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====== e.g. 1 ======


<code>
> Logit(Turnover ~ JS, data=td)

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")
head(d)
d$Species <- factor(d$Species)
str(d)

m <- glm(Species ~ ., data=d, family="binomial")

round(fitted(m)[c(1:5, 51:55)],3)
round(fitted(m)[c(1:5, 51:55)],2)
f <- fitted(m)
as.numeric(d$Species)
ifelse(f > .5, 1, 0) == as.numeric(d$Species) - 1 

is_correct <- ifelse(f > .5, 1, 0) == as.numeric(d$Species) - 1 
sum(is_correct)
sum(is_correct) / NROW(is_correct)

predict(m, newdata=d[c(1,10,55),], type="response")

d3 <- read.csv(file="d3.csv")
round(predict(m, newdata=d3[c(1:5),], type="response"),2)

is_correct <- ifelse(f > .5, 1, 0) == as.numeric(d$Species) - 1 
sum(is_correct)
sum(is_correct) / NROW(is_correct)
</code>

<code>> d <- subset(iris, Species == "virginica" | Species == "versicolor")
> d
    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica
</code>

<code>> d$Species <- factor(d$Species)
> str(d)
'data.frame':	100 obs. of  5 variables:
 $ Sepal.Length: num  7 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 ...
 $ Sepal.Width : num  3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 ...
 $ Petal.Length: num  4.7 4.5 4.9 4 4.6 4.5 4.7 3.3 4.6 3.9 ...
 $ Petal.Width : num  1.4 1.5 1.5 1.3 1.5 1.3 1.6 1 1.3 1.4 ...
 $ Species     : Factor w/ 2 levels "versicolor","virginica": 1 1 1 1 1 1 1 1 1 1 ...
</code>

<code>> m <- glm(Species ~ ., data=d, family="binomial")
> m
Call:  glm(formula = Species ~ ., family = "binomial", data = d)

Coefficients:
 (Intercept)  Sepal.Length   Sepal.Width  Petal.Length  
     -42.638        -2.465        -6.681         9.429  
 Petal.Width  
      18.286  

Degrees of Freedom: 99 Total (i.e. Null);  95 Residual
Null Deviance:	    138.6 
Residual Deviance: 11.9 	AIC: 21.9
</code>

<code>> round(fitted(m)[c(1:5, 51:55)],3)
   51    52    53    54    55   101   102   103   104   105 
0.000 0.000 0.001 0.000 0.001 1.000 1.000 1.000 1.000 1.000 

> round(fitted(m)[c(1:5, 51:55)],2)
 51  52  53  54  55 101 102 103 104 105 
  0   0   0   0   0   1   1   1   1   1 
</code>

<code>> f <- fitted(m)
> as.numeric(d$Species)
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [32] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
 [63] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [94] 2 2 2 2 2 2 2
> ifelse(f > .5, 1, 0) == as.numeric(d$Species) - 1 
   51    52    53    54    55    56    57    58    59    60    61 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
   62    63    64    65    66    67    68    69    70    71    72 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
   73    74    75    76    77    78    79    80    81    82    83 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
   84    85    86    87    88    89    90    91    92    93    94 
FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
   95    96    97    98    99   100   101   102   103   104   105 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
  106   107   108   109   110   111   112   113   114   115   116 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
  117   118   119   120   121   122   123   124   125   126   127 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
  128   129   130   131   132   133   134   135   136   137   138 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE 
  139   140   141   142   143   144   145   146   147   148   149 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
  150 
 TRUE 
</code>

<code>> predict(m, newdata=d[c(1,10,55),], type="response")
          51           60          105 
1.171672e-05 1.481064e-05 9.999999e-01 </code>

{{:d3.csv}}

<code>> d3 <- read.csv("http://commres.net/wiki/_media/d3.csv")
> round(predict(m, newdata=d3[c(1:5),], type="response"),2)
   1    2    3    4    5 
1.00 1.00 0.92 0.00 0.00 
</code>