runMI with categorical data / WLSMV / fit measures
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Jan Brederecke
Mar 30, 2019, 7:10:49 AM3/30/19
to lavaan
Hi everyone,
I am struggling to extract the fit measures of my runMI-output and all the information in the available help files and this forum did not help me solve it.
I did the following with my data (questionnaire data, 5-level-likert-type-scale):
FBK_Subset[,c("FBKP_1","FBKP_2","FBKP_3","FBKP_4","FBKP_5","FBKP_6","FBKP_7","FBKP_8","FBKP_9",
"FBKP_10","FBKP_11","FBKP_12","FBKP_13","FBKP_14","FBKP_15","FBKP_16","FBKP_17","FBKP_18",
"FBKP_20","FBKP_21","FBKP_22","FBKP_23")] <-
lapply(FBK_Subset[,c("FBKP_1","FBKP_2","FBKP_3","FBKP_4","FBKP_5","FBKP_6","FBKP_7","FBKP_8","FBKP_9",
"FBKP_10","FBKP_11","FBKP_12","FBKP_13","FBKP_14","FBKP_15","FBKP_16","FBKP_17","FBKP_18",
"FBKP_20","FBKP_21","FBKP_22","FBKP_23")], ordered)
out1 <- runMI(CFAModel,
data=FBK_Subset,
m = 5,
miPackage="mice",
fun="cfa",
estimator = "WLSMV",
seed = 12345)
summary(out1)
This works fine but now I fail to extract the fit measures via fitMeasures() or lavTestLRT.mi() and I don't understand how to pass arguments from one to the other as is told.
Any help on how to extract the fit measures is highly appreciated!
I use the latest versions of both lavaan() and semTools().
With kind regards
Jan Brederecke
Terrence Jorgensen
Mar 31, 2019, 4:54:35 AM3/31/19
to lavaan
This works fine but now I fail to extract the fit measures via fitMeasures() or lavTestLRT.mi()
could you share that syntax too, to show us what the problem is?
I don't understand how to pass arguments from one to the other as is told.
If you want to pass lavTestLRT() or lavTestLRT.mi() arguments via fitMeasures(), all you have to do is specify those arguments.
fitMeasures(fit, A.method = "exact", scaled.shifted = FALSE) # NOT recommendedSince you are using DWLS, there aren't any other arguments for you to pass that aren't already fixed for that case: lavTestLRT.mi() fixes asymptotic=TRUE, test = "D2", pool.robust=TRUE; lavTestLRT() fixes method="satorra.2000".
Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
UvA web page: http://www.uva.nl/profile/t.d.jorgensen
Jan Brederecke
Mar 31, 2019, 6:17:07 PM3/31/19
to lavaan
Dear Terrence,
thank you for your kind response.
This is what I tried (I tried a lot of different combinations of arguments but deleted them all since they did not work...):
fitMeasures(out1)
lavTestLRT.mi(out1)
lavTestLRT(out1)But the output looks as follows:
> lavTestLRT.mi(out1)
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust correction can only be applied to pooled chi-squared statistic, not F statistic. "asymptotic" was switched to TRUE.
Robust corrections are made by pooling the naive chi-squared statistic across 5 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
chisq df pvalue npar ntotal
478.301 206.000 0.000 138.000 304.000
chisq.scaled df.scaled pvalue.scaled chisq.scaling.factor chisq.shift.parameters
607.926 206.000 0.000 0.933 95.048
> fitMeasures(out1)
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust corrections are made by pooling the naive chi-squared statistic across 5 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust corrections are made by pooling the naive chi-squared statistic across 5 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
Error in gp.resid.lavaan.mi(Observed = object@SampleStatsList[useImps], :
object 'N' not found
In addition: Warning message:
In pchisq(X2.sc, DF.sc, ncp = N * DF.sc * 0.05^2/nG, lower.tail = FALSE) :
full precision may not have been achieved in 'pnchisq'
> lavTestLRT(out1)
Error in lavTestLRT(out1) :
no slot of name "optim" for this object of class "lavaan.mi"All I want is the additional fit measures like RMSEA, CFI and so on and I do not yet understand my mistake.
Thank you so much for your help, Terrence!
