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michaelhallquist / MplusAutomation

The MplusAutomation package leverages the flexibility of the R language to automate latent variable model estimation and interpretation using Mplus, a powerful latent variable modeling program developed by Muthen and Muthen (www.statmodel.com). Specifically, MplusAutomation provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.
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The error in R package #113

Closed mroshanaei closed 4 years ago

mroshanaei commented 4 years ago

Hi,

I'm trying the following code in R package. It's working in Mplus. But I will get the following message in R. Any idea why I'm getting the following error.

Thanks, Mahnaz

Error in R package:

Wrote model to: montecarlo.inp

Running model: montecarlo.inp System command: cd "." && "/Applications/Mplus/mplus" "montecarlo.inp" Reading model: montecarlo.out Warning message: In runModels(target = modelout, Mplus_command = Mplus_command, logFile = NULL) : Mplus returned error code: 1, for model: montecarlo.inp

MPLUS code: MONTECARLO: NAMES ARE Stressed lag_Stressed NgDisRatio PsDisRatio; ! a)

NOBSERVATIONS = 67200 ; ! b) NCSIZES = 1; ! let sub represent the range of the number of subjects NCSIZE = 1 ; CSIZES = 800 (84) ; ! c) SEED = 2020 ; ! d) NREPS = 100; ! e) WITHIN = lag_Stressed NgDisRatio PsDisRatio;! g) !BETWEEN = ; ! h) !PATMISS = Stressed(0.1) lag_Stressed(0.2) NgDisRatio(0.5) PsDisRatio(.2); !PATPROBS = 1; ! f) MISSING = Stressed; ANALYSIS: TYPE = TWOLEVEL RANDOM; ! i) !ALGORITHM=INTEGRATION;

!INTEGRATION = MONTECARLO; MODEL POPULATION: ! j) %WITHIN% ! k) slope | Stressed ON NgDisRatio; ! define the random slope of NgDisRatio main effect Stressed ON lag_Stressed0.54957 ; ! lag.Stressed main effect Stressed ON PsDisRatio-0.18219 ; ! PsDisRatio main effect [lag_Stressed 3.131349] ; lag_Stressed 1.432338; !(lag.stressed Mean and Var) [PsDisRatio 0.3961094]; PsDisRatio 0.2019174; !(PsDisRatio Mean and Var) [NgDisRatio 0.3064143]; NgDisRatio 0.1930179; !(NgDisRatio Mean and Var) Stressed0.87; ! residual variance of Stressed %BETWEEN% ! l) [Stressed1.312]; Stressed 0.046 ; !intercept estimate 1.312 and variance 0.046) [slope0.38058]; slope0.01574; ! NgDisRatio slope (0.38) and variance (0.015) MODEL MISSING: [Stressed@-2]; Stressed ON lag_Stressed.4 NgDisRatio.5 PsDisRatio.5; MODEL: ! m) %WITHIN% ! k) slope | Stressed ON NgDisRatio; ! define the random slope of NgDisRatio main effect Stressed ON lag_Stressed0.54957 ; ! lag.Stressed main effect Stressed ON PsDisRatio-0.18219 ; ! PsDisRatio main effect [lag_Stressed 3.131349] ; lag_Stressed 1.432338; !(lag.stressed Mean and Var) [PsDisRatio 0.3961094]; PsDisRatio 0.2019174; !(PsDisRatio Mean and Var) [NgDisRatio 0.3064143]; NgDisRatio 0.1930179; !(NgDisRatio Mean and Var) Stressed0.87; ! residual variance of Stressed %BETWEEN% ! l) [Stressed1.312]; Stressed 0.046 ; !intercept estimate 1.312 and variance 0.046) [slope0.38058]; slope*0.01574; ! NgDisRatio slope (0.38) and variance (0.015) OUTPUT: TECH9;

michaelhallquist commented 4 years ago

Please upload montecarlo.out as a text file so that we can see what the error is in your syntax.

mroshanaei commented 4 years ago

Hi,

I tried this two methods for saving .out as text, here is the error and I think because there is not any output in .out. right?

out <- capture.output(summary(montecarlo)) Error in summary.mplusObject(montecarlo) : !is.null(object$results) is not TRUE

write.table(montecarlo, "mydata.txt") Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : arguments imply differing number of rows: 1, 0

cjvanlissa commented 4 years ago

The .out file is already plain text, so you can just upload that!

mroshanaei commented 4 years ago

Hi, Here is the .out file from MPLUS:

