
##
## The root mean square of the residuals (RMSA) is 0.09biplot(mssdpca.oblique)
mssdpca.oblique2 <- principal(indiv_mssd, nfactors = 2, rotate = "oblimin")
mssdpca.oblique2## Principal Components Analysis
## Call: principal(r = indiv_mssd, nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC2 TC1 h2 u2 com
## anxious_mssd 0.64 0.34 0.79 0.21 1.5
## nervous_mssd 0.74 0.16 0.72 0.28 1.1
## upset_mssd -0.09 0.93 0.77 0.23 1.0
## sluggish_mssd 0.98 -0.19 0.77 0.23 1.1
## irritable_mssd 0.66 0.28 0.73 0.27 1.4
## content_mssd -0.02 0.93 0.85 0.15 1.0
## relaxed_mssd 0.28 0.63 0.69 0.31 1.4
## excited_mssd 0.51 0.44 0.73 0.27 2.0
## happy_mssd 0.14 0.83 0.85 0.15 1.1
## attentive_mssd 0.82 0.03 0.71 0.29 1.0
##
## TC2 TC1
## SS loadings 3.86 3.74
## Proportion Var 0.39 0.37
## Cumulative Var 0.39 0.76
## Proportion Explained 0.51 0.49
## Cumulative Proportion 0.51 1.00
##
## With component correlations of
## TC2 TC1
## TC2 1.00 0.61
## TC1 0.61 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
## with the empirical chi square 24.46 with prob < 0.55
##
## Fit based upon off diagonal values = 0.99summary(mssdpca.oblique2)##
## Factor analysis with Call: principal(r = indiv_mssd, nfactors = 2, rotate = "oblimin")
##
## Test of the hypothesis that 2 factors are sufficient.
## The degrees of freedom for the model is 26 and the objective function was 1.23
## The number of observations was 85 with Chi Square = 96.42 with prob < 5e-10
##
## The root mean square of the residuals (RMSA) is 0.06
##
## With component correlations of
## TC2 TC1
## TC2 1.00 0.61
## TC1 0.61 1.00biplot(mssdpca.oblique2)
mssdpca.oblique3 <- principal(indiv_mssd, nfactors = 3, rotate = "oblimin")
