I am well aware how to read the model summary in R for a regression model when a factor is included. The "first" level, in terms of ABC, is regarded as the base level to which all further levels of that factor are compared to. In an ANOVA-style model the baseline value is found in the intercept (equals the mean response value for base-level class).

However, if one or several factors are mixed with continuous predictors, then how can I see what the base-level values are at all? to what would I compare? 

In the model output below, there are two factors:

1. LandUse (4 Levels)
2. Type\_LU (4 Levels)

I see now for example that `LandUseLow` is 0.35 units higher than the base-line `LandUseHigh`. Would one now simply look at the mean response for the class `LandUseHigh` and compare? Is it that simple? 

```
My_model: 
                  Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept)      4.6086772  1.7754606   1.7773711   2.593  0.00951 **
DiTempRange     -0.1409464  0.0764872   0.0765969   1.840  0.06575 .
LandUseLow       0.3520743  0.5989777   0.5997903   0.587  0.55721
LandUseMedium    0.2741413  0.3668149   0.3675811   0.746  0.45579
LandUseNone     -1.0128945  0.5735342   0.5744652   1.763  0.07787 .
MAP              0.0048810  0.0009128   0.0009142   5.339    1e-07 ***
Rivier           0.3502782  0.2252743   0.2257546   1.552  0.12076
TempRange        0.0823410  0.0606546   0.0607942   1.354  0.17560
Tmean           -0.1762862  0.0994486   0.0996353   1.769  0.07684 .
TYPE_LUconserva -0.9312487  0.4770681   0.4781244   1.948  0.05145 .
TYPE_LUprivate  -0.4839229  0.3289011   0.3296201   1.468  0.14207
TYPE_LUstate     0.0004062  0.4079678   0.4089744   0.001  0.99921
logVRM           0.1370973  0.1140166   0.1142342   1.200  0.23008
logTWI          -0.0735540  0.4195267   0.4202589   0.175  0.86106
logDAH           1.7132823  3.5937000   3.6028996   0.476  0.63441

```