# Regression analysis

4 posts / 0 new Offline
Joined: 03/18/2022 - 12:47
Regression analysis
AttachmentSize Regression.csv1.52 KB

Dear Community,

I’m trying to fit a regression model by regressing the true effect size y_strength on the true effect size y_MEP (see attached dataset). I use the following code to remove NA and unnecessary columns, as well as to specify the SEM in OpenMx:

library(metaSEM)

";", escape_double = FALSE, col_types = cols(Type_training = col_factor(levels = c("SP",
"MP", "IM"))), na = "NA", trim_ws = TRUE)
View(regression_model)
## Regression between both ES: MEP --> strength
# 1. remove the missing values (these models cannot work with MV) and exclude
# the 3 first columns
RM_new <- regression_model[-8,4:9]

## A: asymmetric paths for regression coefficients

A <- matrix(c(0, "0.1*beta1_2", 0, 0,
0, 0, 0, 0,
1, 0, 0, 0,
0, 1, 0, 0),
ncol=4, nrow=4, byrow=TRUE)

dimnames(A) <- list(c("f_strength","f_MEP",
"y_strength","y_MEP"),
c("f_strength","f_MEP",
"y_strength","y_MEP"))
A
## Convert it into OpenMx matrix

A <- as.mxMatrix(A)

# S: symmetric covariances and variances

S <- mxMatrix(type="Symm", nrow=4, ncol=4, byrow=TRUE,
free=c(TRUE,
FALSE,TRUE,
FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE,FALSE),
values=c(0.1,
0,0.1,
0,0,0,
0,0,0,0),
labels=c("tau2_1_1",
NA,"tau2_2_2",
NA,NA,"data.v_strength",
NA,NA,"data.Cov_strength_MEP","data.v_MEP"),
name = "S")

## F: select observed variables

F <- matrix(c(0, 0, 1, 0,
0, 0, 0, 1), nrow = 2, ncol = 4, byrow = TRUE)
dimnames(F) <- list(c("y_strength","y_MEP"),
c("f_strength","f_MEP","y_strength",
"y_MEP"))
F
F <-as.mxMatrix(F)
## M: intercepts or means (only intercepts of the latent are estimated
## the intercepts of the observed variables are set to 0)

M <- matrix(c("0*beta1_0","0*beta2_0",0,0), nrow=1, ncol=4, byrow = TRUE)
dimnames(M)[] <- c("f_strength","f_MEP",
"y_strength","y_MEP")
M
M <- as.mxMatrix(M)

## Formula for R2
R2 <- mxAlgebra(beta1_2^2*tau2_2_2/(beta1_2^2*tau2_2_2 + tau2_1_1),
name="R2")

## Build the model
reg <- mxModel("Regression",
mxData(observed=RM_new, type="raw"),
A, S, F, M, R2, mxCI("R2"),
mxExpectationRAM(A="A", S="S",
F="F", M="M",
dimnames = c("f_strength","f_MEP",
"y_strength","y_MEP")),
mxFitFunctionML())

When I run the analysis, obtained the following error:

Error in value[rows[[i]], cols[[i]]] <- startValue :
incorrect number of subscripto n matrix

If a traceback the error, it gives:

> traceback()
5: FUN(X[[i]], ...)
4: lapply(flatModel@matrices, populateDefVarMatrix, flatModel)
3: populateDefInitialValues(flatModel)
2: runHelper(model, frontendStart, intervals, silent, suppressWarnings,
unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer,
beginMessage)
1: mxRun(reg, intervals = TRUE, silent = TRUE)

I’m trying to figure out what’s going on but without success. Could you help me with this error please?

Thanks Offline
Joined: 03/18/2022 - 12:47
Regression analysis

Please find attached the R code. Thank you!
Juanfran

File attachments: Regression_model.R Offline
Joined: 10/08/2009 - 22:37
Hi Juanfran,

Hi Juanfran,

The issue is caused by the readr package, which returns a tibble. You may solve it using as.data.frame(RM_new).

Attached is the complete R code and output. I have also included an alternative approach, which may simplify the code.

Mike

File attachments: Regression_model.pdf Offline
Joined: 03/18/2022 - 12:47
Regression analysis

Thank you very much Mike!