mssdpca.oblique3## Principal Components Analysis
## Call: principal(r = indiv_mssd, nfactors = 3, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC3 TC2 h2 u2 com
## anxious_mssd 0.10 0.84 0.08 0.89 0.108 1.0
## nervous_mssd -0.07 0.90 0.12 0.85 0.149 1.1
## upset_mssd 0.72 0.36 -0.28 0.79 0.205 1.8
## sluggish_mssd -0.05 0.28 0.74 0.80 0.198 1.3
## irritable_mssd 0.22 0.50 0.31 0.73 0.265 2.1
## content_mssd 0.98 -0.11 0.09 0.91 0.095 1.0
## relaxed_mssd 0.47 0.49 -0.03 0.71 0.287 2.0
## excited_mssd 0.58 0.03 0.49 0.81 0.187 2.0
## happy_mssd 0.81 0.11 0.10 0.86 0.136 1.1
## attentive_mssd 0.20 0.13 0.69 0.78 0.225 1.3
##
## TC1 TC3 TC2
## SS loadings 3.34 2.90 1.90
## Proportion Var 0.33 0.29 0.19
## Cumulative Var 0.33 0.62 0.81
## Proportion Explained 0.41 0.36 0.23
## Cumulative Proportion 0.41 0.77 1.00
##
## With component correlations of
## TC1 TC3 TC2
## TC1 1.00 0.61 0.37
## TC3 0.61 1.00 0.51
## TC2 0.37 0.51 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.05
## with the empirical chi square 18.07 with prob < 0.45
##
## Fit based upon off diagonal values = 0.99summary(mssdpca.oblique3)##
## Factor analysis with Call: principal(r = indiv_mssd, nfactors = 3, rotate = "oblimin")
##
## Test of the hypothesis that 3 factors are sufficient.
## The degrees of freedom for the model is 18 and the objective function was 1.02
## The number of observations was 85 with Chi Square = 79.71 with prob < 9.6e-10
##
## The root mean square of the residuals (RMSA) is 0.05
##
## With component correlations of
## TC1 TC3 TC2
## TC1 1.00 0.61 0.37
## TC3 0.61 1.00 0.51
## TC2 0.37 0.51 1.00mssdpca.oblique4 <- principal(indiv_mssd, nfactors = 4, rotate = "oblimin")
mssdpca.oblique4## Principal Components Analysis
## Call: principal(r = indiv_mssd, nfactors = 4, rotate = "oblimin")
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC3 TC2 TC4 h2 u2 com
## anxious_mssd 0.03 0.82 0.08 0.15 0.91 0.093 1.1
## nervous_mssd -0.01 1.03 -0.05 -0.07 0.95 0.052 1.0
## upset_mssd 0.32 0.06 0.06 0.68 0.87 0.131 1.5
## sluggish_mssd -0.04 -0.01 0.96 0.02 0.88 0.119 1.0
## irritable_mssd 0.03 0.20 0.60 0.33 0.81 0.194 1.8
## content_mssd 0.96 -0.05 -0.07 0.12 0.92 0.079 1.0
## relaxed_mssd 0.20 0.25 0.22 0.47 0.75 0.251 2.4
## excited_mssd 0.71 0.10 0.28 -0.14 0.83 0.165 1.4
## happy_mssd 0.79 0.16 -0.03 0.12 0.88 0.117 1.1
## attentive_mssd 0.40 0.16 0.54 -0.25 0.78 0.217 2.5
##
## TC1 TC3 TC2 TC4
## SS loadings 2.89 2.37 2.13 1.20
## Proportion Var 0.29 0.24 0.21 0.12
## Cumulative Var 0.29 0.53 0.74 0.86
## Proportion Explained 0.34 0.28 0.25 0.14
## Cumulative Proportion 0.34 0.61 0.86 1.00
##
## With component correlations of
## TC1 TC3 TC2 TC4
## TC1 1.00 0.63 0.51 0.47
## TC3 0.63 1.00 0.65 0.34
## TC2 0.51 0.65 1.00 0.16
## TC4 0.47 0.34 0.16 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.04
## with the empirical chi square 13.95 with prob < 0.24
##
## Fit based upon off diagonal values = 1summary(mssdpca.oblique4)##
## Factor analysis with Call: principal(r = indiv_mssd, nfactors = 4, rotate = "oblimin")
##
## Test of the hypothesis that 4 factors are sufficient.
## The degrees of freedom for the model is 11 and the objective function was 1.17
## The number of observations was 85 with Chi Square = 90.02 with prob < 1.6e-14
##
## The root mean square of the residuals (RMSA) is 0.04
##
## With component correlations of
## TC1 TC3 TC2 TC4
## TC1 1.00 0.63 0.51 0.47
## TC3 0.63 1.00 0.65 0.34
## TC2 0.51 0.65 1.00 0.16
## TC4 0.47 0.34 0.16 1.00biplot(mssdpca.oblique4)
md3$pa_val_mean <- rowMeans(md3[,c("happy", "content", "excited")], na.rm=T)
all4$PA_val_MSSD <- mssd(md3$pa_val_mean, group = md3$id, lag = 1, na.rm=T)
md3$na_val_mean <- rowMeans(md3[,c("upset", "irritable")], na.rm=T)
all4$NA_val_MSSD <- mssd(md3$na_val_mean, group = md3$id, lag = 1, na.rm=T)
md3$attentive_neg <- md3$attentive*-1
md3$energy_mean <- rowMeans(md3[,c("sluggish", "attentive_neg")], na.rm=T)
all4$energy_MSSD <- mssd(md3$energy_mean, group = md3$id, lag = 1, na.rm=T)
md3$relaxed_neg <- md3$relaxed*-1
md3$anxiety_mean <- rowMeans(md3[,c("anxious", "nervous", "relaxed_neg")], na.rm=T)
all4$anxiety_MSSD <- mssd(md3$anxiety_mean, group = md3$id, lag = 1, na.rm=T)
all4$PA_val_Mean <- aggregate(md3[,c("pa_val_mean")], list(md3$id), mean, na.rm=T)
PA_val_Mean <- ddply(md3,.(id), summarize, mean=mean(pa_val_mean, na.rm=T), number=length(id))
all4$PA_val_Mean <- c(PA_val_Mean$mean)
all4$NA_val_Mean <- aggregate(md3[,c("na_val_mean")], list(md3$id), mean, na.rm=T)
NA_val_Mean <- ddply(md3,.(id), summarize, mean=mean(na_val_mean, na.rm=T), number=length(id))
all4$NA_val_Mean <- c(NA_val_Mean$mean)
all4$energy_mean <- aggregate(md3[,c("energy_mean")], list(md3$id), mean, na.rm=T)
