library(ggplot2)
library(ggeffects)
library(tidyr)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(haven)
library(naniar)
library(report)
library(effectsize)
##
## Attaching package: 'effectsize'
## The following object is masked from 'package:psych':
##
## phi
Egoproject<-read.csv("Parental Egocentrism Project.csv") #Loading in data file
Sex <- factor(Egoproject$chsex_d1, levels = 1:2, labels = c("Male", "Female")) #Turning sex into a factor
summary(Sex) #Getting summary of sex
## Male Female
## 111 109
Ethnicity <- factor(Egoproject$cethn_d1, levels = 1:6, labels = c("White", "Black", "Hispanic", "Asian-Oriental", "Mixed", "Other")) #Turning ethnicity into a factor
summary(Ethnicity) #Getting summary of ethnicity
## White Black Hispanic Asian-Oriental Mixed
## 154 35 4 6 21
## Other
## 0
Income <- factor(Egoproject$inc_d1, levels = 1:6, labels = c("Less than $20k", "$20k-40k", "$41k-$60k", "$61k-$80k", "$81k-$100k", "Over $100k")) #Turning income into a factor
summary(Income) #Getting summary of income
## Less than $20k $20k-40k $41k-$60k $61k-$80k $81k-$100k
## 12 25 40 41 35
## Over $100k NA's
## 66 1
mean(Egoproject$cage_d1) #Calculating adolescents' mean age
## [1] 13.67034
sd(Egoproject$cage_d1) #Calculating adolescents' age sd
## [1] 1.520727
Ego <- subset(Egoproject,select=c(id,chsex_d1, cage_d1, cethn_d1, inc_d1, t_dep_m1, t_dep_f1, ego_m, ego_f, temo_c1)) #Designating columns to run analyses on
vis_miss(Ego) #Looking at missing data
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
Ego <- na.omit(Ego) #Excluding missing data
r <- corr.test(Ego$t_dep_f1,Ego$ego_f)
print(r,short=F) #Calculating pearson's correlation r for fathers' depression and egocentrism
## Call:corr.test(x = Ego$t_dep_f1, y = Ego$ego_f)
## Correlation matrix
## [1] 0.19
## Sample Size
## [1] 152
## These are the unadjusted probability values.
## The probability values adjusted for multiple tests are in the p.adj object.
## [1] 0.02
##
## Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci
## raw.lower raw.r raw.upper raw.p lower.adj upper.adj
## NA-NA 0.03 0.19 0.34 0.02 0.03 0.34
ggplot(data = Ego, aes(x =t_dep_f1,y=ego_f)) +
geom_point() +
xlab("Paternal Depression") +
ylab("Paternal Egocentrism") +
geom_smooth(method = "lm") #Plotting correlation for fathers' depression and egocentrism using a scatter plot
## `geom_smooth()` using formula 'y ~ x'
Ego$ego_f1c<- scale(Ego$ego_f,center=T,scale=F)
Ego$temo_c1c <- scale(Ego$temo_c1,center=T,scale=F) #Mean centering fathers' egocentrism and adolescents' emotion-focused coping
reg <- lm(temo_c1~ego_f,data=Ego)
summary(reg) #Computing regression for fathers' egocentrism and adolescents' emotion-focused coping
##
## Call:
## lm(formula = temo_c1 ~ ego_f, data = Ego)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.5260 -6.7891 -0.4733 6.9214 26.7372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.8417 1.6812 27.267 <2e-16 ***
## ego_f 1.4211 0.7011 2.027 0.0444 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.33 on 150 degrees of freedom
## Multiple R-squared: 0.02666, Adjusted R-squared: 0.02017
## F-statistic: 4.108 on 1 and 150 DF, p-value: 0.04445
confint(reg) #Finding CI of mean centered regression
## 2.5 % 97.5 %
## (Intercept) 42.51978564 49.163653
## ego_f 0.03572971 2.806414
report(reg)