Jan Brederecke
Mar 31, 2019, 6:39:10 PM3/31/19
to lavaan
Additionally, I tried this:
> fitMeasures(out1, fit.measures = ("all"),asymptotic=TRUE, test = "D2", pool.robust=TRUE)
Error in .local(object, fit.measures, baseline.model, ...) :
unused arguments (asymptotic = TRUE, test = "D2", pool.robust = TRUE)Terrence Jorgensen
Apr 1, 2019, 9:46:18 AM4/1/19
to lavaan
object 'N' not found
This was an old problem that was already resolved.
> fitMeasures(out1, fit.measures = ("all"),asymptotic=TRUE, test = "D2", pool.robust=TRUE)Error in .local(object, fit.measures, baseline.model, ...) :
unusedarguments (asymptotic = TRUE, test = "D2", pool.robust = TRUE)
If the arguments aren't recognized, this must be an older version of fitMeasures() before Yves Rosseel made the function generic in lavaan.
I use the latest versions of both lavaan() and semTools()
Are you sure? Try again:
install.packages("lavaan", repos = "http://www.da.ugent.be", type = "source")
devtools::install_github("simsem/semTools/semTools")I also just fixed another bug, so today semTools went from 0.5-1.915 to 0.5-1.916
Error in lavTestLRT(out1) :
no slot of name "optim" for this object of class "lavaan.mi"
lavTestLRT() is for lavaan objects, not lavaan.mi objects. You have to use lavTestLRT.mi() for lavaan.mi objects.
Jan Brederecke
Apr 3, 2019, 10:02:29 AM4/3/19
to lavaan
Dear Terrence,
thank you for your help!
I followed your instructions and installed the latest versions of semTools and lavaan but my problem was not resolved.
Additionally, my EFA (you already helped me out with this...) that I used on my first sample does not converge anymore after the update so I had to change to an ULS-EFA using psych... nonetheless, my run.mi() CFA won't give me fitmeasures which is sort of my greater problem at the moment, as I'm keen to use MI for this sample.
Any further help is highly appreciated.
Best regards
Jan
> out1 <- runMI(CFATcool,
+ data=FBK_Subset,
+ m = 5,
+ miPackage="mice",
+ fun="cfa",
+ estimator = "WLSMV",
+ seed = 12345)
>
> summary(out1)
lavaan.mi object based on 5 imputed data sets.
See class?lavaan.mi help page for available methods.
Convergence information:
The model converged on 5 imputed data sets
Rubin's (1987) rules were used to pool point and SE estimates across 5 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.
Parameter Estimates:
Information Expected
Information saturated (h1) model Unstructured
Standard Errors Robust.sem
Latent Variables:
Estimate Std.Err t-value df P(>|t|)
AllgemeinepsychischeStressoren =~
FBKP_1 1.000
FBKP_3 1.005 0.021 48.988 3730.703 0.000
FBKP_4 0.781 0.035 22.531 1906.053 0.000