energy_mean <- ddply(md3,.(id), summarize, mean=mean(energy_mean, na.rm=T), number=length(id))
all4$energy_mean <- c(energy_mean$mean)
all4$anxiety_mean <- aggregate(md3[,c("anxiety_mean")], list(md3$id), mean, na.rm=T)
anxiety_mean <- ddply(md3,.(id), summarize, mean=mean(anxiety_mean, na.rm=T), number=length(id))
all4$anxiety_mean <- c(anxiety_mean$mean)
all_70 <- subset(all4, nd_resprate >= 0.70)
aa <- all_70[!is.na(all_70["NAD_Mean"]),]Regression analyses with 4 factors and PTQ
m.ptq.NA <- lm(all_indiv$ptq_total ~ scale(all_indiv$NA_val_Mean))
summary(m.ptq.NA)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$NA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.670 -8.395 -1.274 6.299 32.095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.223 16.039 <2e-16 ***
## scale(all_indiv$NA_val_Mean) 3.097 1.230 2.518 0.0137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.27 on 83 degrees of freedom
## Multiple R-squared: 0.07095, Adjusted R-squared: 0.05975
## F-statistic: 6.338 on 1 and 83 DF, p-value: 0.01374m.ptq.PA <- lm(all_indiv$ptq_total ~ scale(all_indiv$PA_val_Mean))
summary(m.ptq.PA)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$PA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.819 -8.091 -2.067 6.598 33.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.6118 1.2648 15.506 <2e-16 ***
## scale(all_indiv$PA_val_Mean) -0.9063 1.2723 -0.712 0.478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.66 on 83 degrees of freedom
## Multiple R-squared: 0.006077, Adjusted R-squared: -0.005898
## F-statistic: 0.5074 on 1 and 83 DF, p-value: 0.4782m.ptq.anx <- lm(all_indiv$ptq_total ~ scale(all_indiv$anxiety_mean))
summary(m.ptq.anx)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$anxiety_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.816 -7.642 -2.040 6.077 30.741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.196 16.392 < 2e-16 ***
## scale(all_indiv$anxiety_mean) 3.867 1.203 3.213 0.00187 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.03 on 83 degrees of freedom
## Multiple R-squared: 0.1106, Adjusted R-squared: 0.0999
## F-statistic: 10.32 on 1 and 83 DF, p-value: 0.001871m.ptq.eng <- lm(all_indiv$ptq_total ~ scale(all_indiv$energy_mean))
summary(m.ptq.eng)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.107 -8.496 -1.441 5.972 31.852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.256 15.609 <2e-16 ***
## scale(all_indiv$energy_mean) 1.608 1.264 1.272 0.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.58 on 83 degrees of freedom
## Multiple R-squared: 0.01912, Adjusted R-squared: 0.007303
## F-statistic: 1.618 on 1 and 83 DF, p-value: 0.2069m.ptq.all <-lm(all_indiv$ptq_total ~ scale(all_indiv$NA_val_Mean) + scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) + scale(all_indiv$energy_mean ))
summary(m.ptq.all)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$NA_val_Mean) +
## scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) +
## scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.710 -7.563 -1.931 6.064 29.761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.6118 1.2104 16.202 <2e-16 ***
## scale(all_indiv$NA_val_Mean) 0.5025 2.1801 0.231 0.8183
## scale(all_indiv$PA_val_Mean) 1.1202 1.5705 0.713 0.4778
## scale(all_indiv$anxiety_mean) 4.4217 2.1005 2.105 0.0384 *
## scale(all_indiv$energy_mean) -0.6697 1.8070 -0.371 0.7119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.16 on 80 degrees of freedom
## Multiple R-squared: 0.1225, Adjusted R-squared: 0.07866
## F-statistic: 2.793 on 4 and 80 DF, p-value: 0.03164Regression analyses with 4 factors and PHQ
m.PHQ.NA <- lm(all_indiv$PHQ_total ~ scale(all_indiv$NA_val_Mean))
summary(m.PHQ.NA)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$NA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5931 -1.8433 -0.5977 1.3405 12.9660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3422 13.578 < 2e-16 ***
## scale(all_indiv$NA_val_Mean) 1.6074 0.3443 4.669 1.15e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.155 on 83 degrees of freedom
## Multiple R-squared: 0.208, Adjusted R-squared: 0.1985
## F-statistic: 21.8 on 1 and 83 DF, p-value: 1.152e-05m.PHQ.PA <- lm(all_indiv$PHQ_total ~ scale(all_indiv$PA_val_Mean))
summary(m.PHQ.PA)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$PA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7400 -2.4312 -0.4594 1.5524 13.6703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3630 12.803 < 2e-16 ***
