r - Linear Regression Coefficient Information as Data Frame or Matrix -

i trying create script optimize linear regression analysis, , operate on model output, pr(>|t|) value. unfortunately, not know how model output matrix or data table.

here example: in code below, create 7 columns of data, , fit seventh using other six. when summary of model, clear 3 of parameters more significant than other three. if had access coefficient output numerically, perhaps create script drop least significant parameter , re-run analysis... is, doing manually.

what best way this?

thank in advance help.

q = matrix(  c(2,14,-4,1,10,9,41,8,13,2,0,20,3,27,1,10,-1,0, 10,-6,23,6,13,-8,1,15,-7,55,7,14,10,0,20,-3,6,4,20, -1,5,19,-2,48,10,19,8,8,10,-2,24,8,13,9,8,14,5,7,7, 12,1,0,16,7,27,7,10,-1,1,15,7,31,2,20,-5,10,12,3,57, 0,19,-8,8,11,-4,63,5,11,7,8,10,-7,6,9,10,-7,2,19,8, 51,2,18,3,3,14,4,30), nrow=15, ncol=7, byrow = true) # colnames(q) <- c("a","b","c","d","e","f","z") # q <- as.data.frame(q) # qmodel <- lm(z~.,data=q) # summary(qmodel) # 

output:

call: lm(formula = z ~ ., data = q)  residuals:      min       1q   median       3q      max  -1.25098 -0.52655 -0.02931  0.62350  1.26649   coefficients:             estimate std. error t value pr(>|t|)     (intercept) -2.09303    1.51627  -1.380    0.205                0.91161    0.11719   7.779 5.34e-05 *** b            1.99503    0.09539  20.914 2.87e-08 *** c           -2.98252    0.04789 -62.283 4.91e-12 *** d            0.13458    0.10377   1.297    0.231     e            0.15191    0.09397   1.617    0.145     f            0.01417    0.04716   0.300    0.772     --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  residual standard error: 0.9439 on 8 degrees of freedom multiple r-squared:  0.9986,    adjusted r-squared:  0.9975  f-statistic: 928.9 on 6 , 8 df,  p-value: 6.317e-11 

now here i'd like see:

 > coeffs              estimate std. error t value pr(>|t|)  (intercept) -2.09303    1.51627  -1.380 2.05e-01             0.91161    0.11719   7.779 5.34e-05  b            1.99503    0.09539  20.914 2.87e-08  c           -2.98252    0.04789 -62.283 4.91e-12  d            0.13458    0.10377   1.297 2.31e-01  e            0.15191    0.09397   1.617 1.45e-01  f            0.01417    0.04716   0.300 7.72e-01 

as is, got in manner... not automated @ all...

coeffs = matrix( c(-2.09303,1.51627,-1.38,0.205,0.91161,0.11719, 7.779,0.0000534,1.99503,0.09539,20.914,0.0000000287, -2.98252,0.04789,-62.283,0.00000000000491,0.13458, 0.10377,1.297,0.231,0.15191,0.09397,1.617,0.145, 0.01417,0.04716,0.3,0.772), nrow=7, ncol=4, byrow = true) # rownames(coeffs) <- c("(intercept)","a","b","c","d","e","f") colnames(coeffs) <- c("estimate","std. error","t value","pr(>|t|)") # coeffs <- as.data.frame(coeffs) # coeffs 

what want coefficients component of summary object.

m <- lm(z~.,data=q)  summary(m)$coefficients 

some further comments:

• use step stepwise variable selection rather coding yourself;
• stepwise variable selection has bad statistical properties; consider glmnet (in package of same name) regularized model building instead.