FBKP_5 0.893 0.025 35.321 4253.089 0.000
FBKP_6 0.801 0.038 21.329 3989.771 0.000
FBKP_7 0.899 0.024 37.851 1636.363 0.000
FBKP_9 0.883 0.027 32.345 7896.067 0.000
FBKP_10 0.775 0.034 22.678 Inf 0.000
FBKP_12 0.963 0.021 45.726 Inf 0.000
Progredienzangst =~
FBKP_8 1.000
FBKP_15 1.001 0.031 32.579 Inf 0.000
FBKP_16 0.739 0.047 15.779 Inf 0.000
FBKP_17 1.021 0.030 33.680 3871.302 0.000
FBKP_18 0.759 0.049 15.616 1264.125 0.000
FBKP_20 0.941 0.034 27.862 502.513 0.000
FBKP_21 0.997 0.030 33.217 Inf 0.000
FBKP_22 0.715 0.050 14.235 3757.965 0.000
FBKP_23 1.053 0.028 37.551 744.200 0.000
PartnerschaftlicheStressoren =~
FBKP_2 1.000
FBKP_11 0.872 0.062 13.957 3839.797 0.000
FBKP_13 1.091 0.052 21.169 Inf 0.000
FBKP_14 1.076 0.054 20.075 686.255 0.000
Covariances:
Estimate Std.Err t-value df P(>|t|)
AllgemeinepsychischeStressoren ~~
Progredinzngst 0.543 0.033 16.285 7204.925 0.000
PrtnrschftlchS 0.410 0.041 10.089 Inf 0.000
Progredienzangst ~~
PrtnrschftlchS 0.305 0.040 7.700 6905.005 0.000
Intercepts:
Estimate Std.Err t-value df P(>|t|)
.FBKP_1 0.000
.FBKP_3 0.000
.FBKP_4 0.000
.FBKP_5 0.000
.FBKP_6 0.000
.FBKP_7 0.000
.FBKP_9 0.000
.FBKP_10 0.000
.FBKP_12 0.000
.FBKP_8 0.000
.FBKP_15 0.000
.FBKP_16 0.000
.FBKP_17 0.000
.FBKP_18 0.000
.FBKP_20 0.000
.FBKP_21 0.000
.FBKP_22 0.000
.FBKP_23 0.000
.FBKP_2 0.000
.FBKP_11 0.000
.FBKP_13 0.000
.FBKP_14 0.000
AllgmnpsychscS 0.000
Progredinzngst 0.000
PrtnrschftlchS 0.000
Thresholds:
Estimate Std.Err t-value df P(>|t|)
FBKP_1|t1 -0.596 0.079 -7.581 Inf 0.000
FBKP_1|t2 -0.071 0.074 -0.963 Inf 0.336
FBKP_1|t3 0.361 0.075 4.782 Inf 0.000
FBKP_1|t4 1.045 0.090 11.573 4370.060 0.000
FBKP_1|t5 1.727 0.131 13.138 1006.527 0.000
FBKP_3|t1 -0.553 0.078 -7.100 Inf 0.000
FBKP_3|t2 -0.181 0.074 -2.440 Inf 0.015
FBKP_3|t3 0.269 0.075 3.601 Inf 0.000
FBKP_3|t4 0.966 0.088 11.025 Inf 0.000
FBKP_3|t5 1.789 0.138 13.010 741.812 0.000
FBKP_4|t1 -0.049 0.074 -0.672 Inf 0.502
FBKP_4|t2 0.412 0.076 5.425 8073.090 0.000
FBKP_4|t3 0.738 0.081 9.065 Inf 0.000
FBKP_4|t4 1.256 0.099 12.663 Inf 0.000
FBKP_4|t5 1.877 0.147 12.775 6502.062 0.000
FBKP_5|t1 -0.262 0.075 -3.512 Inf 0.000
FBKP_5|t2 0.269 0.075 3.601 Inf 0.000
FBKP_5|t3 0.668 0.080 8.360 5708.816 0.000
FBKP_5|t4 1.068 0.091 11.719 2504.940 0.000
FBKP_5|t5 1.907 0.150 12.673 2274.567 0.000
FBKP_6|t1 0.013 0.074 0.179 3276.308 0.858
FBKP_6|t2 0.496 0.077 6.442 2631.275 0.000
FBKP_6|t3 0.856 0.084 10.148 1382.981 0.000
FBKP_6|t4 1.278 0.100 12.746 Inf 0.000
FBKP_6|t5 1.859 0.145 12.831 2802.615 0.000
FBKP_7|t1 -0.553 0.078 -7.100 Inf 0.000
FBKP_7|t2 0.136 0.074 1.836 Inf 0.066
FBKP_7|t3 0.524 0.077 6.772 Inf 0.000
FBKP_7|t4 1.065 0.091 11.701 6540.398 0.000