## scale(all_indiv$PA_val_Mean) -1.1644 0.3651 -3.189 0.00202 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.347 on 83 degrees of freedom
## Multiple R-squared: 0.1091, Adjusted R-squared: 0.09841
## F-statistic: 10.17 on 1 and 83 DF, p-value: 0.002015m.PHQ.anx <- lm(all_indiv$PHQ_total ~ scale(all_indiv$anxiety_mean))
summary(m.PHQ.anx)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$anxiety_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.202 -1.983 -0.475 1.163 11.502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3405 13.650 < 2e-16 ***
## scale(all_indiv$anxiety_mean) 1.6391 0.3425 4.786 7.32e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.139 on 83 degrees of freedom
## Multiple R-squared: 0.2163, Adjusted R-squared: 0.2068
## F-statistic: 22.9 on 1 and 83 DF, p-value: 7.318e-06m.PHQ.eng <- lm(all_indiv$PHQ_total ~ scale(all_indiv$energy_mean))
summary(m.PHQ.eng)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5528 -2.0743 -0.8122 1.3023 12.1641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3454 13.453 < 2e-16 ***
## scale(all_indiv$energy_mean) 1.5493 0.3475 4.459 2.56e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.185 on 83 degrees of freedom
## Multiple R-squared: 0.1932, Adjusted R-squared: 0.1835
## F-statistic: 19.88 on 1 and 83 DF, p-value: 2.563e-05m.PHQ.all <-lm(all_indiv$PHQ_total ~ scale(all_indiv$NA_val_Mean) + scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) + scale(all_indiv$energy_mean ))
summary(m.PHQ.all)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$NA_val_Mean) +
## scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) +
## scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2946 -1.7708 -0.6021 1.2773 11.1861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3361 13.824 <2e-16 ***
## scale(all_indiv$NA_val_Mean) 0.4655 0.6054 0.769 0.444
## scale(all_indiv$PA_val_Mean) -0.1445 0.4361 -0.331 0.741
## scale(all_indiv$anxiety_mean) 0.7822 0.5833 1.341 0.184
## scale(all_indiv$energy_mean) 0.6944 0.5018 1.384 0.170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.099 on 80 degrees of freedom
## Multiple R-squared: 0.2636, Adjusted R-squared: 0.2268
## F-statistic: 7.159 on 4 and 80 DF, p-value: 5.585e-05Regression analyses with 4 factors and SIAS
m.sias.NA <- lm(all_indiv$sias_total ~ scale(all_indiv$NA_val_Mean))
summary(m.sias.NA)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$NA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.145 -8.032 -3.356 7.806 30.192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.275 21.004 < 2e-16 ***
## scale(all_indiv$NA_val_Mean) 4.532 1.282 3.534 0.000673 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.75 on 83 degrees of freedom
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.1203
## F-statistic: 12.49 on 1 and 83 DF, p-value: 0.0006728m.sias.PA <- lm(all_indiv$sias_total ~ scale(all_indiv$PA_val_Mean))
summary(m.sias.PA)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$PA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.069 -8.765 -3.123 8.204 32.095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.313 20.394 < 2e-16 ***
## scale(all_indiv$PA_val_Mean) -3.499 1.321 -2.649 0.00965 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.11 on 83 degrees of freedom
## Multiple R-squared: 0.07797, Adjusted R-squared: 0.06686
## F-statistic: 7.019 on 1 and 83 DF, p-value: 0.009654m.sias.anx <- lm(all_indiv$sias_total ~ scale(all_indiv$anxiety_mean))
summary(m.sias.anx)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$anxiety_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.707 -8.714 -2.436 9.005 34.722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.248 21.450 < 2e-16 ***
## scale(all_indiv$anxiety_mean) 5.114 1.256 4.073 0.000106 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.51 on 83 degrees of freedom
## Multiple R-squared: 0.1666, Adjusted R-squared: 0.1565
## F-statistic: 16.59 on 1 and 83 DF, p-value: 0.0001057m.sias.eng <- lm(all_indiv$sias_total ~ scale(all_indiv$energy_mean))
summary(m.sias.eng)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.810 -9.742 -3.228 8.807 32.802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.321 20.276 <2e-16 ***
## scale(all_indiv$energy_mean) 3.250 1.328 2.447 0.0165 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.18 on 83 degrees of freedom
## Multiple R-squared: 0.06728, Adjusted R-squared: 0.05604
## F-statistic: 5.987 on 1 and 83 DF, p-value: 0.01652m.sias.all <-lm(all_indiv$sias_total ~ scale(all_indiv$NA_val_Mean) + scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) + scale(all_indiv$energy_mean ))
summary(m.sias.all)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$NA_val_Mean) +
## scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) +
## scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.101 -8.231 -2.351 7.838 32.439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.7765 1.2625 21.210 <2e-16 ***
