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Print an object generated by the function 'path_coeff()'. By default, the results are shown in the R console. The results can also be exported to the directory.

Usage

# S3 method for path_coeff
print(x, export = FALSE, file.name = NULL, digits = 4, ...)

Arguments

x

An object of class path_coeff or group_path.

export

A logical argument. If TRUE, a *.txt file is exported to the working directory

file.name

The name of the file if export = TRUE

digits

The significant digits to be shown.

...

Options used by the tibble package to format the output. See tibble::print() for more details.

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)

# KW as dependent trait and all others as predictors
pcoeff <- path_coeff(data_ge2, resp = KW)
#> Severe multicollinearity. 
#> Condition Number: 7865.84
#> Consider using a correction factor with 'correction' argument.
#> Consider identifying collinear traits with `non_collinear_vars()`
print(pcoeff)
#> ----------------------------------------------------------------------------------------------
#> Correlation matrix between the predictor traits
#> ----------------------------------------------------------------------------------------------
#>            PH       EH       EP       EL       ED       CL       CD      CW
#> PH    1.00000  0.93183  0.63841  0.38020  0.66131  0.32516  0.31539  0.5047
#> EH    0.93183  1.00000  0.86955  0.36265  0.63026  0.39719  0.28051  0.5193
#> EP    0.63841  0.86955  1.00000  0.26342  0.45802  0.39082  0.17504  0.4248
#> EL    0.38020  0.36265  0.26342  1.00000  0.38515  0.25541  0.91187  0.4582
#> ED    0.66131  0.63026  0.45802  0.38515  1.00000  0.69746  0.38971  0.7371
#> CL    0.32516  0.39719  0.39082  0.25541  0.69746  1.00000  0.30036  0.7383
#> CD    0.31539  0.28051  0.17504  0.91187  0.38971  0.30036  1.00000  0.4840
#> CW    0.50474  0.51931  0.42481  0.45817  0.73713  0.73834  0.48403  1.0000
#> NR    0.32861  0.26481  0.14043 -0.01387  0.55253  0.26194 -0.03585  0.1657
#> NKR   0.35305  0.33105  0.25883  0.61715  0.22207 -0.11494  0.59332  0.3403
#> CDED -0.19202 -0.06591  0.08966 -0.01258 -0.01004  0.70800  0.04531  0.2999
#> PERK  0.04081 -0.02135 -0.08709  0.03526 -0.22440 -0.57313 -0.04820 -0.6811
#> TKW   0.56854  0.56236  0.42631  0.44210  0.64199  0.61870  0.44332  0.6735
#> NKE   0.45838  0.38812  0.23305  0.46570  0.50508  0.04894  0.41562  0.3463
#>            NR      NKR     CDED     PERK      TKW      NKE
#> PH    0.32861  0.35305 -0.19202  0.04081  0.56854  0.45838
#> EH    0.26481  0.33105 -0.06591 -0.02135  0.56236  0.38812
#> EP    0.14043  0.25883  0.08966 -0.08709  0.42631  0.23305
#> EL   -0.01387  0.61715 -0.01258  0.03526  0.44210  0.46570
#> ED    0.55253  0.22207 -0.01004 -0.22440  0.64199  0.50508
#> CL    0.26194 -0.11494  0.70800 -0.57313  0.61870  0.04894
#> CD   -0.03585  0.59332  0.04531 -0.04820  0.44332  0.41562
#> CW    0.16566  0.34032  0.29986 -0.68107  0.67346  0.34628
#> NR    1.00000  0.02055 -0.16966  0.12054 -0.10876  0.62609
#> NKR   0.02055  1.00000 -0.37442  0.13554  0.09286  0.70783
#> CDED -0.16966 -0.37442  1.00000 -0.57138  0.23283 -0.42051
#> PERK  0.12054  0.13554 -0.57138  1.00000 -0.27789  0.20528
#> TKW  -0.10876  0.09286  0.23283 -0.27789  1.00000 -0.06516
#> NKE   0.62609  0.70783 -0.42051  0.20528 -0.06516  1.00000
#> ----------------------------------------------------------------------------------------------
#> Vector of correlations between dependent and each predictor
#> ----------------------------------------------------------------------------------------------
#>           PH        EH        EP        EL        ED       CL        CD
#> KW 0.7534439 0.7029469 0.4974193 0.6685601 0.8241426 0.470931 0.6259806
#>           CW        NR       NKR      CDED        PERK       TKW       NKE
#> KW 0.7348622 0.3621447 0.5973701 -0.147029 -0.02683251 0.6730371 0.6810756
#> ----------------------------------------------------------------------------------------------
#> Multicollinearity diagnosis and goodness-of-fit
#> ----------------------------------------------------------------------------------------------
#> Condition number:  7865.84 
#> Determinant:       0 
#> R-square:          0.9889 
#> Residual:          0.1052 
#> Response:          KW 
#> Predictors:        PH EH EP EL ED CL CD CW NR NKR CDED PERK TKW NKE 
#> ----------------------------------------------------------------------------------------------
#> Variance inflation factors
#> ----------------------------------------------------------------------------------------------
#> # A tibble: 14 × 2
#>    VAR       VIF
#>    <chr>   <dbl>
#>  1 PH    123.6  
#>  2 EH    278.3  
#>  3 EP     60.31 
#>  4 EL      7.569
#>  5 ED    351.3  
#>  6 CL    665.1  
#>  7 CD      7.219
#>  8 CW     46.46 
#>  9 NR      5.768
#> 10 NKR     6.451
#> 11 CDED  327.1  
#> 12 PERK   18.24 
#> 13 TKW    19.69 
#> 14 NKE    26.07 
#> ----------------------------------------------------------------------------------------------
#> Eigenvalues and eigenvectors
#> ----------------------------------------------------------------------------------------------
#> # A tibble: 14 × 15
#>    Eigenvalues       PH         EH        EP        EL       ED       CL