Mplus VERSION 8.3 (Mac) MUTHEN & MUTHEN 11/02/2019 5:15 PM

INPUT INSTRUCTIONS

MONTECARLO: NAMES ARE Stressed lag_Stressed NgDisRatio PsDisRatio; ! a)

NOBSERVATIONS = 67200 ; ! b) NCSIZES = 1; ! let sub represent the range of the number of subjects NCSIZE = 1 ; CSIZES = 800 (84) ; ! c) SEED = 2020 ; ! d) NREPS = 100; ! e) WITHIN = lag_Stressed NgDisRatio PsDisRatio;! g) !BETWEEN = ; ! h) !PATMISS = Stressed(0.1) lag_Stressed(0.2) NgDisRatio(0.5) PsDisRatio(.2); !PATPROBS = 1; ! f) MISSING = Stressed; ANALYSIS: TYPE = TWOLEVEL RANDOM; ! i) !ALGORITHM=INTEGRATION;

!INTEGRATION = MONTECARLO; MODEL POPULATION: ! j) %WITHIN% ! k) slope | Stressed ON NgDisRatio; ! define the random slope of NgDisRatio main effect Stressed ON lag_Stressed0.54957 ; ! lag.Stressed main effect Stressed ON PsDisRatio-0.18219 ; ! PsDisRatio main effect [lag_Stressed 3.131349] ; lag_Stressed 1.432338; !(lag.stressed Mean and Var) [PsDisRatio 0.3961094]; PsDisRatio 0.2019174; !(PsDisRatio Mean and Var) [NgDisRatio 0.3064143]; NgDisRatio 0.1930179; !(NgDisRatio Mean and Var) Stressed0.87; ! residual variance of Stressed %BETWEEN% ! l) [Stressed1.312]; Stressed 0.046 ; !intercept estimate 1.312 and variance 0.046) [slope0.38058]; slope0.01574; ! NgDisRatio slope (0.38) and variance (0.015) MODEL MISSING: [Stressed@-2]; Stressed ON lag_Stressed.4 NgDisRatio.5 PsDisRatio.5; MODEL: ! m) %WITHIN% ! k) slope | Stressed ON NgDisRatio; ! define the random slope of NgDisRatio main effect Stressed ON lag_Stressed0.54957 ; ! lag.Stressed main effect Stressed ON PsDisRatio-0.18219 ; ! PsDisRatio main effect [lag_Stressed 3.131349] ; lag_Stressed 1.432338; !(lag.stressed Mean and Var) [PsDisRatio 0.3961094]; PsDisRatio 0.2019174; !(PsDisRatio Mean and Var) [NgDisRatio 0.3064143]; NgDisRatio 0.1930179; !(NgDisRatio Mean and Var) Stressed0.87; ! residual variance of Stressed %BETWEEN% ! l) [Stressed1.312]; Stressed 0.046 ; !intercept estimate 1.312 and variance 0.046) [slope0.38058]; slope*0.01574; ! NgDisRatio slope (0.38) and variance (0.015) OUTPUT: TECH9;

INPUT READING TERMINATED NORMALLY

SUMMARY OF ANALYSIS

Number of groups 1 Number of observations 67200

Number of replications Requested 100 Completed 100 Value of seed 2020

Number of dependent variables 1 Number of independent variables 3 Number of continuous latent variables 1

Observed dependent variables

Continuous STRESSED

Observed independent variables LAG_STRE NGDISRAT PSDISRAT

Continuous latent variables SLOPE

Variables with special functions

Within variables LAG_STRE NGDISRAT PSDISRAT

Estimator MLR Information matrix OBSERVED Maximum number of iterations 100 Convergence criterion 0.100D-05 Maximum number of EM iterations 500 Convergence criteria for the EM algorithm Loglikelihood change 0.100D-02 Relative loglikelihood change 0.100D-05 Derivative 0.100D-03 Minimum variance 0.100D-03 Maximum number of steepest descent iterations 20 Maximum number of iterations for H1 2000 Convergence criterion for H1 0.100D-03 Optimization algorithm EMA

SUMMARY OF DATA FOR THE FIRST REPLICATION

 Number of missing data patterns             2
 Cluster information

   Size (s)    Number of clusters of Size s

     84           800

 Average cluster size       84.000

 Estimated Intraclass Correlations for the Y Variables

            Intraclass              Intraclass              Intraclass
 Variable  Correlation   Variable  Correlation   Variable  Correlation