## scale(all_indiv$NA_val_Mean) 1.3789 2.2738 0.606 0.5459
## scale(all_indiv$PA_val_Mean) -1.4756 1.6380 -0.901 0.3704
## scale(all_indiv$anxiety_mean) 3.7226 2.1908 1.699 0.0932 .
## scale(all_indiv$energy_mean) -0.7513 1.8847 -0.399 0.6912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.64 on 80 degrees of freedom
## Multiple R-squared: 0.1784, Adjusted R-squared: 0.1373
## F-statistic: 4.341 on 4 and 80 DF, p-value: 0.003146Regression analyses with 4 factors and OCIR
m.OCIR.NA <- lm(all_indiv$OCIR_total ~ scale(all_indiv$NA_val_Mean))
summary(m.OCIR.NA)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$NA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.100 -7.159 -3.152 5.591 33.455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.365 1.060 11.66 <2e-16 ***
## scale(all_indiv$NA_val_Mean) 1.909 1.067 1.79 0.0771 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.777 on 83 degrees of freedom
## Multiple R-squared: 0.03716, Adjusted R-squared: 0.02556
## F-statistic: 3.203 on 1 and 83 DF, p-value: 0.07714m.OCIR.PA <- lm(all_indiv$OCIR_total ~ scale(all_indiv$PA_val_Mean))
summary(m.OCIR.PA)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$PA_val_Mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.265 -7.237 -2.473 4.673 33.523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0806 11.442 <2e-16 ***
## scale(all_indiv$PA_val_Mean) 0.1363 1.0870 0.125 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.963 on 83 degrees of freedom
## Multiple R-squared: 0.0001894, Adjusted R-squared: -0.01186
## F-statistic: 0.01572 on 1 and 83 DF, p-value: 0.9005m.OCIR.anx <- lm(all_indiv$OCIR_total ~ scale(all_indiv$anxiety_mean))
summary(m.OCIR.anx)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$anxiety_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.509 -6.865 -2.938 5.610 32.581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.365 1.046 11.816 <2e-16 ***
## scale(all_indiv$anxiety_mean) 2.475 1.053 2.351 0.0211 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.647 on 83 degrees of freedom
## Multiple R-squared: 0.06245, Adjusted R-squared: 0.05115
## F-statistic: 5.528 on 1 and 83 DF, p-value: 0.02108m.OCIR.eng <- lm(all_indiv$OCIR_total ~ scale(all_indiv$energy_mean))
summary(m.OCIR.eng)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.979 -7.282 -3.949 4.929 33.224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.365 1.072 11.531 <2e-16 ***
## scale(all_indiv$energy_mean) 1.233 1.079 1.143 0.256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.886 on 83 degrees of freedom
## Multiple R-squared: 0.01551, Adjusted R-squared: 0.003648
## F-statistic: 1.308 on 1 and 83 DF, p-value: 0.2561m.OCIR.all <-lm(all_indiv$OCIR_total ~ scale(all_indiv$NA_val_Mean) + scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) + scale(all_indiv$energy_mean ))
summary(m.OCIR.all)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$NA_val_Mean) +
## scale(all_indiv$PA_val_Mean) + scale(all_indiv$anxiety_mean) +
## scale(all_indiv$energy_mean))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.104 -6.566 -3.060 5.839 30.308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0500 11.776 <2e-16 ***
