CRAN Package Check Results for Package lessR

Last updated on 2017-12-11 08:48:43 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.6.6 2.88 128.17 131.05 ERROR
r-devel-linux-x86_64-debian-gcc 3.6.6 2.80 111.50 114.30 ERROR
r-devel-linux-x86_64-fedora-clang 3.6.7 177.30 OK
r-devel-linux-x86_64-fedora-gcc 3.6.7 168.30 OK
r-devel-windows-ix86+x86_64 3.6.7 14.00 229.00 243.00 OK
r-patched-linux-x86_64 3.6.6 3.08 162.74 165.82 OK
r-patched-solaris-x86 3.6.7 170.30 OK
r-release-linux-x86_64 3.6.6 3.75 166.24 169.99 OK
r-release-windows-ix86+x86_64 3.6.6 9.00 163.00 172.00 OK
r-release-osx-x86_64 3.6.6 OK
r-oldrel-windows-ix86+x86_64 3.6.6 10.00 181.00 191.00 OK
r-oldrel-osx-x86_64 3.6.6 OK

Check Details

Version: 3.6.6
Check: examples
Result: ERROR
    Running examples in ‘lessR-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: Regression
    > ### Title: Regression Analysis
    > ### Aliases: Regression reg reg.brief
    > ### Keywords: regression
    >
    > ### ** Examples
    >
    > # read internal data set
    > mydata <- rd("Reading", format="lessR", quiet=TRUE)
    
    > # do not need all this data, so take only 30% to reduce CPU time
    > mydata <- Subset(random=.3)
    
    -----------------
    Before the subset
    -----------------
    
    Number of variables in mydata: 4
    Number of cases (rows) in mydata: 100
    
    First five rows of data for data frame: mydata
    --------------------------------------------------------------------
     Reading Verbal Absent Income
    1 73 64 4 36
    2 77 80 2 103
    3 42 54 9 4
    4 83 60 2 95
    5 94 91 0 75
    
    
    Rows of data randomly extracted
    ------------------------------------------
    Proportion of randomly retained rows: 0.3
    Number of randomly retained rows: 30
    
    
    ----------------
    After the subset
    ----------------
    
    Number of variables : 4
    Number of cases (rows) : 30
    
    
    First five rows of data
    --------------------------------------------------------------------
     Reading Verbal Absent Income
    1 73 64 4 36
    6 84 67 0 90
    10 45 60 12 35
    16 100 79 0 91
    17 80 65 5 89
    
    >
    > # one-predictor regression
    > # Provide all default analyses including scatterplot etc.
    > # Can abbreviate Regression with reg
    > Regression(Reading ~ Verbal)
    dev.new(): using pdf(file="Rplots126.pdf")
    dev.new(): using pdf(file="Rplots127.pdf")
    dev.new(): using pdf(file="Rplots128.pdf")
    
    >>> Suggestion
    # Create an R markdown file for interpretative output with the Rmd option
    # In RStudio, open and then knit this file to generate the output
    Regression(my.formula=Reading ~ Verbal, Rmd="eg")
    
    
     BACKGROUND
    
    Data Frame: mydata
    
    Response Variable: Reading, Reading Ability
    Predictor Variable: Verbal, Verbal Aptitude
    
    Number of cases (rows) of data: 30
    Number of cases retained for analysis: 30
    
    
     BASIC ANALYSIS
    
    Estimated Model
    
     Estimate Std Err t-value p-value Lower 95% Upper 95%
    (Intercept) 43.026 12.738 3.378 0.002 16.933 69.119
     Verbal 0.459 0.184 2.502 0.018 0.083 0.835
    
    
    Model Fit
    
    Standard deviation of residuals: 10.462 for 28 degrees of freedom
    
    R-squared: 0.183 Adjusted R-squared: 0.153 PRESS R-squared: 0.092
    
    Null hypothesis that all population slope coefficients are 0:
     F-statistic: 6.259 df: 1 and 28 p-value: 0.018
    
    
    Analysis of Variance
    
     df Sum Sq Mean Sq F-value p-value
    Model 1 684.982 684.982 6.259 0.018
    Residuals 28 3064.485 109.446
    Reading 29 3749.467 129.292
    