## We fitted a linear model (estimated using OLS) to predict temo_c1 with ego_f
## (formula: temo_c1 ~ ego_f). The model explains a statistically significant and
## weak proportion of variance (R2 = 0.03, F(1, 150) = 4.11, p = 0.044, adj. R2 =
## 0.02). The model's intercept, corresponding to ego_f = 0, is at 45.84 (95% CI
## [42.52, 49.16], t(150) = 27.27, p < .001). Within this model:
##
## - The effect of ego f is statistically significant and positive (beta = 1.42,
## 95% CI [0.04, 2.81], t(150) = 2.03, p = 0.044; Std. beta = 0.16, 95% CI
## [4.11e-03, 0.32])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald t-distribution approximation.
gg <- ggpredict(reg,terms=c("ego_f"))
plot(gg)+labs(x="Paternal Egocentrism",y= "Adolescent Emotion-Focused Coping",title="") #Plotting regression between fathers' egocentrism and adolescnts' emotion-focused coping
describe(Ego$chsex_d1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 152 1.49 0.5 1 1.49 0 1 2 1 0.03 -2.01 0.04
describe(Ego$t_dep_f1) #Computing M and SD of adolescents' sex and fathers' depression
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 152 54.8 10.65 54 54.52 11.12 38 80 42 0.2 -0.51 0.86
Ego$chsex_d1 <- as.factor(Ego$chsex_d1)
levels(Ego$chsex_d1) <- list('Male'="1",'Female'="2") #Relabel adolescents' sex to ensure it is treated as a factor, not numerical
FemaleC <- (Ego$temo_c1[Ego$chsex_d1 == "Female"])
describe(FemaleC)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 75 48.31 10.93 48 48.26 10.38 27 71 44 0.08 -0.77 1.26
MaleC <- (Ego$temo_c1[Ego$chsex_d1 == "Male"])
describe(MaleC) #Computing M and SD of females and males' emotion-focused coping
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 77 49.27 9.98 47 49.1 8.9 28 74 46 0.22 -0.41 1.14
var.test(FemaleC,MaleC) #Looking at homogeneity of variance for adolescents' sex and emotion-focused coping
##
## F test to compare two variances
##
## data: FemaleC and MaleC
## F = 1.1986, num df = 74, denom df = 76, p-value = 0.4339
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7601059 1.8926373
## sample estimates:
## ratio of variances
## 1.198577
t.test(FemaleC, MaleC,var.equal=T) #Conducting a two sample t-test between adolescents' sex and emotion-focused coping
##
## Two Sample t-test
##
## data: FemaleC and MaleC
## t = -0.56936, df = 150, p-value = 0.57
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.318676 2.386555
## sample estimates:
## mean of x mean of y
## 48.30667 49.27273
model1 <- t.test(FemaleC, MaleC,var.equal=T)
print(model1)
##
## Two Sample t-test
##
## data: FemaleC and MaleC
## t = -0.56936, df = 150, p-value = 0.57
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.318676 2.386555
## sample estimates:
## mean of x mean of y
## 48.30667 49.27273
report(model1)
## Warning: Missing values detected. NAs dropped.
## Effect sizes were labelled following Cohen's (1988) recommendations.
##
## The Two Sample t-test testing the difference between FemaleC and MaleC (mean of
## x = 48.31, mean of y = 49.27) suggests that the effect is negative,
## statistically not significant, and very small (difference = -0.97, 95% CI
## [-4.32, 2.39], t(150) = -0.57, p = 0.570; Cohen's d = -0.09, 95% CI [-0.41,
## 0.23])
cohens_d(model1) #Computing cohen's d for two sample t-test
## Warning: Missing values detected. NAs dropped.
## Cohen's d | 95% CI
## -------------------------
## -0.09 | [-0.41, 0.23]
##
## - Estimated using pooled SD.
dfSumm <- Ego %>%
group_by(chsex_d1) %>%
dplyr:: summarise(
n=n(),
sd = sd(temo_c1, na.rm=T),
temo_c1 = mean(temo_c1,na.rm=T)
)%>%
mutate( se=sd/sqrt(n)) %>%
mutate( ci=se * 1.96)
ggplot(dfSumm, aes(chsex_d1, temo_c1)) +
geom_col(fill=c("blue", "red")) +
geom_errorbar(aes(ymin = temo_c1-ci, ymax = temo_c1+ci),width=0.3)+
xlab("Adolescents' Sex")+
ylab("Adolescents' Emotion-Focused Coping") #Graphing two samples t-test between adolescents' sex and emotion-focused coping
sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] effectsize_0.8.2 report_0.5.5 naniar_0.6.1 haven_2.5.1
## [5] dplyr_1.0.10 Rmisc_1.5.1 plyr_1.8.7 lattice_0.20-45
## [9] psych_2.2.5 tidyr_1.2.1 ggeffects_1.1.4 ggplot2_3.3.6
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 assertthat_0.2.1 digest_0.6.29 utf8_1.2.2
## [5] R6_2.5.1 visdat_0.5.3 evaluate_0.16 highr_0.9
## [9] pillar_1.8.1 rlang_1.0.5 performance_0.10.0 rstudioapi_0.14
## [13] jquerylib_0.1.4 Matrix_1.5-1 rmarkdown_2.16 splines_4.2.2
## [17] labeling_0.4.2 stringr_1.4.1 munsell_0.5.0 compiler_4.2.2
## [21] xfun_0.33 pkgconfig_2.0.3 parameters_0.19.0 mnormt_2.1.0
## [25] mgcv_1.8-41 htmltools_0.5.3 insight_0.18.6 tidyselect_1.1.2
## [29] tibble_3.1.8 fansi_1.0.3 withr_2.5.0 grid_4.2.2
## [33] nlme_3.1-160 jsonlite_1.8.0 gtable_0.3.1 lifecycle_1.0.2
## [37] DBI_1.1.3 magrittr_2.0.3 bayestestR_0.13.0 scales_1.2.1
## [41] datawizard_0.6.3 cli_3.4.0 stringi_1.7.8 cachem_1.0.6
## [45] farver_2.1.1 bslib_0.4.0 ellipsis_0.3.2 generics_0.1.3
## [49] vctrs_0.4.1 tools_4.2.2 forcats_0.5.2 glue_1.6.2
## [53] purrr_0.3.4 hms_1.1.2 parallel_4.2.2 fastmap_1.1.0
## [57] yaml_2.3.5 colorspace_2.0-3 knitr_1.40 sass_0.4.2
citation("ggplot2")
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## H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
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##
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