## scale(all_indiv$NA_val_Mean) -0.2245 1.8911 -0.119 0.906
## scale(all_indiv$PA_val_Mean) 2.0923 1.3623 1.536 0.129
## scale(all_indiv$anxiety_mean) 3.2869 1.8220 1.804 0.075 .
## scale(all_indiv$energy_mean) 0.6912 1.5675 0.441 0.660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.68 on 80 degrees of freedom
## Multiple R-squared: 0.09016, Adjusted R-squared: 0.04467
## F-statistic: 1.982 on 4 and 80 DF, p-value: 0.1052Analyses between 4 factor MSSD and individual difference measures
Regression analyses with 4 factors and PTQ
mssd.ptq.NA <- lm(all_indiv$ptq_total ~ scale(all_indiv$NA_val_MSSD))
summary(mssd.ptq.NA)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$NA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.320 -8.595 -1.718 6.553 29.649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.242 15.793 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 2.379 1.249 1.904 0.0603 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.45 on 83 degrees of freedom
## Multiple R-squared: 0.04186, Adjusted R-squared: 0.03032
## F-statistic: 3.626 on 1 and 83 DF, p-value: 0.06034mssd.ptq.PA <- lm(all_indiv$ptq_total ~ scale(all_indiv$PA_val_MSSD))
summary(mssd.ptq.PA)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$PA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.357 -8.177 -1.785 6.994 30.823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.254 15.641 <2e-16 ***
## scale(all_indiv$PA_val_MSSD) 1.768 1.261 1.401 0.165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.56 on 83 degrees of freedom
## Multiple R-squared: 0.02312, Adjusted R-squared: 0.01135
## F-statistic: 1.964 on 1 and 83 DF, p-value: 0.1648mssd.ptq.anx <- lm(all_indiv$ptq_total ~ scale(all_indiv$anxiety_MSSD))
summary(mssd.ptq.anx)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$anxiety_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.105 -8.821 -1.584 7.039 29.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.256 15.614 <2e-16 ***
## scale(all_indiv$anxiety_MSSD) 1.633 1.263 1.292 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.58 on 83 degrees of freedom
## Multiple R-squared: 0.01973, Adjusted R-squared: 0.007917
## F-statistic: 1.67 on 1 and 83 DF, p-value: 0.1998mssd.ptq.eng <- lm(all_indiv$ptq_total ~ scale(all_indiv$energy_MSSD))
summary(mssd.ptq.eng)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.492 -7.493 -1.964 5.903 30.814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.612 1.265 15.503 <2e-16 ***
## scale(all_indiv$energy_MSSD) 0.875 1.272 0.688 0.494
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.66 on 83 degrees of freedom
## Multiple R-squared: 0.005664, Adjusted R-squared: -0.006316
## F-statistic: 0.4728 on 1 and 83 DF, p-value: 0.4936mssd.ptq.all <-lm(all_indiv$ptq_total ~ scale(all_indiv$NA_val_MSSD) + scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) + scale(all_indiv$energy_MSSD ))
summary(mssd.ptq.all)##
## Call:
## lm(formula = all_indiv$ptq_total ~ scale(all_indiv$NA_val_MSSD) +
## scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) +
## scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.372 -8.432 -1.825 6.358 30.318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.6118 1.2629 15.529 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 2.4849 2.0449 1.215 0.228
## scale(all_indiv$PA_val_MSSD) 0.2447 2.0873 0.117 0.907
## scale(all_indiv$anxiety_MSSD) 0.2592 1.8991 0.137 0.892
## scale(all_indiv$energy_MSSD) -0.8225 1.6778 -0.490 0.625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.64 on 80 degrees of freedom
## Multiple R-squared: 0.04474, Adjusted R-squared: -0.003028
## F-statistic: 0.9366 on 4 and 80 DF, p-value: 0.4472Regression analyses with 4 factors and PHQ
mssd.PHQ.NA <- lm(all_indiv$PHQ_total ~ scale(all_indiv$NA_val_MSSD))
summary(mssd.PHQ.NA)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$NA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0547 -2.4220 -0.6656 1.7338 15.5263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3826 12.15 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 0.3580 0.3849 0.93 0.355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.527 on 83 degrees of freedom
## Multiple R-squared: 0.01032, Adjusted R-squared: -0.001608
## F-statistic: 0.8652 on 1 and 83 DF, p-value: 0.355mssd.PHQ.PA <- lm(all_indiv$PHQ_total ~ scale(all_indiv$PA_val_MSSD))
summary(mssd.PHQ.PA)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$PA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8227 -2.5340 -0.6342 1.5154 15.3956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3843 12.093 <2e-16 ***