FBKP_7|t5 1.727 0.131 13.143 Inf 0.000
FBKP_9|t1 -0.493 0.077 -6.398 Inf 0.000
FBKP_9|t2 0.117 0.074 1.590 Inf 0.112
FBKP_9|t3 0.509 0.077 6.596 4332.059 0.000
FBKP_9|t4 1.062 0.091 11.682 942.202 0.000
FBKP_9|t5 1.831 0.142 12.912 7817.911 0.000
FBKP_10|t1 -0.636 0.079 -8.015 Inf 0.000
FBKP_10|t2 -0.139 0.074 -1.881 Inf 0.060
FBKP_10|t3 0.255 0.075 3.423 Inf 0.001
FBKP_10|t4 0.708 0.081 8.767 Inf 0.000
FBKP_10|t5 1.377 0.106 13.042 2700.219 0.000
FBKP_12|t1 -0.592 0.079 -7.537 Inf 0.000
FBKP_12|t2 -0.021 0.074 -0.291 Inf 0.771
FBKP_12|t3 0.389 0.076 5.137 Inf 0.000
FBKP_12|t4 1.071 0.091 11.737 8129.746 0.000
FBKP_12|t5 2.089 0.175 11.906 1007.018 0.000
FBKP_8|t1 -0.897 0.085 -10.492 Inf 0.000
FBKP_8|t2 -0.387 0.076 -5.115 Inf 0.000
FBKP_8|t3 0.104 0.074 1.411 3228.895 0.158
FBKP_8|t4 0.580 0.078 7.407 2182.588 0.000
FBKP_8|t5 1.340 0.104 12.946 Inf 0.000
FBKP_15|t1 -0.837 0.084 -9.984 Inf 0.000
FBKP_15|t2 -0.414 0.076 -5.448 4782.280 0.000
FBKP_15|t3 -0.194 0.074 -2.619 Inf 0.009
FBKP_15|t4 0.252 0.074 3.378 Inf 0.001
FBKP_15|t5 0.632 0.079 7.972 7175.969 0.000
FBKP_16|t1 -1.285 0.101 -12.772 4429.571 0.000
FBKP_16|t2 -0.823 0.083 -9.861 Inf 0.000
FBKP_16|t3 -0.394 0.076 -5.204 Inf 0.000
FBKP_16|t4 0.018 0.074 0.246 Inf 0.805
FBKP_16|t5 0.642 0.079 8.080 Inf 0.000
FBKP_17|t1 -1.186 0.096 -12.364 6055.854 0.000
FBKP_17|t2 -0.742 0.082 -9.107 Inf 0.000
FBKP_17|t3 -0.392 0.076 -5.182 Inf 0.000
FBKP_17|t4 0.035 0.074 0.470 Inf 0.638
FBKP_17|t5 0.644 0.079 8.101 Inf 0.000
FBKP_18|t1 -0.984 0.088 -11.160 1629.171 0.000
FBKP_18|t2 -0.496 0.077 -6.442 4365.146 0.000
FBKP_18|t3 -0.111 0.074 -1.500 9795.253 0.134
FBKP_18|t4 0.384 0.076 5.070 Inf 0.000
FBKP_18|t5 0.976 0.088 11.103 5163.115 0.000
FBKP_20|t1 -1.282 0.100 -12.759 Inf 0.000
FBKP_20|t2 -0.784 0.082 -9.507 2758.456 0.000
FBKP_20|t3 -0.359 0.075 -4.760 8492.501 0.000
FBKP_20|t4 0.023 0.074 0.314 Inf 0.754
FBKP_20|t5 0.640 0.079 8.058 Inf 0.000
FBKP_21|t1 -1.503 0.113 -13.244 Inf 0.000
FBKP_21|t2 -0.909 0.086 -10.593 Inf 0.000
FBKP_21|t3 -0.427 0.076 -5.603 4752.969 0.000
FBKP_21|t4 0.058 0.074 0.784 7482.226 0.433
FBKP_21|t5 0.620 0.079 7.842 Inf 0.000
FBKP_22|t1 -0.914 0.086 -10.632 Inf 0.000
FBKP_22|t2 -0.400 0.076 -5.270 Inf 0.000
FBKP_22|t3 -0.117 0.074 -1.590 5786.860 0.112
FBKP_22|t4 0.389 0.076 5.137 3584.561 0.000
FBKP_22|t5 0.873 0.085 10.291 2351.218 0.000
FBKP_23|t1 -1.180 0.096 -12.332 Inf 0.000
FBKP_23|t2 -0.666 0.080 -8.338 Inf 0.000
FBKP_23|t3 -0.184 0.074 -2.484 Inf 0.013
FBKP_23|t4 0.320 0.075 4.270 Inf 0.000
FBKP_23|t5 0.873 0.085 10.291 Inf 0.000
FBKP_2|t1 -0.166 0.074 -2.238 Inf 0.025
FBKP_2|t2 0.405 0.076 5.337 4765.323 0.000