#>          <dbl>    <dbl>      <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
#>  1   5.702     -0.3344  -0.3424    -0.2805   -0.2756   -0.3578  -0.2791 
#>  2   2.960      0.1284   0.06995   -0.007025  0.1463   -0.02762 -0.3756 
#>  3   1.819     -0.2329  -0.2507    -0.2220    0.4533   -0.1981  -0.06882
#>  4   1.364      0.2629   0.3518     0.3861   -0.006280 -0.2013  -0.2008 
#>  5   0.7747    -0.03192  0.1044     0.2783   -0.2332   -0.2196  -0.1746 
#>  6   0.6654    -0.1246   0.1278     0.4317    0.1863   -0.2731   0.1965 
#>  7   0.2473     0.5967   0.2245    -0.3831    0.1622   -0.3374  -0.1759 
#>  8   0.2214    -0.2918   0.0004341  0.4050    0.2985    0.1087  -0.1990 
#>  9   0.09185    0.1205   0.07788   -0.05355  -0.4210    0.4094   0.2413 
#> 10   0.08453    0.01898  0.02316   -0.07535   0.4072    0.2085   0.1458 
#> 11   0.05241    0.1106   0.002531  -0.1027    0.3856    0.2729   0.1481 
#> 12   0.01359   -0.01388 -0.03245    0.04255  -0.02922   0.03773 -0.08825
#> 13   0.002298   0.5063  -0.7688     0.3532    0.004010  0.08191 -0.09833
#> 14   0.0007249 -0.08362  0.1219    -0.05200  -0.002194  0.4992  -0.6910 
#> # … with 8 more variables: CD <dbl>, CW <dbl>, NR <dbl>, NKR <dbl>, CDED <dbl>,
#> #   PERK <dbl>, TKW <dbl>, NKE <dbl>
#> ----------------------------------------------------------------------------------------------
#> Variables with the largest weight in the eigenvalue of smallest magnitude
#> ----------------------------------------------------------------------------------------------
#> CL > ED > CDED > EH > CW > PH > NKE > EP > TKW > PERK > NR > EL > NKR > CD 
#> ----------------------------------------------------------------------------------------------
#> Direct (diagonal) and indirect (off-diagonal) effects
#> ----------------------------------------------------------------------------------------------
#>                PH           EH           EP            EL           ED
#> PH    0.134390791 -0.171222875  0.061877581  0.0108837614  0.446623699
#> EH    0.125229130 -0.183749400  0.084280015  0.0103815836  0.425647933
#> EP    0.085796739 -0.159778550  0.096924162  0.0075409552  0.309326800
#> EL    0.051094843 -0.066637406  0.025532125  0.0286267110  0.260110513
#> ED    0.088874623 -0.115809172  0.044393164  0.0110254381  0.675357151
#> CL    0.043699157 -0.072984071  0.037880281  0.0073114554  0.471036554
#> CD    0.042385640 -0.051543877  0.016966075  0.0261037034  0.263195341
#> CW    0.067832246 -0.095423559  0.041174330  0.0131159798  0.497826367
#> NR    0.044161682 -0.048657777  0.013611203 -0.0003971608  0.373158110
#> NKR   0.047446604 -0.060830763  0.025087060  0.0176670988  0.149978415
#> CDED -0.025805444  0.012110905  0.008690601 -0.0003601173 -0.006777457
#> PERK  0.005485082  0.003922896 -0.008441100  0.0010094234 -0.151546980
#> TKW   0.076406333 -0.103332717  0.041319823  0.0126558999  0.433570484
#> NKE   0.061602057 -0.071317081  0.022588399  0.0133314408  0.341109956
#>               CL            CD          CW            NR           NKR
#> PH   -0.29336452 -0.0052666833  0.26553284 -0.0053723322  0.0033603980
#> EH   -0.35834900 -0.0046842398  0.27320033 -0.0043292542  0.0031510292
#> EP   -0.35260233 -0.0029230571  0.22348379 -0.0022958906  0.0024636148
#> EL   -0.23042862 -0.0152271506  0.24103540  0.0002268202  0.0058742020
#> ED   -0.62925279 -0.0065077770  0.38778940 -0.0090332939  0.0021137341
#> CL   -0.90220252 -0.0050157435  0.38842460 -0.0042823466 -0.0010940278
#> CD   -0.27098883 -0.0166989041  0.25463830  0.0005861031  0.0056473477
#> CW   -0.66613034 -0.0080827687  0.52607970 -0.0027083071  0.0032391928
#> NR   -0.23631925  0.0005986531  0.08714906 -0.0163488330  0.0001956084
#> NKR   0.10369965 -0.0099078040  0.17903307 -0.0003359844  0.0095182058
#> CDED -0.63875871 -0.0007565790  0.15775067  0.0027736939 -0.0035638500
#> PERK  0.51708255  0.0008049516 -0.35829571 -0.0019707062  0.0012901065
#> TKW  -0.55819282 -0.0074030400  0.35429286  0.0017781427  0.0008838738
#> NKE  -0.04415581 -0.0069403457  0.18216953 -0.0102357663  0.0067373019
#>              CDED         PERK         TKW         NKE      linear
#> PH   -0.119615809  0.014201880  0.22682326  0.18459191  0.75344390
#> EH   -0.041057967 -0.007428705  0.22435702  0.15629842  0.70294690
#> EP    0.055855319 -0.030303950  0.17008035  0.09385131  0.49741927
#> EL   -0.007836449  0.012269706  0.17637999  0.18753943  0.66856012
#> ED   -0.006251439 -0.078081119  0.25612617  0.20339855  0.82414264
#> CL    0.441041603 -0.199428897  0.24683569  0.01970928  0.47093101
#> CD    0.028223645 -0.016773128  0.17686825  0.16737096  0.62598062
#> CW    0.186795502 -0.236985794  0.26868202  0.13944763  0.73486220
#> NR   -0.105686261  0.041943761 -0.04339179  0.25212770  0.36214470
#> NKR  -0.233244323  0.047163142  0.03704784  0.28504792  0.59737013
#> CDED  0.622940776 -0.198818924  0.09288786 -0.16934243 -0.14702901
#> PERK -0.355936260  0.347962342 -0.11086829  0.08266918 -0.02683251
#> TKW   0.145036730 -0.096696742  0.39895852 -0.02624020  0.67303715
#> NKE  -0.261954336  0.071431363 -0.02599608  0.40270494  0.68107556
#> ----------------------------------------------------------------------------------------------

# Call the algorithm for selecting a set of predictors
# With minimal multicollinearity (no VIF larger than 5)
pcoeff2 <- path_coeff(data_ge2,
                      resp = KW,
                      brutstep = TRUE,
                      maxvif = 5)
#> --------------------------------------------------------------------------
#> The algorithm has selected a set of 8 predictors with largest VIF = 3.346. 
#> Selected predictors: NR PERK EP CDED EL NKR TKW PH 
#> A forward stepwise-based selection procedure will fit 6 models.
#> --------------------------------------------------------------------------
#> Adjusting the model 1 with 7 predictors (16.67% concluded)
#> Adjusting the model 2 with 6 predictors (33.33% concluded)
#> Adjusting the model 3 with 5 predictors (50% concluded)
#> Adjusting the model 4 with 4 predictors (66.67% concluded)
#> Adjusting the model 5 with 3 predictors (83.33% concluded)
#> Adjusting the model 6 with 2 predictors (100% concluded)
#> Done!
#> --------------------------------------------------------------------------
#> Summary of the adjusted models 
#> --------------------------------------------------------------------------
#>    Model  AIC Numpred    CN Determinant    R2 Residual maxVIF
#>  MODEL_1 1127       7 13.67      0.0841 0.933    0.259   2.59
#>  MODEL_2 1125       6 12.26      0.1383 0.933    0.259   2.46
#>  MODEL_3 1126       5 12.05      0.1989 0.932    0.261   2.31
#>  MODEL_4 1251       4  6.66      0.4016 0.846    0.393   1.98
#>  MODEL_5 1308       3  3.05      0.7438 0.774    0.475   1.34
#>  MODEL_6 1329       2  2.23      0.8555 0.738    0.512   1.17
#> --------------------------------------------------------------------------
#> 
print(pcoeff2)
#> # A tibble: 6 × 8
#>   Model     AIC Numpred     CN Determinant     R2 Residual maxVIF
#>   <chr>   <dbl>   <dbl>  <dbl>       <dbl>  <dbl>    <dbl>  <dbl>
#> 1 MODEL_1 1127.       7 13.67      0.08406 0.9331   0.2587  2.592
#> 2 MODEL_2 1125.       6 12.26      0.1383  0.9330   0.2587  2.461
#> 3 MODEL_3 1126.       5 12.05      0.1989  0.9317   0.2612  2.310
#> 4 MODEL_4 1251.       4  6.661     0.4016  0.8456   0.3930  1.977
#> 5 MODEL_5 1308.       3  3.049     0.7438  0.7742   0.4752  1.344
#> 6 MODEL_6 1329.       2  2.227     0.8555  0.7384   0.5115  1.169
#> Go to 'pcoeff2 > s' to select a specific model
# }