## scale(all_indiv$PA_val_MSSD) 0.1412 0.3865 0.365 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.543 on 83 degrees of freedom
## Multiple R-squared: 0.001604, Adjusted R-squared: -0.01042
## F-statistic: 0.1334 on 1 and 83 DF, p-value: 0.7159mssd.PHQ.anx <- lm(all_indiv$PHQ_total ~ scale(all_indiv$anxiety_MSSD))
summary(mssd.PHQ.anx)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$anxiety_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9190 -2.5653 -0.5021 1.5422 15.1064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6471 0.3835 12.119 <2e-16 ***
## scale(all_indiv$anxiety_MSSD) -0.2684 0.3857 -0.696 0.489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.535 on 83 degrees of freedom
## Multiple R-squared: 0.005799, Adjusted R-squared: -0.00618
## F-statistic: 0.4841 on 1 and 83 DF, p-value: 0.4885mssd.PHQ.eng <- lm(all_indiv$PHQ_total ~ scale(all_indiv$energy_MSSD))
summary(mssd.PHQ.eng)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6567 -2.6422 -0.6439 1.3558 15.3506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.647059 0.384573 12.08 <2e-16 ***
## scale(all_indiv$energy_MSSD) -0.007544 0.386855 -0.02 0.984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.546 on 83 degrees of freedom
## Multiple R-squared: 4.582e-06, Adjusted R-squared: -0.01204
## F-statistic: 0.0003803 on 1 and 83 DF, p-value: 0.9845mssd.PHQ.all <-lm(all_indiv$PHQ_total ~ scale(all_indiv$NA_val_MSSD) + scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) + scale(all_indiv$energy_MSSD ))
summary(mssd.PHQ.all)##
## Call:
## lm(formula = all_indiv$PHQ_total ~ scale(all_indiv$NA_val_MSSD) +
## scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) +
## scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2076 -2.3760 -0.3638 1.6462 14.9499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.64706 0.38186 12.170 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 0.95661 0.61827 1.547 0.1258
## scale(all_indiv$PA_val_MSSD) 0.11795 0.63109 0.187 0.8522
## scale(all_indiv$anxiety_MSSD) -0.96160 0.57419 -1.675 0.0979 .
## scale(all_indiv$energy_MSSD) -0.06059 0.50729 -0.119 0.9052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.521 on 80 degrees of freedom
## Multiple R-squared: 0.04972, Adjusted R-squared: 0.002208
## F-statistic: 1.046 on 4 and 80 DF, p-value: 0.3886Regression analyses with 4 factors and SIAS
mssd.sias.NA <- lm(all_indiv$sias_total ~ scale(all_indiv$NA_val_MSSD))
summary(mssd.sias.NA)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$NA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.205 -9.830 -3.154 8.450 27.960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.341 19.972 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 2.462 1.349 1.825 0.0716 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.36 on 83 degrees of freedom
## Multiple R-squared: 0.03859, Adjusted R-squared: 0.027
## F-statistic: 3.331 on 1 and 83 DF, p-value: 0.07157mssd.sias.PA <- lm(all_indiv$sias_total ~ scale(all_indiv$PA_val_MSSD))
summary(mssd.sias.PA)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$PA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.129 -9.999 -3.879 8.382 27.778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.356 19.751 <2e-16 ***
## scale(all_indiv$PA_val_MSSD) 1.632 1.364 1.197 0.235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.5 on 83 degrees of freedom
## Multiple R-squared: 0.01696, Adjusted R-squared: 0.005118
## F-statistic: 1.432 on 1 and 83 DF, p-value: 0.2348mssd.sias.anx <- lm(all_indiv$sias_total ~ scale(all_indiv$anxiety_MSSD))
summary(mssd.sias.anx)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$anxiety_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.219 -9.773 -2.818 8.959 28.200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.776 1.358 19.715 <2e-16 ***
## scale(all_indiv$anxiety_MSSD) 1.452 1.366 1.063 0.291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.52 on 83 degrees of freedom
## Multiple R-squared: 0.01343, Adjusted R-squared: 0.001541
## F-statistic: 1.13 on 1 and 83 DF, p-value: 0.2909mssd.sias.eng <- lm(all_indiv$sias_total ~ scale(all_indiv$energy_MSSD))
summary(mssd.sias.eng)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.249 -10.198 -2.837 8.093 29.048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.7765 1.3664 19.596 <2e-16 ***