    
     RELATIONS AMONG THE VARIABLES
    
    Correlation Matrix
    
     Reading Verbal
     Reading 1.00 0.43
     Verbal 0.43 1.00
    
    
     RESIDUALS AND INFLUENCE
    
    Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
     [sorted by Cook's Distance]
     [res.rows = 20, out of 30 rows of data, or do res.rows="all"]
    ------------------------------------------------------
     Verbal Reading fitted resid rstdnt dffits cooks
     10 60 45 70.583 -25.583 -2.810 -0.685 0.188
     16 79 100 79.310 20.690 2.180 0.582 0.150
     20 70 95 75.176 19.824 2.033 0.381 0.065
     25 79 66 79.310 -13.310 -1.335 -0.357 0.062
     63 63 57 71.961 -14.961 -1.494 -0.317 0.048
     83 73 61 76.554 -15.554 -1.555 -0.314 0.047
     67 69 90 74.717 15.283 1.520 0.283 0.038
     21 49 71 65.531 5.469 0.561 0.237 0.029
     19 73 88 76.554 11.446 1.121 0.227 0.025
     88 66 63 73.339 -10.339 -1.007 -0.193 0.019
     6 67 84 73.798 10.202 0.992 0.186 0.017
     58 83 76 81.147 -5.147 -0.511 -0.168 0.014
     75 68 65 74.258 -9.258 -0.897 -0.167 0.014
     28 57 75 69.206 5.794 0.569 0.162 0.013
     97 84 86 81.606 4.394 0.438 0.151 0.012
     34 46 67 64.153 2.847 0.297 0.144 0.011
     81 54 72 67.828 4.172 0.414 0.137 0.010
     17 65 80 72.880 7.120 0.687 0.135 0.009
     27 87 80 82.984 -2.984 -0.302 -0.121 0.008
     32 75 72 77.473 -5.473 -0.529 -0.116 0.007
    
    
     FORECASTING ERROR
    
    Data, Predicted, Standard Error of Forecast, 95% Prediction Intervals
     [sorted by lower bound of prediction interval]
     [to see all intervals do pred.rows="all"]
    -------------------------------------------------------
     Verbal Reading pred sf pi:lwr pi:upr width
     34 46 67 64.153 11.415 40.770 87.537 46.766
     21 49 71 65.531 11.227 42.534 88.528 45.994
    ...
     75 68 65 74.258 10.635 52.473 96.043 43.570
     67 69 90 74.717 10.635 52.933 96.501 43.569
     20 70 95 75.176 10.638 53.386 96.967 43.581
    ...
     97 84 86 81.606 11.004 59.066 104.147 45.081
     62 86 83 82.525 11.104 59.779 105.270 45.491
     27 87 80 82.984 11.158 60.128 105.841 45.713
    
    
    ------------------------------------------------------------
    Plot 1: Distribution of Residuals
    Plot 2: Residuals vs Fitted Values
    Plot 3: Regression Line, Confidence and Prediction Intervals
    ------------------------------------------------------------
    
    > # Provide only the brief analysis on the standardized variables
    > reg.brief(Reading ~ Verbal, standardize=TRUE)
    dev.new(): using pdf(file="Rplots129.pdf")
    
    >>> Suggestion
    # Create an R markdown file for interpretative output with the Rmd option
    # In RStudio, open and then knit this file to generate the output
    reg(Reading ~ Verbal, standardize=TRUE, Rmd="eg")
    
    
     BACKGROUND
    
    Data Frame: mydata
    
    Response Variable: Reading, Reading Ability
    Predictor Variable: Verbal, Verbal Aptitude
    
    Number of cases (rows) of data: 30
    Number of cases retained for analysis: 30
    
    Data are Standardized
    
    
     BASIC ANALYSIS
    
    Estimated Model
    
     Estimate Std Err t-value p-value Lower 95% Upper 95%
    (Intercept) -0.000 0.168 -0.000 1.000 -0.344 0.344
     Verbal 0.428 0.171 2.503 0.018 0.078 0.777
    
    
    Model Fit
    
    Standard deviation of residuals: 0.920 for 28 degrees of freedom
    
    R-squared: 0.183 Adjusted R-squared: 0.154 PRESS R-squared: 0.092
    
    Null hypothesis that all population slope coefficients are 0:
     F-statistic: 6.264 df: 1 and 28 p-value: 0.018
    
