library(lavaan)
## Warning: package 'lavaan' was built under R version 4.1.3
## This is lavaan 0.6-11
## lavaan is FREE software! Please report any bugs.
library(semPlot)
## Warning: package 'semPlot' was built under R version 4.1.3
library(OpenMx)
## Warning: package 'OpenMx' was built under R version 4.1.3
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(knitr)
## Warning: package 'knitr' was built under R version 4.1.3
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
library(GGally)
## Warning: package 'GGally' was built under R version 4.1.3
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
library(readr)
library(Hmisc)
## Warning: package 'Hmisc' was built under R version 4.1.3
## Loading required package: lattice
## Warning: package 'lattice' was built under R version 4.1.3
## Loading required package: survival
## Warning: package 'survival' was built under R version 4.1.3
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, units
corstarsl <- function(x){
x <- as.matrix(x)
R <- rcorr(x)$r
p <- rcorr(x)$P
mystars <- ifelse(p > 0.05, "ns", ifelse(p > 0.01 & p <= 0.05, "*", "**"))
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
stars
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- paste(diag(R), " ", sep="")
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew)
Rnew <- cbind(Rnew[1:length(Rnew)-1])
return(Rnew)
}
exemplo_cursomeg <- read_table("C:/CursoGenes/exemplo_cursomeg.dat",
col_names = FALSE)
##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## X2 = col_double(),
## X3 = col_double(),
## X4 = col_double(),
## X5 = col_double(),
## X6 = col_logical()
## )
exemplo_cursomeg <- exemplo_cursomeg %>% select(1:5)
colnames(exemplo_cursomeg) <-c("A","B","C","D","E")
corstarsl(exemplo_cursomeg) %>% kable("pandoc")
| A |
|
|
|
|
| B |
0.90** |
|
|
|
| C |
0.87** |
0.97** |
|
|
| D |
0.74** |
0.93** |
0.93** |
|
| E |
-0.08ns |
-0.29ns |
-0.31ns |
-0.36ns |
model <-'A ~ B+C+D+E'
fit <- cfa(model, data = exemplo_cursomeg)
summary(fit, fit.measures = TRUE, standardized=T,rsquare=T)
## lavaan 0.6-11 ended normally after 1 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Number of observations 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 45.375
## Degrees of freedom 4
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -57.886
## Loglikelihood unrestricted model (H1) -57.886
##
## Akaike (AIC) 125.771
## Bayesian (BIC) 130.750
## Sample-size adjusted Bayesian (BIC) 115.336
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## A ~
## B 2.462 0.700 3.515 0.000 2.462 1.101
## C 0.809 0.515 1.570 0.117 0.809 0.492
## D -8.228 2.512 -3.276 0.001 -8.228 -0.689
## E 1.512 0.810 1.867 0.062 1.512 0.145
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .A 19.119 6.046 3.162 0.002 19.119 0.103
##
## R-Square:
## Estimate
## A 0.897
semPaths(fit,"std",layout = 'spring', edge.label.cex=.9, curvePivot = TRUE)
ggcorr(exemplo_cursomeg, nbreaks = 6, label = T, low = "red3", high = "green4",
label_round = 2, name = "Correlation Scale", label_alpha = T, hjust = 0.75) +
ggtitle(label = "Correlation Plot") +
theme(plot.title = element_text(hjust = 0.6))
