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I am in interested in how do effect coding in R. I know that someone else has asked this question (i.e How to do regression with effect coding instead of dummy coding in R?How to do regression with effect coding instead of dummy coding in R?). Here is the lm() model on its own:

I am in interested in how do effect coding in R. I know that someone else has asked this question (i.e How to do regression with effect coding instead of dummy coding in R?). Here is the lm() model on its own:

I am in interested in how do effect coding in R. I know that someone else has asked this question (i.e How to do regression with effect coding instead of dummy coding in R?). Here is the lm() model on its own:

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Effects Coding in R

I am in interested in how do effect coding in R. I know that someone else has asked this question (i.e How to do regression with effect coding instead of dummy coding in R?). Here is the lm() model on its own:

  Fixed.Full<-lm(sqrtClay_Tot ~ Physio.Code+ROCKTYPE1, data=PedonsTx_Reread,
             contrasts =  list(Factor.0.Physio = contr.sum))

> summary(Fixed.Full)

Call:
lm(formula = sqrtClay_Tot ~ Physio.Code + ROCKTYPE1, data = PedonsTx_Reread)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5719 -1.1543  0.0102  1.1366  6.0146 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          5.18961    0.54194   9.576  < 2e-16 ***
Physio.Code2                        -0.90588    0.15752  -5.751 1.11e-08 ***
Physio.Code3                         0.70532    0.21141   3.336 0.000874 ***
Physio.Code4                         0.56462    0.31295   1.804 0.071444 .  
Physio.Code7                        -0.04656    0.26410  -0.176 0.860075    
Physio.Code8                        -0.41242    0.24330  -1.695 0.090294 .  
Physio.Code9                        -0.81833    0.32048  -2.553 0.010782 *  
Physio.Code11                       -0.04619    0.16420  -0.281 0.778515    
ROCKTYPE1chert                       1.43390    1.80191   0.796 0.426319    
ROCKTYPE1clay or mud                 0.35031    0.53861   0.650 0.515562    
ROCKTYPE1evaporite                  -0.95719    0.65049  -1.471 0.141408    
ROCKTYPE1fine-grained mixed clastic -1.02193    0.59167  -1.727 0.084377 .  
ROCKTYPE1gravel                     -0.16708    0.56139  -0.298 0.766041    
ROCKTYPE1limestone                   0.36667    0.55323   0.663 0.507585    
ROCKTYPE1mudstone                    0.18934    0.58553   0.323 0.746476    
ROCKTYPE1rhyolite                   -0.25743    0.83684  -0.308 0.758426    
ROCKTYPE1sand                       -0.92281    0.53193  -1.735 0.083014 .  
ROCKTYPE1sandstone                  -1.57669    0.57599  -2.737 0.006281 ** 
ROCKTYPE1serpentinite               -0.18647    1.84384  -0.101 0.919464    
ROCKTYPE1shale                       0.34408    0.62577   0.550 0.582518    
ROCKTYPE1silt                       -0.82415    0.65554  -1.257 0.208912    
ROCKTYPE1siltstone                  -1.21094    0.79293  -1.527 0.126970    
ROCKTYPE1terrace                    -0.64082    0.60119  -1.066 0.286663    
ROCKTYPE1tuff                        1.10034    1.31108   0.839 0.401482    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.748 on 1261 degrees of freedom
Multiple R-squared:  0.2072,    Adjusted R-squared:  0.1928 
F-statistic: 14.33 on 23 and 1261 DF,  p-value: < 2.2e-16

Now here is the model using effecting coding within lm():

> Fixed.Full<-lm(sqrtClay_Tot ~ Physio.Code+ROCKTYPE1, data=PedonsTx_Reread,
+ contrasts =  list(Physio.Code = contr.sum))
> summary(Fixed.Full)

Call:
lm(formula = sqrtClay_Tot ~ Physio.Code + ROCKTYPE1, data = PedonsTx_Reread, 
    contrasts = list(Physio.Code = contr.sum))

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5719 -1.1543  0.0102  1.1366  6.0146 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          5.06968    0.50933   9.954  < 2e-16 ***
Physio.Code1                         0.11993    0.13291   0.902  0.36703    
Physio.Code2                        -0.78595    0.11689  -6.724 2.68e-11 ***
Physio.Code3                         0.82525    0.15844   5.208 2.22e-07 ***
Physio.Code4                         0.68455    0.24897   2.750  0.00605 ** 
Physio.Code5                         0.07337    0.21084   0.348  0.72792    
Physio.Code6                        -0.29249    0.18905  -1.547  0.12208    
Physio.Code7                        -0.69840    0.26157  -2.670  0.00768 ** 
ROCKTYPE1chert                       1.43390    1.80191   0.796  0.42632    
ROCKTYPE1clay or mud                 0.35031    0.53861   0.650  0.51556    
ROCKTYPE1evaporite                  -0.95719    0.65049  -1.471  0.14141    
ROCKTYPE1fine-grained mixed clastic -1.02193    0.59167  -1.727  0.08438 .  
ROCKTYPE1gravel                     -0.16708    0.56139  -0.298  0.76604    
ROCKTYPE1limestone                   0.36667    0.55323   0.663  0.50759    
ROCKTYPE1mudstone                    0.18934    0.58553   0.323  0.74648    
ROCKTYPE1rhyolite                   -0.25743    0.83684  -0.308  0.75843    
ROCKTYPE1sand                       -0.92281    0.53193  -1.735  0.08301 .  
ROCKTYPE1sandstone                  -1.57669    0.57599  -2.737  0.00628 ** 
ROCKTYPE1serpentinite               -0.18647    1.84384  -0.101  0.91946    
ROCKTYPE1shale                       0.34408    0.62577   0.550  0.58252    
ROCKTYPE1silt                       -0.82415    0.65554  -1.257  0.20891    
ROCKTYPE1siltstone                  -1.21094    0.79293  -1.527  0.12697    
ROCKTYPE1terrace                    -0.64082    0.60119  -1.066  0.28666    
ROCKTYPE1tuff                        1.10034    1.31108   0.839  0.40148    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.748 on 1261 degrees of freedom
Multiple R-squared:  0.2072,    Adjusted R-squared:  0.1928 
F-statistic: 14.33 on 23 and 1261 DF,  p-value: < 2.2e-16

The code above is functional within the lm() function as seen above,and what I am trying to do in the code above is to predict clay content (square-root transformed) from the categorical variables 'Physio.Code' and 'ROCKTYPE1'. What I would like to know, however, is if there is any way to do effect coding using the gls() or glm() functions in R, where method="REML" or REML estimation can be used. I am interested in comparing the levels within 'Physio.Code' specifically to to the grand sample mean (i.e. sum contrasts).

The other question I had was how to interpret the numbers after the categorical variable and whether they correspond to the same levels. With respect to 'Physio.Code', in the two models above '1' was used as the baseline - the levels were originally '1,2,3,4,7,8,9,11" but when '1' was used as the baseline group, it switched to "2,3,4,7,8,9" - I wasn't sure if levels are the same (i.e. Physio.Code2 in model is the same as Physio.Code1 in the 2nd model). Thank you.