    
    Analysis of Variance
    
     df Sum Sq Mean Sq F-value p-value
    Model 1 5.302 5.302 6.264 0.018
    Residuals 28 23.698 0.846
    Reading 29 29.000 1.000
    
    
     RELATIONS AMONG THE VARIABLES
    
     RESIDUALS AND INFLUENCE
    
     FORECASTING ERROR
    >
    > # Access the pieces of output, here in an object named \code{r}
    > r <- reg(Reading ~ Verbal + Absent + Income)
    dev.new(): using pdf(file="Rplots130.pdf")
    dev.new(): using pdf(file="Rplots131.pdf")
    dev.new(): using pdf(file="Rplots132.pdf")
    > # Display all output at the console in the standard sequence
    > r
    
    >>> Suggestion
    # Create an R markdown file for interpretative output with the Rmd option
    # In RStudio, open and then knit this file to generate the output
    reg(Reading ~ Verbal + Absent + Income, Rmd="eg")
    
    
     BACKGROUND
    
    Data Frame: mydata
    
    Response Variable: Reading, Reading Ability
    Predictor Variable 1: Verbal, Verbal Aptitude
    Predictor Variable 2: Absent, Number of Absences
    Predictor Variable 3: Income, Family Income ($1000)
    
    Number of cases (rows) of data: 30
    Number of cases retained for analysis: 30
    
    
     BASIC ANALYSIS
    
    Estimated Model
    
     Estimate Std Err t-value p-value Lower 95% Upper 95%
    (Intercept) 78.332 18.809 4.165 0.000 39.670 116.994
     Verbal 0.120 0.215 0.560 0.580 -0.321 0.562
     Absent -2.327 0.905 -2.571 0.016 -4.188 -0.466
     Income -0.077 0.070 -1.090 0.286 -0.222 0.068
    
    
    Model Fit
    
    Standard deviation of residuals: 9.686 for 26 degrees of freedom
    
    R-squared: 0.349 Adjusted R-squared: 0.274 PRESS R-squared: 0.134
    
    Null hypothesis that all population slope coefficients are 0:
     F-statistic: 4.655 df: 3 and 26 p-value: 0.010
    
    
    Analysis of Variance
    
     df Sum Sq Mean Sq F-value p-value
     Verbal 1 684.982 684.982 7.301 0.012
     Absent 1 513.754 513.754 5.476 0.027
     Income 1 111.397 111.397 1.187 0.286
    Model 3 1310.134 436.711 4.655 0.010
    
    Residuals 26 2439.333 93.821
    
    Reading 29 3749.467 129.292
    
    
     RELATIONS AMONG THE VARIABLES
    
    Correlation Matrix
    
     Reading Verbal Absent Income
     Reading 1.00 0.43 -0.55 0.06
     Verbal 0.43 1.00 -0.57 0.04
     Absent -0.55 -0.57 1.00 -0.44
     Income 0.06 0.04 -0.44 1.00
    
    
    Collinearity
    
     Tolerance VIF
     Verbal 0.626 1.598
     Absent 0.507 1.973
     Income 0.746 1.340
    
    
    Best Subset Regression Models
    
     Verbal Absent Income R2adj X's
     0 1 1 0.293 2
     0 1 0 0.275 1
     1 1 1 0.274 3
     1 1 0 0.269 2
     1 0 0 0.153 1
     1 0 1 0.124 2
     0 0 1 -0.033 1
    
    [based on Thomas Lumley's leaps function from the leaps package]
    
    
    
