Description Usage Arguments Details Value Author(s) References Examples
path_coeff()
computes a path analysis using a data frame as input data.
path_coeff_mat()
computes a path analysis using correlation matrices as
input data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  path_coeff(
.data,
resp,
pred = everything(),
by = NULL,
exclude = FALSE,
correction = NULL,
knumber = 50,
brutstep = FALSE,
maxvif = 10,
missingval = "pairwise.complete.obs",
plot_res = FALSE,
verbose = TRUE,
...
)
path_coeff_mat(cor_mat, resp, correction = NULL, knumber = 50, verbose = TRUE)

.data 
The data. Must be a data frame or a grouped data passed from

resp 
The dependent variable. 
pred 
The predictor variables, set to 
by 
One variable (factor) to compute the function by. It is a shortcut
to 
exclude 
Logical argument, set to false. If 
correction 
Set to 
knumber 
When 
brutstep 
Logical argument, set to 
maxvif 
The maximum value for the Variance Inflation Factor (cut point) that will be accepted. See the Details section for more information. 
missingval 
How to deal with missing values. For more information,
please see 
plot_res 
If 
verbose 
If 
... 
Additional arguments passed on to 
cor_mat 
Matrix of correlations containing both dependent and independent traits. 
In path_coeff()
, when brutstep = TRUE
, an algorithm to
select a set of predictors with minimal multicollinearity and high
explanatory power is implemented. first, the algorithm will select a set of
predictors with minimal multicollinearity. The selection is based on the
variance inflation factor (VIF). An iterative process is performed until
the maximum VIF observed is less than maxvif
. The variables selected
in this iterative process are then used in a series of stepwisebased
regressions. The first model is fitted and p1 predictor variables are
retained (p is the number of variables selected in the iterative process.
The second model adjusts a regression considering p2 selected variables,
and so on until the last model, which considers only two variables. Three
objects are created. Summary
, with the process summary,
Models
, containing the aforementioned values for all the adjusted
models; and Selectedpred
, a vector with the name of the selected
variables in the iterative process.
An object of class path_coeff, group_path, or brute_path
with
the following items:
Corr.x A correlation matrix between the predictor variables.
Corr.y A vector of correlations between each predictor variable with the dependent variable.
Coefficients The path coefficients. Direct effects are the diagonal elements, and the indirect effects those in the offdiagonal elements (lines).
Eigen Eigenvectors and eigenvalues of the Corr.x.
VIF The Variance Inflation Factors.
plot A ggplot2based graphic showing the direct effects in 21 different k values.
Predictors The predictor variables used in the model.
CN The Condition Number, i.e., the ratio between the highest and lowest eigenvalue.
Det The matrix determinant of the Corr.x.
.
R2 The coefficient of determination of the model.
Residual The residual effect of the model.
Response The response variable.
weightvar The order of the predictor variables with the highest weight (highest eigenvector) in the lowest eigenvalue.
If .data
is a grouped data passed from dplyr::group_by()
then the results will be returned into a listcolumn of data frames,
containing:
Tiago Olivoto tiagoolivoto@gmail.com
Olivoto, T., V.Q. Souza, M. Nardino, I.R. Carvalho, M. Ferrari, A.J. Pelegrin, V.J. Szareski, and D. Schmidt. 2017. Multicollinearity in path analysis: a simple method to reduce its effects. Agron. J. 109:131142. doi: 10.2134/agronj2016.04.0196
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  library(metan)
# Using KW as the response variable and all other ones as predictors
pcoeff < path_coeff(data_ge2, resp = KW)
# The same as above, but using the correlation matrix
cor_mat < cor(data_ge2 %>% select_numeric_cols())
pcoeff < path_coeff_mat(cor_mat, resp = KW)
# Declaring the predictors
# Create a residual plot with 'plot_res = TRUE'
pcoeff2 < path_coeff(data_ge2,
resp = KW,
pred = c(PH, EH, NKE, TKW),
plot_res = TRUE)
# Selecting variables to be excluded from the analysis
pcoeff3 < path_coeff(data_ge2,
resp = KW,
pred = c(NKR, PERK, KW, NKE),
exclude = TRUE)
# Selecting a set of predictors with minimal multicollinearity
# Maximum variance Inflation factor of 5
pcoeff4 < path_coeff(data_ge2,
resp = KW,
brutstep = TRUE,
maxvif = 5)
# When one analysis should be carried out for each environment
# Using the forwardpipe operator %>%
pcoeff5 < path_coeff(data_ge2, resp = KW, by = ENV)

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