Logistic regression analysis after changing variables to factors [closed]

Question

I have done a logistic regression with my binary outcome. Majority of my variables in my model were changed to factors as they are categorical. I put my categories into levels ie. Very Low = 1, Medium=2, High=3. I do not understand why my output is numbered for example: skills2, skills3 & skills4? I just want to know if skills is significant or not, as for all the other variables. Please see my output below:

Call:
glm(formula = FOODLIT$T1D2 ~ FOODLIT$EDUCATION + FOODLIT$SKILLS + 
    FOODLIT$CONFIDENCE + FOODLIT$`F&V` + FOODLIT$PROTEIN + FOODLIT$CHOS + 
    FOODLIT$SKILLSB + FOODLIT$`CONF. W REC.` + FOODLIT$`MET W/PDT` + 
    FOODLIT$KNOWLEDGE + FOODLIT$AGE + FOODLIT$`AGE DX` + FOODLIT$`WHO PREP MEALS`, 
    family = "binomial")

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.83314  -0.34830  -0.17582  -0.07559   2.88906  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -3.477e+00  2.910e+00  -1.195 0.232064    
FOODLIT$EDUCATION2        -4.275e-01  7.822e-01  -0.546 0.584726    
FOODLIT$EDUCATION3        -1.024e+00  8.212e-01  -1.247 0.212366    
FOODLIT$SKILLS2            3.928e-01  1.084e+00   0.362 0.717086    
FOODLIT$SKILLS3            4.590e-01  9.267e-01   0.495 0.620378    
FOODLIT$SKILLS4            1.325e-01  1.571e+00   0.084 0.932796    
FOODLIT$CONFIDENCE2       -5.894e-01  9.072e-01  -0.650 0.515892    
FOODLIT$CONFIDENCE3       -1.470e+00  1.126e+00  -1.305 0.191765    
FOODLIT$`F&V`2             2.196e+00  7.703e-01   2.851 0.004359 ** 
FOODLIT$PROTEIN2           8.357e-02  6.423e-01   0.130 0.896472    
FOODLIT$CHOS2              1.956e+00  7.247e-01   2.699 0.006957 ** 
FOODLIT$SKILLSB2          -3.435e-01  1.059e+00  -0.324 0.745600    
FOODLIT$SKILLSB3           9.864e-01  1.083e+00   0.911 0.362428    
FOODLIT$SKILLSB4           1.373e+00  1.181e+00   1.162 0.245046    
FOODLIT$`CONF. W REC.`2   -2.835e+00  1.040e+00  -2.727 0.006385 ** 
FOODLIT$`CONF. W REC.`3   -3.826e+00  1.106e+00  -3.457 0.000545 ***
FOODLIT$`MET W/PDT`        4.691e-01  3.484e-01   1.346 0.178189    
FOODLIT$KNOWLEDGE          6.796e-03  1.036e-02   0.656 0.511763    
FOODLIT$AGE                5.011e-02  9.963e-02   0.503 0.614969    
FOODLIT$`AGE DX`           2.388e-02  4.623e-02   0.516 0.605542    
FOODLIT$`WHO PREP MEALS`2  1.899e-02  8.339e-01   0.023 0.981829    
FOODLIT$`WHO PREP MEALS`3  2.159e+00  1.005e+00   2.148 0.031711 *  
FOODLIT$`WHO PREP MEALS`5 -1.097e+01  2.400e+03  -0.005 0.996354    
FOODLIT$`WHO PREP MEALS`6 -1.446e+01  1.691e+03  -0.009 0.993178    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 167.986  on 220  degrees of freedom
Residual deviance:  96.167  on 197  degrees of freedom
  (15 observations deleted due to missingness)
AIC: 144.17

Number of Fisher Scoring iterations: 15

Thanks for your help! It is greatly appreciated.

Answer 1

First, I want to save you time and energy when specifying your equation. For example, inside of the glm() function, I strongly encourage you to use the data = ... argument. It will eliminate the redundancies in your code. See the example R code below:

glm(formula = T1D2 ~ EDUCATION + SKILLS + ..., data = FOODLIT, family = "binomial")

Note, specifying the data = ... argument obviates the requirement to explicitly reference your data frame each time you want to extract a column (variable). The $ is a basic extraction operator in R, and the data = ... argument pulls the desired columns you want without further effort on your part. Now for your concerns.

I do not understand why my output is numbered for example: skills2, skills3 & skills4?

R concatenates the level to the variable name to distinguish the categories. For instance, I assume there are four categories associated with the SKILLS variable. By using the factor() function to create an ordered factor, the variable now has four ordered levels (e.g., Levels: 1 2 3 4). R omits the first level, and calculates separate estimates for each category. The first level is the "baseline" (i.e., reference) category; it is absorbed into the overall intercept term. Put simply, each skill level is compared with the omitted level. That is the why.

Furthermore, your levels are ordered (numbered) categories. To ease the readability of the output, try converting the variable to a named factor. See the example code below:

FOODLIT$... <- factor(FOODLIT$SKILLS, labels = c("Low", "Medium", "...", "..."))

The order of the labels should follow the numeric ordering. Thus, the label "Low" might correspond to 1, Medium might correspond to 2, so on and so forth.

I just want to know if skills is significant or not, as for all the other variables.

I observe more than one variable designation for "skill" in your specification (i.e., SKILLS and SKILLSB). Each appears to have four levels. Since you specified these variables as factor variables, then each level (category) is a separate estimate in your output. Each category is compared with the omitted category. To demonstrate this explicitly, the discretized version of SKILLS is making the following comparisons:

• SKILLS2 versus SKILLS1
• SKILLS3 versus SKILLS1
• SKILLS4 versus SKILLS1

In sum, the numbers help disambiguate the different factor levels.

I hope this clears things up!