## scale(all_indiv$energy_MSSD) 0.4665 1.3745 0.339 0.735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.6 on 83 degrees of freedom
## Multiple R-squared: 0.001386, Adjusted R-squared: -0.01065
## F-statistic: 0.1152 on 1 and 83 DF, p-value: 0.7352mssd.sias.all <-lm(all_indiv$sias_total ~ scale(all_indiv$NA_val_MSSD) + scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) + scale(all_indiv$energy_MSSD ))
summary(mssd.sias.all)##
## Call:
## lm(formula = all_indiv$sias_total ~ scale(all_indiv$NA_val_MSSD) +
## scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) +
## scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.152 -9.532 -2.862 7.562 27.921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.77647 1.35997 19.689 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 3.11606 2.20194 1.415 0.161
## scale(all_indiv$PA_val_MSSD) 0.11494 2.24763 0.051 0.959
## scale(all_indiv$anxiety_MSSD) 0.07286 2.04495 0.036 0.972
## scale(all_indiv$energy_MSSD) -1.40059 1.80669 -0.775 0.440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.54 on 80 degrees of freedom
## Multiple R-squared: 0.04655, Adjusted R-squared: -0.001118
## F-statistic: 0.9765 on 4 and 80 DF, p-value: 0.4251Regression analyses with 4 factors and OCIR
mssd.OCIR.NA <- lm(all_indiv$OCIR_total ~ scale(all_indiv$NA_val_MSSD))
summary(mssd.OCIR.NA)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$NA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.448 -7.268 -2.374 4.651 33.781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0806 11.442 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) -0.1265 1.0870 -0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.963 on 83 degrees of freedom
## Multiple R-squared: 0.0001631, Adjusted R-squared: -0.01188
## F-statistic: 0.01354 on 1 and 83 DF, p-value: 0.9076mssd.OCIR.PA <- lm(all_indiv$OCIR_total ~ scale(all_indiv$PA_val_MSSD))
summary(mssd.OCIR.PA)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$PA_val_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.435 -7.422 -2.410 4.672 33.784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0805 11.443 <2e-16 ***
## scale(all_indiv$PA_val_MSSD) -0.1683 1.0870 -0.155 0.877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.962 on 83 degrees of freedom
## Multiple R-squared: 0.0002887, Adjusted R-squared: -0.01176
## F-statistic: 0.02397 on 1 and 83 DF, p-value: 0.8773mssd.OCIR.anx <- lm(all_indiv$OCIR_total ~ scale(all_indiv$anxiety_MSSD))
summary(mssd.OCIR.anx)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$anxiety_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.189 -7.134 -2.720 4.863 34.997
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0776 11.47 <2e-16 ***
## scale(all_indiv$anxiety_MSSD) -0.7476 1.0840 -0.69 0.492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.935 on 83 degrees of freedom
## Multiple R-squared: 0.005698, Adjusted R-squared: -0.006282
## F-statistic: 0.4756 on 1 and 83 DF, p-value: 0.4923mssd.OCIR.eng <- lm(all_indiv$OCIR_total ~ scale(all_indiv$energy_MSSD))
summary(mssd.OCIR.eng)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.592 -7.073 -2.617 4.560 34.416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0797 11.452 <2e-16 ***
## scale(all_indiv$energy_MSSD) -0.4336 1.0861 -0.399 0.691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.954 on 83 degrees of freedom
## Multiple R-squared: 0.001916, Adjusted R-squared: -0.01011
## F-statistic: 0.1594 on 1 and 83 DF, p-value: 0.6908mssd.OCIR.all <-lm(all_indiv$OCIR_total ~ scale(all_indiv$NA_val_MSSD) + scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) + scale(all_indiv$energy_MSSD ))
summary(mssd.OCIR.all)##
## Call:
## lm(formula = all_indiv$OCIR_total ~ scale(all_indiv$NA_val_MSSD) +
## scale(all_indiv$PA_val_MSSD) + scale(all_indiv$anxiety_MSSD) +
## scale(all_indiv$energy_MSSD))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.349 -6.871 -2.974 5.134 35.406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.3647 1.0956 11.286 <2e-16 ***
## scale(all_indiv$NA_val_MSSD) 0.5654 1.7739 0.319 0.751
## scale(all_indiv$PA_val_MSSD) 0.4353 1.8107 0.240 0.811
## scale(all_indiv$anxiety_MSSD) -1.2605 1.6474 -0.765 0.446
## scale(all_indiv$energy_MSSD) -0.2842 1.4555 -0.195 0.846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.1 on 80 degrees of freedom
## Multiple R-squared: 0.009387, Adjusted R-squared: -0.04014
## F-statistic: 0.1895 on 4 and 80 DF, p-value: 0.9432