     RESIDUALS AND INFLUENCE
    
    Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
     [sorted by Cook's Distance]
     [res.rows = 20, out of 30 rows of data, or do res.rows="all"]
    ---------------------------------------------------------------------
     Verbal Absent Income Reading fitted resid rstdnt dffits cooks
     10 60 12 35 45 54.935 -9.935 -1.455 -1.402 0.471
     63 63 2 127 57 71.508 -14.508 -1.691 -0.735 0.126
     16 79 0 91 100 80.851 19.149 2.208 0.647 0.091
     20 70 0 111 95 78.233 16.767 1.913 0.629 0.090
     21 49 8 15 71 64.457 6.543 0.790 0.491 0.061
     83 73 1 74 61 79.107 -18.107 -2.030 -0.465 0.048
     17 65 5 89 80 67.684 12.316 1.357 0.437 0.046
     25 79 1 69 66 80.213 -14.213 -1.563 -0.428 0.043
     34 46 6 110 67 61.459 5.541 0.661 0.399 0.041
     28 57 6 84 75 64.778 10.222 1.127 0.404 0.040
     81 54 0 100 72 77.153 -5.153 -0.610 -0.360 0.033
     19 73 1 22 88 83.098 4.902 0.571 0.316 0.026
     67 69 3 63 90 74.815 15.185 1.651 0.330 0.026
     97 84 2 52 86 79.792 6.208 0.681 0.266 0.018
     62 86 3 43 83 78.396 4.604 0.521 0.254 0.017
     32 75 0 117 72 78.374 -6.374 -0.693 -0.251 0.016
     51 65 2 48 72 77.813 -5.813 -0.628 -0.219 0.012
     88 66 4 69 63 71.667 -8.667 -0.910 -0.187 0.009
     75 68 3 81 65 73.313 -8.313 -0.871 -0.171 0.007
     6 67 0 90 84 79.484 4.516 0.482 0.154 0.006
    
    
     FORECASTING ERROR
    
    Data, Predicted, Standard Error of Forecast, 95% Prediction Intervals
     [sorted by lower bound of prediction interval]
     [to see all intervals do pred.rows="all"]
    ----------------------------------------------------------------------
     Verbal Absent Income Reading pred sf pi:lwr pi:upr width
     10 60 12 35 45 54.935 11.790 30.701 79.170 48.469
     34 46 6 110 67 61.459 10.902 39.050 83.867 44.817
    ...
     57 61 3 71 70 73.239 9.969 52.747 93.731 40.984
     75 68 3 81 65 73.313 9.864 53.037 93.590 40.552
     1 64 4 36 73 73.959 10.171 53.053 94.865 41.813
    ...
     16 79 0 91 100 80.851 10.062 60.169 101.533 41.364
     19 73 1 22 88 83.098 10.763 60.974 105.223 44.248
     89 83 0 60 82 83.712 10.250 62.643 104.781 42.137
    
    
    ----------------------------------
    Plot 1: Distribution of Residuals
    Plot 2: Residuals vs Fitted Values
    Plot 3: ScatterPlot Matrix
    ----------------------------------
    
    > # list the names of all the saved components
    > names(r)
     [1] "out_suggest" "call" "formula" "out_title_bck" "out_background" "out_title_basic"
     [7] "out_estimates" "out_fit" "out_anova" "out_title_rel" "out_cor" "out_collinear"
    [13] "out_subsets" "out_title_res" "out_residuals" "out_title_pred" "out_predict" "out_ref"
    [19] "out_Rmd" "out_plots" "n.vars" "n.obs" "n.keep" "coefficients"
    [25] "sterrs" "tvalues" "pvalues" "cilb" "ciub" "anova_model"
    [31] "anova_residual" "anova_total" "se" "resid_range" "Rsq" "Rsqadj"
    [37] "PRESS" "RsqPRESS" "cor" "tolerances" "vif" "resid.max"
    [43] "pred_min_max" "residuals" "fitted" "cooks.distance" "model" "terms"
    > # Display just the estimated coefficients and their inferential analysis
    > r$out_estimates
    Estimated Model
    
     Estimate Std Err t-value p-value Lower 95% Upper 95%
    (Intercept) 78.332 18.809 4.165 0.000 39.670 116.994
     Verbal 0.120 0.215 0.560 0.580 -0.321 0.562
     Absent -2.327 0.905 -2.571 0.016 -4.188 -0.466
     Income -0.077 0.070 -1.090 0.286 -0.222 0.068
    >
    > # Generate an R markdown file with the option: Rmd
    > # Output file here will be read.Rmd, a simple text file that can
    > # be edited with any text editor including RStudio from which it
    > # can be knit to generate dynamic output to a Word document,
    > # pdf file or html file
    > reg(Reading ~ Verbal + Absent, Rmd="read")
    dev.new(): using pdf(file="Rplots133.pdf")
    dev.new(): using pdf(file="Rplots134.pdf")
    dev.new(): using pdf(file="Rplots135.pdf")
    Error in var.unit[i] <- myunits[which(names(myunits) == nm[i])] :
     replacement has length zero
    Calls: reg -> Regression -> .reg.Rmd
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc