Warning: package 'psych' was built under R version 4.2.3
library (corrr)
Warning: package 'corrr' was built under R version 4.2.3
library(ggstatsplot)
Warning: package 'ggstatsplot' was built under R version 4.2.3
You can cite this package as:
Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
library("RColorBrewer")library(correlation)
Warning: package 'correlation' was built under R version 4.2.3
library(see)
Warning: package 'see' was built under R version 4.2.3
attach(bodyfat)
Resolución de ejercicio 14.1
#Regresión cor.test(Fat,Weight)
Pearson's product-moment correlation
data: Fat and Weight
t = 12.249, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.5288644 0.6842076
sample estimates:
cor
0.612414
cor.test(Fat,Height)
Pearson's product-moment correlation
data: Fat and Height
t = -1.4207, df = 250, p-value = 0.1566
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.21073764 0.03445855
sample estimates:
cor
-0.08949538
cor.test(Fat,Age)
Pearson's product-moment correlation
data: Fat and Age
t = 4.8175, df = 250, p-value = 2.522e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1741581 0.4006030
sample estimates:
cor
0.2914584
cor.test(Fat,Abdomen)
Pearson's product-moment correlation
data: Fat and Abdomen
t = 22.112, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.7669520 0.8514218
sample estimates:
cor
0.8134323
cor.test(Fat,Wrist)
Pearson's product-moment correlation
data: Fat and Wrist
t = 5.8419, df = 250, p-value = 1.6e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.2329799 0.4508395
sample estimates:
cor
0.3465749
cor.test(Fat,Thigh)
Pearson's product-moment correlation
data: Fat and Thigh
t = 10.676, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4684275 0.6389926
sample estimates:
cor
0.5596075
Rows: 210 Columns: 74
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): dma
dbl (73): geoCode, 2010+cancer, 2010+cardiovascular, 2010+stroke, 2010+depre...
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.
ggcorrmat(data = RegionalInterestByConditionOverTime[1,3:9], ## Data Frametype ="np", ## which correlation coefficient is to be computedmatrix.type ="lower", ## Estructura de la gráficatitle ="Grafica de correlación", ## custom titlesubtitle ="Biostadística DCB"## custom subtitle)
Warning: 2010+cancer and 2010+cardiovascular have less than 3 complete
observations. Returning NA.
# Cargar bibliotecalibrary("psych")#Mejor funcion para bases grandescorrelate(RegionalInterestByConditionOverTime)
Non-numeric variables removed from input: `dma`
Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'
The following objects are masked from bodyfat (pos = 4):
Abdomen, Age, Ankle, Biceps, Chest, Density, Fat, Forearm, Height,
Hip, Knee, Neck, Thigh, Weight, Wrist
cor.test(Density, Fat)
Pearson's product-moment correlation
data: Density and Fat
t = -100.22, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.9904570 -0.9843641
sample estimates:
cor
-0.9877824
plot(Density, Fat, main ="Densidad mineral vs Porcentaje de grasa ",xlab="Densidad mineral", ylab="Porcentaje de grasa", col="cyan4", pch=20)# Segunda capatext(x=1.06, y=40, label="r=--0.9877824; p<0.001")# Tercera capaabline(lm(Fat~Density))
cor.test(Fat, Height)
Pearson's product-moment correlation
data: Fat and Height
t = -1.4207, df = 250, p-value = 0.1566
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.21073764 0.03445855
sample estimates:
cor
-0.08949538
plot(Fat, Height, main ="Fat vs altura ",xlab="Porcentaje de grasa", ylab="Altura", col="cyan4", pch=20)# Segunda capatext(x=1.06, y=40, label="r=-0.08949538 ; p=0.1566")# Tercera capaabline(lm(Fat~Height))
cor.test(Fat, Abdomen)
Pearson's product-moment correlation
data: Fat and Abdomen
t = 22.112, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.7669520 0.8514218
sample estimates:
cor
0.8134323
plot(Fat, Abdomen, main ="Fat vs abdomen ",xlab="Porcentaje de grasa", ylab="Medida de abdomen", col="cyan4", pch=20)# Segunda capatext(x=1.06, y=40, label="r=0.8134323 ; p=0.0001")# Tercera capaabline(lm(Fat~Abdomen))
cor.test(Fat, Chest)
Pearson's product-moment correlation
data: Fat and Chest
t = 15.613, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.6341034 0.7601899
sample estimates:
cor
0.7026203
plot(Fat, Abdomen, main ="Fat vs Chest ",xlab="Porcentaje de grasa", ylab="Pecho", col="cyan4", pch=20)# Segunda capatext(x=1.06, y=40, label="r=0.7026203 ; p=0.0001")# Tercera capaabline(lm(Fat~Chest))
cor.test(Fat, Biceps)
Pearson's product-moment correlation
data: Fat and Biceps
t = 8.966, df = 250, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3936960 0.5814045
sample estimates:
cor
0.4932711
plot(Fat, Biceps, main ="Fat vs Chest ",xlab="Porcentaje de grasa", ylab="Pecho", col="cyan4", pch=20)# Segunda capatext(x=1.06, y=40, label="r=0.4932711 ; p=0.0001")# Tercera capaabline(lm(Fat~Biceps))
