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r:logistic_regression_analysis

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r:logistic_regression_analysis [2016/12/14 10:13] – hkimscilr:logistic_regression_analysis [2023/12/07 08:00] (current) – [e.g. 1] hkimscil
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-====== Logitistic Regression Analysis ======+====== e.g. 1 ======
  
-$$ log({\frac{p}{(1-p)}) = \beta_{0} + \beta_{1}X $$ 
-  * p = 변인 X가 A일 확률 
-  * 1-p = 변인 X가 A가 아닐 확률 
  
 +<code>
 +> Logit(Turnover ~ JS, data=td)
  
-$$ \displaystyle{\frac{p}{(1-p)} = \beta_{0} + \beta_{1}X $$+Data Frame:  mydata 
  
 +Response Variable:   Turnover
 +Predictor Variable 1:  JS
 +
 +Number of cases (rows) of data:  99 
 +Number of cases retained for analysis:  98 
 +
 +
 +   BASIC ANALYSIS 
 +
 +-- Estimated Model of Turnover for the Logit of Reference Group Membership
 +
 +             Estimate    Std Err  z-value  p-value   Lower 95%   Upper 95%
 +(Intercept)   -1.8554     0.6883   -2.695    0.007     -3.2044     -0.5063 
 +         JS    0.4378     0.1958    2.236    0.025      0.0540      0.8216 
 +
 +
 +-- Odds Ratios and Confidence Intervals
 +
 +             Odds Ratio   Lower 95%   Upper 95%
 +(Intercept)      0.1564      0.0406      0.6027 
 +         JS      1.5492      1.0555      2.2740 
 +
 +
 +-- Model Fit
 +
 +    Null deviance: 131.746 on 97 degrees of freedom
 +Residual deviance: 126.341 on 96 degrees of freedom
 +
 +AIC: 130.3413 
 +
 +Number of iterations to convergence: 4 
 +
 +
 +   ANALYSIS OF RESIDUALS AND INFLUENCE 
 +Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
 +   [sorted by Cook's Distance]
 +   [res_rows = 20 out of 98 cases (rows) of data]
 +--------------------------------------------------------------------
 +     JS Turnover fitted residual rstudent  dffits   cooks
 +69 6.00     quit 0.6838  -0.6838  -1.5688 -0.3725 0.08496
 +7  1.38     stay 0.2225   0.7775   1.7682  0.2877 0.06241
 +73 5.48     quit 0.6327  -0.6327  -1.4476 -0.2949 0.04889
 +58 5.43     quit 0.6276  -0.6276  -1.4363 -0.2877 0.04618
 +12 1.72     stay 0.2493   0.7507   1.6920  0.2486 0.04353
 +31 1.77     stay 0.2534   0.7466   1.6810  0.2429 0.04117
 +13 1.96     stay 0.2695   0.7305   1.6393  0.2219 0.03314
 +1  4.96     quit 0.5783  -0.5783  -1.3332 -0.2239 0.02609
 +33 4.88     quit 0.5698  -0.5698  -1.3162 -0.2138 0.02353
 +84 4.66     quit 0.5460  -0.5460  -1.2703 -0.1875 0.01757
 +63 4.65     quit 0.5449  -0.5449  -1.2682 -0.1863 0.01733
 +61 2.52     stay 0.3203   0.6797   1.5199  0.1668 0.01693
 +97 5.59     stay 0.6438   0.3562   0.9554  0.2021 0.01693
 +70 5.48     stay 0.6327   0.3673   0.9731  0.1985 0.01648
 +74 2.56     stay 0.3242   0.6758   1.5115  0.1635 0.01615
 +75 2.57     stay 0.3251   0.6749   1.5095  0.1626 0.01596
 +67 2.65     stay 0.3329   0.6671   1.4929  0.1563 0.01454
 +80 5.04     stay 0.5869   0.4131   1.0457  0.1813 0.01431
 +77 4.46     quit 0.5243  -0.5243  -1.2296 -0.1656 0.01336
 +39 4.43     quit 0.5210  -0.5210  -1.2235 -0.1625 0.01282
 +
 +
 +   PREDICTION 
 +
 +Probability threshold for classification stay: 0.5
 +
 + 0: quit
 + 1: stay
 +
 +Data, Fitted Values, Standard Errors
 +   [sorted by fitted value]
 +   [pred_all=TRUE to see all intervals displayed]
 +--------------------------------------------------------------------
 +     JS Turnover label fitted std.err
 +24 0.23     quit     0 0.1475 0.08116
 +88 0.67     quit     0 0.1734 0.08096
 +48 1.05     quit     0 0.1985 0.07904
 +66 1.19     quit     0 0.2084 0.07790
 +
 +... for the rows of data where fitted is close to 0.5 ...
 +
 +     JS Turnover label fitted std.err
 +14 4.14     stay     0 0.4893 0.06579
 +27 4.15     stay     0 0.4903 0.06609
 +64 4.26     quit     1 0.5024 0.06946
 +83 4.41     stay     1 0.5188 0.07431
 +39 4.43     quit     1 0.5210 0.07497
 +
 +... for the last 4 rows of sorted data ...
 +
 +     JS Turnover label fitted std.err
 +70 5.48     stay     1 0.6327  0.1090
 +73 5.48     quit     1 0.6327  0.1090
 +97 5.59     stay     1 0.6438  0.1120
 +69 6.00     quit     1 0.6838  0.1215
 +--------------------------------------------------------------------
 +
 +
 +----------------------------
 +Specified confusion matrices
 +----------------------------
 +
 +Probability threshold for predicting stay: 0.5
 +Corresponding cutoff threshold for JS: 4.238
 +
 +                 Baseline         Predicted 
 +---------------------------------------------------
 +                Total  %Tot        0      1  %Correct 
 +---------------------------------------------------
 +           1       39  39.8       31      8     20.5 
 +Turnover   0       59  60.2       49     10     83.1 
 +---------------------------------------------------
 +         Total     98                           58.2 
 +
 +Accuracy: 58.16 
 +Sensitivity: 20.51 
 +Precision: 44.44 
 +
 +> 
 +</code>
 +====== e.g. 2 ======
 <code>d <- subset(iris, Species == "virginica" | Species == "versicolor") <code>d <- subset(iris, Species == "virginica" | Species == "versicolor")
-d+head(d)
 d$Species <- factor(d$Species) d$Species <- factor(d$Species)
 str(d) str(d)
Line 209: Line 328:
 {{:d3.csv}} {{:d3.csv}}
  
-<code>> d3 <- read.csv(file="d3.csv")+<code>> d3 <- read.csv("http://commres.net/wiki/_media/d3.csv")
 > round(predict(m, newdata=d3[c(1:5),], type="response"),2) > round(predict(m, newdata=d3[c(1:5),], type="response"),2)
    1    2    3    4    5     1    2    3    4    5 
r/logistic_regression_analysis.1481679825.txt.gz · Last modified: 2016/12/14 10:13 by hkimscil
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