 STRESSED     0.032      LAG_STRE     0.000      NGDISRAT     0.000
 PSDISRAT     0.000

SUMMARY OF MISSING DATA PATTERNS FOR THE FIRST REPLICATION

 MISSING DATA PATTERNS (x = not missing)

       1  2

STRESSED x LAG_STRE x x NGDISRAT x x PSDISRAT x x

 MISSING DATA PATTERN FREQUENCIES

Pattern   Frequency     Pattern   Frequency
      1       39940           2       27260

COVARIANCE COVERAGE OF DATA FOR THE FIRST REPLICATION

Minimum covariance coverage value 0.100

 PROPORTION OF DATA PRESENT

       Covariance Coverage
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.594 LAG_STRE 0.594 1.000 NGDISRAT 0.594 1.000 1.000 PSDISRAT 0.594 1.000 1.000 1.000

SAMPLE STATISTICS FOR THE FIRST REPLICATION

NOTE: The sample statistics for within and between refer to the maximum-likelihood estimated within and between covariance matrices, respectively.

 ESTIMATED SAMPLE STATISTICS FOR WITHIN

       Means
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
            0.000         3.122         0.306         0.392

       Covariances
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 1.350 LAG_STRE 0.795 1.430 NGDISRAT 0.072 0.001 0.191 PSDISRAT -0.038 0.000 0.001 0.202

       Correlations
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 1.000 LAG_STRE 0.572 1.000 NGDISRAT 0.141 0.002 1.000 PSDISRAT -0.073 0.001 0.007 1.000

 ESTIMATED SAMPLE STATISTICS FOR BETWEEN

       Means
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
            3.065         0.000         0.000         0.000

       Covariances
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.045 LAG_STRE 0.000 0.000 NGDISRAT 0.000 0.000 0.000 PSDISRAT 0.000 0.000 0.000 0.000

       Correlations
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 1.000 LAG_STRE 0.000 0.000 NGDISRAT 0.000 0.000 0.000 PSDISRAT 0.000 0.000 0.000 0.000

 MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -243280.028

MODEL FIT INFORMATION

Number of Free Parameters 13

Loglikelihood

H0 Value

    Mean                           -243304.863
    Std Dev                            377.620
    Number of successful computations      100

         Proportions                   Percentiles
    Expected    Observed         Expected       Observed
       0.990       0.980      -244183.321    -244268.879
       0.980       0.980      -244080.381    -244260.598
       0.950       0.940      -243926.010    -243957.247
       0.900       0.880      -243788.821    -243848.245
       0.800       0.800      -243622.668    -243653.901
       0.700       0.680      -243502.887    -243523.432
       0.500       0.520      -243304.863    -243290.098
       0.300       0.310      -243106.839    -243100.712
       0.200       0.220      -242987.058    -242958.736
       0.100       0.080      -242820.905    -242847.031
       0.050       0.050      -242683.716    -242757.416
       0.020       0.010      -242529.345    -242619.275
       0.010       0.000      -242426.405    -242599.335

Information Criteria

Akaike (AIC)

    Mean                            486635.726
    Std Dev                            755.240
    Number of successful computations      100

         Proportions                   Percentiles
    Expected    Observed         Expected       Observed
       0.990       1.000       484878.811     485007.606
       0.980       0.990       485084.689     485224.669
       0.950       0.950       485393.431     485380.061
       0.900       0.920       485667.810     485682.984
       0.800       0.780       486000.116     485931.983
       0.700       0.690       486239.678     486133.283
       0.500       0.480       486635.726     486597.241
       0.300       0.320       487031.774     487065.734
       0.200       0.200       487271.336     487269.352
       0.100       0.120       487603.642     487621.614
       0.050       0.060       487878.021     487930.067
       0.020       0.020       488186.763     488133.873
       0.010       0.020       488392.641     488547.196

Bayesian (BIC)

    Mean                            486754.227
    Std Dev                            755.240
    Number of successful computations      100

         Proportions                   Percentiles
    Expected    Observed         Expected       Observed
       0.990       1.000       484997.311     485126.107
       0.980       0.990       485203.190     485343.170
       0.950       0.950       485511.932     485498.561
       0.900       0.920       485786.311     485801.484
       0.800       0.780       486118.616     486050.484
       0.700       0.690       486358.179     486251.783
       0.500       0.480       486754.227     486715.742
       0.300       0.320       487150.274     487184.235
       0.200       0.200       487389.837     487387.852
       0.100       0.120       487722.142     487740.115
       0.050       0.060       487996.521     488048.567
       0.020       0.020       488305.263     488252.373
       0.010       0.020       488511.142     488665.697

Sample-Size Adjusted BIC (n* = (n + 2) / 24)

    Mean                            486712.912
    Std Dev                            755.240
    Number of successful computations      100

         Proportions                   Percentiles
    Expected    Observed         Expected       Observed
       0.990       1.000       484955.997     485084.793
       0.980       0.990       485161.875     485301.856
       0.950       0.950       485470.618     485457.247
       0.900       0.920       485744.996     485760.170
       0.800       0.780       486077.302     486009.170
       0.700       0.690       486316.864     486210.469
       0.500       0.480       486712.912     486674.427
       0.300       0.320       487108.960     487142.921
       0.200       0.200       487348.522     487346.538
       0.100       0.120       487680.828     487698.800
       0.050       0.060       487955.207     488007.253
       0.020       0.020       488263.949     488211.059
       0.010       0.020       488469.827     488624.383

MODEL RESULTS

                          ESTIMATES              S. E.     M. S. E.  95%  % Sig
             Population   Average   Std. Dev.   Average             Cover Coeff

Within Level

STRESSED ON LAG_STRESS 0.550 0.5494 0.0042 0.0040 0.0000 0.950 1.000 PSDISRATIO -0.182 -0.1820 0.0107 0.0106 0.0001 0.920 1.000

Means LAG_STRESS 3.131 3.1315 0.0048 0.0046 0.0000 0.910 1.000 NGDISRATIO 0.306 0.3063 0.0016 0.0017 0.0000 0.960 1.000 PSDISRATIO 0.396 0.3963 0.0017 0.0017 0.0000 0.980 1.000

Variances LAG_STRESS 1.432 1.4332 0.0092 0.0078 0.0001 0.890 1.000 NGDISRATIO 0.193 0.1930 0.0010 0.0011 0.0000 0.960 1.000 PSDISRATIO 0.202 0.2018 0.0011 0.0011 0.0000 0.930 1.000

Residual Variances STRESSED 0.870 0.8701 0.0060 0.0063 0.0000 0.940 1.000

Between Level

Means STRESSED 1.312 1.3124 0.0152 0.0157 0.0002 0.950 1.000 SLOPE 0.381 0.3817 0.0114 0.0117 0.0001 0.960 1.000

Variances STRESSED 0.046 0.0462 0.0034 0.0033 0.0000 0.940 1.000 SLOPE 0.016 0.0163 0.0047 0.0050 0.0000 0.950 0.930

QUALITY OF NUMERICAL RESULTS

 Average Condition Number for the Information Matrix      0.353E-02
   (ratio of smallest to largest eigenvalue)

TECHNICAL 1 OUTPUT

 PARAMETER SPECIFICATION FOR WITHIN

       NU
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
                0             0             0             0

       LAMBDA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0 0 0 0 LAG_STRE 0 0 0 0 NGDISRAT 0 0 0 0 PSDISRAT 0 0 0 0

       THETA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0 LAG_STRE 0 0 NGDISRAT 0 0 0 PSDISRAT 0 0 0 0

       ALPHA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
                0             1             2             3

       BETA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0 4 0 5 LAG_STRE 0 0 0 0 NGDISRAT 0 0 0 0 PSDISRAT 0 0 0 0

       PSI
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 6 LAG_STRE 0 7 NGDISRAT 0 0 8 PSDISRAT 0 0 0 9

 PARAMETER SPECIFICATION FOR BETWEEN

       NU
          STRESSED
          ________
                0

       LAMBDA
          SLOPE         STRESSED
          ________      ________

STRESSED 0 0

       THETA
          STRESSED
          ________

STRESSED 0

       ALPHA
          SLOPE         STRESSED
          ________      ________
               10            11

       BETA
          SLOPE         STRESSED
          ________      ________

SLOPE 0 0 STRESSED 0 0

       PSI
          SLOPE         STRESSED
          ________      ________

SLOPE 12 STRESSED 0 13

 STARTING VALUES FOR WITHIN

       NU
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
            0.000         0.000         0.000         0.000

       LAMBDA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 1.000 0.000 0.000 0.000 LAG_STRE 0.000 1.000 0.000 0.000 NGDISRAT 0.000 0.000 1.000 0.000 PSDISRAT 0.000 0.000 0.000 1.000

       THETA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.000 LAG_STRE 0.000 0.000 NGDISRAT 0.000 0.000 0.000 PSDISRAT 0.000 0.000 0.000 0.000

       ALPHA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
            0.000         3.131         0.306         0.396