FBKP_2|t3 0.832 0.084 9.943 Inf 0.000
FBKP_2|t4 1.513 0.114 13.252 Inf 0.000
FBKP_2|t5 2.133 0.182 11.700 Inf 0.000
FBKP_11|t1 -0.538 0.078 -6.925 Inf 0.000
FBKP_11|t2 0.051 0.074 0.694 Inf 0.488
FBKP_11|t3 0.305 0.075 4.069 Inf 0.000
FBKP_11|t4 0.771 0.082 9.381 Inf 0.000
FBKP_11|t5 1.408 0.107 13.109 Inf 0.000
FBKP_13|t1 0.021 0.074 0.291 Inf 0.771
FBKP_13|t2 0.494 0.077 6.420 Inf 0.000
FBKP_13|t3 0.849 0.084 10.087 Inf 0.000
FBKP_13|t4 1.170 0.095 12.284 6341.644 0.000
FBKP_13|t5 1.898 0.149 12.697 522.160 0.000
FBKP_14|t1 -0.033 0.074 -0.448 Inf 0.654
FBKP_14|t2 0.513 0.077 6.640 Inf 0.000
FBKP_14|t3 0.773 0.082 9.402 5606.978 0.000
FBKP_14|t4 1.297 0.101 12.811 3454.131 0.000
FBKP_14|t5 2.059 0.171 12.063 Inf 0.000
Variances:
Estimate Std.Err t-value df P(>|t|)
.FBKP_1 0.178
.FBKP_3 0.171
.FBKP_4 0.499
.FBKP_5 0.344
.FBKP_6 0.472
.FBKP_7 0.336
.FBKP_9 0.360
.FBKP_10 0.507
.FBKP_12 0.237
.FBKP_8 0.299
.FBKP_15 0.297
.FBKP_16 0.617
.FBKP_17 0.269
.FBKP_18 0.596
.FBKP_20 0.379
.FBKP_21 0.304
.FBKP_22 0.642
.FBKP_23 0.222
.FBKP_2 0.346
.FBKP_11 0.503
.FBKP_13 0.221
.FBKP_14 0.242
AllgmnpsychscS 0.822 0.023 35.324 2404.089 0.000
Progredinzngst 0.701 0.036 19.471 4554.905 0.000
PrtnrschftlchS 0.654 0.049 13.408 4521.102 0.000
Scales y*:
Estimate Std.Err t-value df P(>|t|)
FBKP_1 1.000
FBKP_3 1.000
FBKP_4 1.000
FBKP_5 1.000
FBKP_6 1.000
FBKP_7 1.000
FBKP_9 1.000
FBKP_10 1.000
FBKP_12 1.000
FBKP_8 1.000
FBKP_15 1.000
FBKP_16 1.000
FBKP_17 1.000
FBKP_18 1.000
FBKP_20 1.000
FBKP_21 1.000
FBKP_22 1.000
FBKP_23 1.000
FBKP_2 1.000
FBKP_11 1.000
FBKP_13 1.000
FBKP_14 1.000
> fitMeasures(out1)
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust corrections are made by pooling the naive chi-squared statistic across 5 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust corrections are made by pooling the naive chi-squared statistic across 5 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
Error in gp.resid.lavaan.mi(Observed = object@SampleStatsList[useImps], :
object 'N' not found
In addition: Warning message:
In pchisq(X2.sc, DF.sc, ncp = N * DF.sc * 0.05^2/nG, lower.tail = FALSE) :
full precision may not have been achieved in 'pnchisq'
>
> lavTestLRT.mi(out1)
"D3" only available using maximum likelihood estimation. Changed test to "D2".
Robust correction can only be applied to pooled chi-squared statistic, not F statistic. "asymptotic" was switched to TRUE.
Robust corrections are made by pooling the naive chi-squared statistic across 5 imputations for which the model converged, then applying the average (across imputations) scaling factor and shift parameter to that pooled value.
To instead pool the robust test statistics, set test = "D2" and pool.robust = TRUE.
chisq df pvalue npar ntotal chisq.scaled df.scaled
478.200 206.000 0.000 135.000 304.000 612.335 206.000
pvalue.scaled chisq.scaling.factor chisq.shift.parameters
0.000 0.923 94.182
> fitMeasures(out1,test = "D2" , pool.robust = TRUE)
Error in .local(object, fit.measures, baseline.model, ...) :
unused arguments (test = "D2", pool.robust = TRUE)
Message has been deleted
Terrence Jorgensen
Apr 4, 2019, 9:46:11 AM4/4/19
to lavaan
I followed your instructions and installed the latest versions of semTools and lavaan but my problem was not resolved.
What does this output tell you the version numbers are?
sessionInfo()Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
Jan Brederecke
Apr 5, 2019, 6:08:09 AM4/5/19
to lavaan
R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] semTools_0.5-1 lavaan_0.6-4.1374
loaded via a namespace (and not attached):
[1] compiler_3.5.3 tools_3.5.3 mnormt_1.5-5 pbivnorm_0.6.0 stats4_3.5.3 I am currently at home but I followed the same procedure at work, creating the same results.
Terrence Jorgensen
Apr 6, 2019, 4:19:13 PM4/6/19
to lavaan
[1] semTools_0.5-1 lavaan_0.6-4.1374
It looks like the development version of lavaan installed, but not semTools, because that is the CRAN version, not the current development version (0.5-1.916). Did you notice an error when you tried installing it? Make sure the devtools package is installed first:
install.packages("devtools")
devtools::install_github("simsem/semTools/semTools")Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
Jan Brederecke
Apr 8, 2019, 4:55:54 AM4/8/19
to lavaan
Dear Terrence,
I follow your instructions but when I try to update semTools from your link, the following error is desplayed:
> devtools::install_github("simsem/semTools/semTools") Error in curl::curl_fetch_memory(url, handle = h) : Failed to connect to api.github.com port 443: Timed out
Kind regards
Jan
Terrence Jorgensen
Apr 8, 2019, 7:32:56 AM4/8/19
to lavaan
I follow your instructions but when I try to update semTools from your link, the following error is desplayed:
Looks like an internet connection issue, possibly due to security measures by your employer if that's where you tried this. One of the solutions in this thread might work for you:
If not, I can build a source file for you to download and install manually.
Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
Jan Brederecke
Apr 8, 2019, 9:58:06 AM4/8/19
to lavaan
Terrence,
I highly appreciate your efforts!
After trying what was recommended in your linked forum, still the same error is displayed.
It would be great, to get such a sourcefile and to finally be able to see what my CFA fitmeasures are, as I really want to employ the MI version for that paper.
Thank you so much for all your time and helpful advice!
Jan
Terrence Jorgensen
Apr 10, 2019, 6:45:05 AM4/10/19
to lavaan
It would be great, to get such a sourcefile
attached. Download to your working directory (or anywhere, but add the full path before the file name) and install with
install.packages("semTools_0.5-1.917.tar.gz", type = "source", repos = NULL)Jan Brederecke
Apr 10, 2019, 7:49:48 AM4/10/19
to lavaan
Dear Terrence,
thank you for creating the file for me. Unfortunately, it produces an error when I try to load it.
A quick internet search did not really help me but I will try it again, when I come home from work (Windows environment) and have access to my Mac, maybe that helps ?!
Have a great day!
Jan
> install.packages("semTools_0.5-1.917.tar.gz", type = "source", repos = NULL)
Installing package into ‘C:/Users/brederja/Documents/R/win-library/3.5’
(as ‘lib’ is unspecified)
ERROR: cannot extract package from 'semTools_0.5-1.917.tar.gz'
In R CMD INSTALL
Warning in install.packages :
installation of package ‘semTools_0.5-1.917.tar.gz’ had non-zero exit status
Jan Brederecke
Apr 11, 2019, 3:37:58 AM4/11/19
to lavaan
Dear Terrence,
thank you so much! It finally worked when I tried it at home!
Thank you for all your support and efforts!
Best regards
Jan
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