       BETA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.000 0.550 0.000 -0.182 LAG_STRE 0.000 0.000 0.000 0.000 NGDISRAT 0.000 0.000 0.000 0.000 PSDISRAT 0.000 0.000 0.000 0.000

       PSI
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.870 LAG_STRE 0.000 1.432 NGDISRAT 0.000 0.000 0.193 PSDISRAT 0.000 0.000 0.000 0.202

 STARTING VALUES FOR BETWEEN

       NU
          STRESSED
          ________
            0.000

       LAMBDA
          SLOPE         STRESSED
          ________      ________

STRESSED 0.000 1.000

       THETA
          STRESSED
          ________

STRESSED 0.000

       ALPHA
          SLOPE         STRESSED
          ________      ________
            0.381         1.312

       BETA
          SLOPE         STRESSED
          ________      ________

SLOPE 0.000 0.000 STRESSED 0.000 0.000

       PSI
          SLOPE         STRESSED
          ________      ________

SLOPE 0.016 STRESSED 0.000 0.046

 POPULATION VALUES FOR WITHIN

       NU
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
            0.000         0.000         0.000         0.000

       LAMBDA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 1.000 0.000 0.000 0.000 LAG_STRE 0.000 1.000 0.000 0.000 NGDISRAT 0.000 0.000 1.000 0.000 PSDISRAT 0.000 0.000 0.000 1.000

       THETA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.000 LAG_STRE 0.000 0.000 NGDISRAT 0.000 0.000 0.000 PSDISRAT 0.000 0.000 0.000 0.000

       ALPHA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________
            0.000         3.131         0.306         0.396

       BETA
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.000 0.550 0.000 -0.182 LAG_STRE 0.000 0.000 0.000 0.000 NGDISRAT 0.000 0.000 0.000 0.000 PSDISRAT 0.000 0.000 0.000 0.000

       PSI
          STRESSED      LAG_STRE      NGDISRAT      PSDISRAT
          ________      ________      ________      ________

STRESSED 0.870 LAG_STRE 0.000 1.432 NGDISRAT 0.000 0.000 0.193 PSDISRAT 0.000 0.000 0.000 0.202

 POPULATION VALUES FOR BETWEEN

       NU
          STRESSED
          ________
            0.000

       LAMBDA
          SLOPE         STRESSED
          ________      ________

STRESSED 0.000 1.000

       THETA
          STRESSED
          ________

STRESSED 0.000

       ALPHA
          SLOPE         STRESSED
          ________      ________
            0.381         1.312

       BETA
          SLOPE         STRESSED
          ________      ________

SLOPE 0.000 0.000 STRESSED 0.000 0.000

       PSI
          SLOPE         STRESSED
          ________      ________

SLOPE 0.016 STRESSED 0.000 0.046

TECHNICAL 9 OUTPUT

Error messages for each replication (if any)

 Beginning Time:  17:15:20
    Ending Time:  17:16:25
   Elapsed Time:  00:01:05

MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066

Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com

Copyright (c) 1998-2019 Muthen & Muthen

michaelhallquist commented 4 years ago

Hi there, Mahnaz,

I didn't have any problem running your code... Attached is the script for running your simulation within mplusModeler. Look in fitMonteCarlo$results. Also, you'll need to change the Mplus_command on your machine to point to where it lives on your computer (or, if it's in the default location, you can just delete that argument).

Hope this helps, Michael

working_input.R.txt

mroshanaei commented 4 years ago

Hi

Thanks for your reply. The problem is its not running on R and the result is empty. Can u please try it on R? I know It is running on MPlus.

Thanks, Mahnaz

On Wed, Nov 20, 2019 at 8:35 PM michaelhallquist notifications@github.com wrote:

Closed #113 https://github.com/michaelhallquist/MplusAutomation/issues/113.

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/michaelhallquist/MplusAutomation/issues/113?email_source=notifications&email_token=AAY4C6B6XWZPHVCB7QV3LV3QUYF2BA5CNFSM4JEMQXV2YY3PNVWWK3TUL52HS4DFWZEXG43VMVCXMZLOORHG65DJMZUWGYLUNFXW5KTDN5WW2ZLOORPWSZGOU74QPEY#event-2818115475, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAY4C6BVIAITEXPM6KHLBG3QUYF2BANCNFSM4JEMQXVQ .

michaelhallquist commented 4 years ago

Hi Mahnaz,

Please see the working_input.R file attached to my previous message. It is the R script I ran that worked just fine.

Michael